magnet:?xt=urn:btih:35B2554B1CE78F11BB2C8CB0A354BB034C77271F
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/5. Demo - Azure Databricks with Azure Data Lake Storage Gen2.mp4 63.4 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/4. Creating Pipelines.mp4 51.2 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/exercise.7z 45.9 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/6. Demo - Working with KQL - Timeseries.mp4 44.0 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/3. Import and Prepare Data for Modeling/2. Data Preprocessing with Microsoft AzureML.mp4 39.4 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/5. Demo - Working with KQL - Basic.mp4 39.0 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/5. Creating and Deploying.mp4 38.8 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/4. Extracting and Matching Features with SIFT.mp4 37.1 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/6. Demo - Data Ingestion Using EventHubs and .Net Custom Code.mp4 36.5 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/6. HD Insights Demo.mp4 34.4 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/6. Demo - Create Model and Perform Predictive Analytics Part3.mp4 34.1 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/3. Automated Machine Learning Experiment Using Python SDK.mp4 33.4 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/3. Import and Prepare Data for Modeling/1. Creating and Registering Microsoft AzureML Datastore.mp4 31.3 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/4. Demo - Communicating Insights using MatPlotLib.mp4 29.9 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/4. Demo - Configuring and Working with Azure Databricks.mp4 29.1 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/6. Extracting and Matching Features with HOG.mp4 28.5 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/6. Demo - Performing Exploratory Data Analysis using Azure Databricks.mp4 27.0 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/5. Demo - Create Model and Perform Predictive Analytics Part 2.mp4 25.7 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/7. Demo - Working with Streaming Data Using Azure Databricks and Event Hubs.mp4 24.7 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/3. Demo - Communicating Insights using Power BI.mp4 24.5 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/5. Creating a CNN for Classification.mp4 24.4 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/4. Tools.mp4 24.2 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/5. Demo - Managing ADX Database Permissions.mp4 24.2 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/4. Training Script and Estimators in AzureML.mp4 23.4 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/2. Hyperparameter Tuning - Demo.mp4 22.2 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/3. Iris Demo.mp4 22.1 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/4. Azure Data Factory Demo.mp4 22.0 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/7. Demo - Data Ingestion Using EventGrids and Blob Storage.mp4 21.8 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/8. Demo - Data Sharing and Visualization Using Power BI.mp4 21.0 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/4. Demo - Create ADX Cluster Using PowerShell.mp4 20.8 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/4. Working with MNIST.mp4 20.4 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/3. How PCA Works.mp4 20.2 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/7. Demo - Data Obfuscation in KQL.mp4 19.6 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/4. Demo - Inspecting an Azure ML Pipeline.mp4 19.2 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/4. Demo - Creating an Automated ML Experiment.mp4 18.7 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/2. Understanding Azure Machine Learning service/2. Creating and Deleting Workspace.mp4 18.7 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/6. Demo.mp4 18.4 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/15. Demo - Word Embeddings with BERT on AMLS.mp4 18.3 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/4. Performing Feature Extraction on Unstructured Text.mp4 18.2 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/3. R Demo.mp4 18.0 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/7. Demo - Scaling the ADX Cluster.mp4 17.9 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/6. Demo - ADX Health and Performance Monitoring.mp4 17.5 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/2. Understanding Azure Machine Learning service/3. Setting up Environments.mp4 17.5 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/5. HOG Introduction.mp4 17.4 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/4. Automated Machine Learning Experiment Using Visual Interface.mp4 17.2 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/2. Metrics Logging in AzureML.mp4 17.1 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/1. Setting up Compute Target.mp4 16.6 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/4. Demo - Create Model and Perform Predictive Analytics Part 1.mp4 16.5 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/4. Python Demo.mp4 16.3 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/2. Performing Feature Extraction.mp4 15.8 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/2. Data Structures.mp4 15.4 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/8. Azure Availability Features.mp4 15.3 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/5. Demo - Human Face or Not Human Face.mp4 15.3 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/5. Demo - Touring the Azure Python Interpretability SDK.mp4 15.1 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/03. Demo - Exploratory Data Analysis.mp4 15.1 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/2. Data.mp4 14.6 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/3. SIFT Introduction.mp4 14.5 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/5. Azure Backup Services Demo.mp4 14.2 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/3. Import and Prepare Data for Modeling/3. Microsoft AzureML Datasets.mp4 14.1 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/3. Demo - Azure Security, Privacy, and Compliance.mp4 13.8 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/5. Dataset Exploration Demo.mp4 13.8 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/5. Launching a Notebook Instance.mp4 13.5 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/7. Demo.mp4 13.4 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/6. Compute Linear Correlation Demo.mp4 13.3 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/4. Fisher Linear Discriminant Analysis Demo.mp4 13.3 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/5. Demo - Scoring and Evaluating the Pipeline Model.mp4 13.1 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/2. Introduction to Computer Vision.mp4 13.1 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/4. Setting Up an Experiment in a Jupyter Notebook.mp4 12.9 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/3. Create ADX Cluster Using PowerShell.mp4 12.8 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/7. Demo - One-hot Encoding Categorical Variables.mp4 12.8 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/1. Hyperparameter Tuning - Theory.mp4 12.8 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/2. Introduction to Data Science.mp4 12.7 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/3. Convolutional Neural Network Overview.mp4 12.5 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/4. Demo - Outlier Detection in Python.mp4 12.5 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/11. Demo - The Hashing Trick.mp4 12.0 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/4. Splitting Data for Model Tuning.mp4 12.0 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/6. Demo - Creating an Azure Machine Learning Studio Workspace.mp4 11.9 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/exercise.7z 11.8 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/5. Distributed Training in AzureML.mp4 11.7 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/6. Demo - Create ADX Cluster Using Command Line Interface.mp4 11.7 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/5. Demo - Cross-validation.mp4 11.5 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/2. Provisioning an Environment.mp4 11.3 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/7. Bad Data.mp4 11.2 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/5. Azure Data Catalog Demo.mp4 11.1 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/2. Demo - Create ADX Cluster Using Azure Portal.mp4 11.1 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/2. Understanding Azure Machine Learning service/1. Overview of Microsoft Azure Machine Learning service.mp4 11.0 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/2. Demo - Training and Testing on Same Data.mp4 10.9 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/2. Image Processing Techniques.mp4 10.8 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/12. Demo - Frequency Filtering.mp4 10.8 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/3. Creating a DSVM.mp4 10.6 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/10. Demo - Stopword Removal.mp4 10.5 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/2. What Is a Feature in Machine Learning.mp4 10.3 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/13. Demo - Locality-sensitive Hashing.mp4 10.1 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/8. Demo - Learning with Counts Categorical Variables.mp4 10.1 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/09. Demo - Word Embeddings Using Word2Vec.mp4 10.0 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/3. Understanding Apache Spark and Notebook.mp4 10.0 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/7. Encoding Features Demo.mp4 10.0 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/7. Azure Open Datasets Demo.mp4 10.0 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/08. Gaussian Distributions.mp4 9.9 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/07. Demo - Data Transformation.mp4 9.9 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/03. Demo - Configure AMLS.mp4 9.9 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/6. Demo.mp4 9.8 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/2. Neural Network Overview.mp4 9.8 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/5. Normalize Data Demo.mp4 9.8 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/5. Demo - Working with Tokens.mp4 9.7 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/8. Demo - Exploring the Dataset.mp4 9.7 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/6. Demo - Model Selection.mp4 9.5 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/4. Azure Data Catalog.mp4 9.5 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/7. Demo - Modifying the Metadata of Datasets.mp4 9.5 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/3. LDA.mp4 9.4 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/3. Clip Values Demo.mp4 9.4 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/4. Access Keys and SAS.mp4 9.4 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/3. Setting up Run Object.mp4 9.3 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/exercise.7z 9.3 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/03. Demo - Listwise Deletion.mp4 9.2 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/3. Azure SQL High Availability.mp4 9.2 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/3. Identifying Constraints.mp4 9.1 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/5. Large Data Sets.mp4 9.1 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/3. Approaches to Computer Vision.mp4 9.0 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/07. Demo - NLTK Tokenizers.mp4 8.9 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/6. Demo.mp4 8.9 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/6. Demo.mp4 8.9 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/6. SQL Data Sampling.mp4 8.8 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/6. Microsofts Team Data Science Process.mp4 8.8 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/3. 6 Characteristics of a Good Feature.mp4 8.8 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/4. Application Insights.mp4 8.7 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/5. Permutation Feature Importance Demo.mp4 8.7 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/03. One-hot and Count Vector Encoding.mp4 8.6 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/2. Extracting and Loading.mp4 8.6 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/2. Data Science Overview.mp4 8.6 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/3. Demo - Split Data into Training and Test Set.mp4 8.6 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/1. Introduction and Module Overview.mp4 8.3 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/07. Demo - TF-IDF Encoding.mp4 8.3 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/2. Authentication and Authorization on Azure.mp4 8.2 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/06. Demo - Sentence and Word Tokenization.mp4 8.2 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/10. Demo - Discretizing Data.mp4 8.1 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/4. Azure Services for Computer Vision.mp4 8.1 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/6. Principal Component Analysis Demo.mp4 8.1 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/3. Azure Data Factory.mp4 8.0 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/5. Demo - Imputation in Python.mp4 8.0 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/4. Group Data into Bins Demo.mp4 8.0 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/3. Discovering Data.mp4 7.9 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/04. Demo - Data Cleaning (Erroneous Data).mp4 7.8 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/5. How Feature Set Complexity Impacts Model Interpretability.mp4 7.8 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/5. Bivariate Techniques.mp4 7.8 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/2. Running a Test Experiment.mp4 7.7 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/2. How Can You Process Categorical or Text Feature Sets.mp4 7.7 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/05. Defining Business Metrics.mp4 7.6 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/2. Understanding the Azure Databricks Ecosystem.mp4 7.6 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/3. Creating Azure Machine Learning.mp4 7.6 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/7. Demo - Creating an Azure Machine Learning Service Workspace.mp4 7.6 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/2. Model Training Process.mp4 7.5 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/exercise.7z 7.5 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/20. Demo - N-grams.mp4 7.4 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/05. Demo - Data Cleaning (Outliers).mp4 7.3 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/04. Demo - Bag-of-words.mp4 7.3 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/2. Aggregation.mp4 7.3 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/3. Fliter Based Feature Selection Demo.mp4 7.2 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/6. Azure Data Explorer Capabilities.mp4 7.2 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/exercise.7z 7.2 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/3. Data Exploration and Visualization.mp4 7.1 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/10. Quantifying the Risks for the Data Science Project.mp4 7.1 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/3. Azure Notebooks Demo.mp4 7.1 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/4. Data Exploration in Azure (ADX).mp4 7.0 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/exercise.7z 7.0 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/8. Handling Bad Data.mp4 6.9 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/1. Ethical and Legal Compliance.mp4 6.9 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/4. Saving Work.mp4 6.9 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/4. How Statistical Tests Work.mp4 6.7 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/3. Scoring and Evaluating an Azure ML Pipeline.mp4 6.6 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/3. Exploring Your Data and Identifying the Distribution of Your Da.mp4 6.6 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/3. How k-means Works.mp4 6.6 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/5. HD Insights.mp4 6.6 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/03. Demo - Common Scaling Approaches.mp4 6.5 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/05. Demo - Using Indicator Variables.mp4 6.5 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/3. Univariate Techniques.mp4 6.4 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/09. Calculating the Mean, Median, and Mode.mp4 6.4 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/02. Encoding Text as Numbers.mp4 6.4 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/08. Demo - Replace with MICE.mp4 6.3 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/14. BERT.mp4 6.3 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/10. Feature Hashing.mp4 6.3 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/4. Data Labeling.mp4 6.2 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/2. Data Science Overview.mp4 6.1 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/3. Understanding the Modeling Process.mp4 5.9 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/4. Autoencoders.mp4 5.9 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/1. Data Availability Concepts.mp4 5.8 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/4. KPCA.mp4 5.7 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/2. KQL Schema Mapping for Data Ingestion.mp4 5.7 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/5. Cosmos DB Availability.mp4 5.7 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/3. Demo - SMOTE.mp4 5.7 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/1. The Shared Responsibility Model.mp4 5.7 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/3. Synthetic Training Data.mp4 5.6 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/05. Tokenization and Cleaning.mp4 5.6 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/5. Create ADX Cluster Using Command Line Interface.mp4 5.6 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/4. Dictionary Learning.mp4 5.5 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/08. Demo - Reducing Data (Record Sampling).mp4 5.4 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/14. Demo - Stemming.mp4 5.3 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/1. Performing Feature Normalization.mp4 5.2 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/2. PCA.mp4 5.2 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/04. Identifying the Hard-skills.mp4 5.1 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/2. Tracking Models.mp4 5.1 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/4. Define Target for ML Problems.mp4 5.0 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/1. Introduction and Module Overview.mp4 5.0 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/1. Introduction and Module Overview.mp4 5.0 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/4. Model Training.mp4 4.9 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/6. Multivariate Techniques.mp4 4.9 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/2. What Is Machine Learning.mp4 4.9 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/12. Locality-sensitive Hashing.mp4 4.9 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/1. Module Overview.mp4 4.9 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/07. Managing Technical Metrics.mp4 4.9 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/1. Course Overview/1. Course Overview.mp4 4.8 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/5. Model Evaluation.mp4 4.7 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/06. Demo - Data Cleaning (Duplicate Rows).mp4 4.7 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/2. Understanding the Kusto Query Language (KQL).mp4 4.7 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/5. Environment Management.mp4 4.7 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/3. Sources of Model Error.mp4 4.7 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/08. Word Embeddings.mp4 4.7 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/02. Prerequisites.mp4 4.6 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/05. Demo - Bag-of-n-grams.mp4 4.6 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/5. Demo - Exploring Datasets for Different Problems.mp4 4.6 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/2. Preliminary Terminology.mp4 4.6 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/exercise.7z 4.5 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/2. Availability on Blob Storage.mp4 4.5 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/3. Model Training Techniques.mp4 4.5 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/5. PCA Limitations.mp4 4.5 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/7. Demo - Label Encoding and XGBoost.mp4 4.5 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/18. Demo - Lemmatization.mp4 4.5 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/4. Version Management.mp4 4.5 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/3. Data Measurement Scales.mp4 4.4 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/3. Unintended Bias and Interpretability.mp4 4.4 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/2. Machine Learning Process Distilled.mp4 4.3 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/2. Why Feature Engineering.mp4 4.3 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/5. Manifold Learning.mp4 4.3 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/09. Demo - Z-score.mp4 4.3 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/01. What Is Data Science.mp4 4.3 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/exercise.7z 4.3 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/5. Demo - Interpreting the Experiment Results.mp4 4.2 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/2. Azure Machine Learning.mp4 4.2 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/2. k-means Model Stacking.mp4 4.2 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/06. Business Metrics Classifications.mp4 4.2 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/7. Problem with High-dimensional Datasets.mp4 4.2 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/07. Demo - Correcting Heteroscedasticity.mp4 4.2 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/1. Introduction.mp4 4.1 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/2. Detecting and Preventing Overfitting.mp4 4.1 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/4. Measuring and Detecting Problems Due to Feature Set Complexity.mp4 4.1 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/07. Selecting the Right Stakeholders.mp4 4.1 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/3. Continuous Deployment.mp4 4.1 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/7. High Quality Datasets.mp4 4.0 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/1. Module Overview.mp4 4.0 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/2. Microsofts Guiding Principles for Responsible AI.mp4 3.9 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/exercise.7z 3.9 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/1. Course Overview/1. Course Overview.mp4 3.9 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/2. Managing ADX Database Permissions.mp4 3.9 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/1. Course Overview/1. Course Overview.mp4 3.9 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/16. Demo - Parts-of-speech.mp4 3.9 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/1. Course Overview/1. Course Overview.mp4 3.9 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/1. Course Overview/1. Course Overview.mp4 3.8 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/6. Correlation vs. Causation.mp4 3.8 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/05. Measures of Variability.mp4 3.8 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/exercise.7z 3.8 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/04. Problems in Deleting Rows.mp4 3.8 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/01. Module Overview.mp4 3.7 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/8. Specialized Roles in Data Science.mp4 3.6 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/10. Summary.mp4 3.6 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/1. Exploring Your Dataset for Feature Selection and Extraction.mp4 3.6 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/11. Entropy-based Discretization.mp4 3.6 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/4. Determining the Feature Structure Appropriate for the Algorithm.mp4 3.6 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/3. ADX Health and Performance Monitoring.mp4 3.6 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/5. Personally Identifiable Information (PII).mp4 3.6 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/5. t-SNE.mp4 3.6 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/5. Multicollinearity Problem in Regression Models.mp4 3.5 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/21. Module Summary.mp4 3.5 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/08. Demo - Token Cleaning.mp4 3.5 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/3. Introduction to Azure Machine Learning.mp4 3.5 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/1. Course Overview/1. Course Overview.mp4 3.5 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/4. Deploying Models.mp4 3.5 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/7. Data Science Services and Tools in Azure.mp4 3.5 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/4. Data Scale Issues in Distance-based Models.mp4 3.5 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/5. Testing for Validity.mp4 3.5 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/2. A New Problem to Be Solved.mp4 3.5 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/1. Course Overview/1. Course Overview.mp4 3.5 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/4. The Complete Media Insights Solution.mp4 3.5 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/2. Communicating Knowledge and Insights.mp4 3.5 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/6. Outliers in Regression Models.mp4 3.5 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/4. The Budget Barrier and Solution.mp4 3.4 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/05. Demo - Binning.mp4 3.4 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/3. Creating and Using Feature Extraction Algorithms.mp4 3.4 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/01. Module Overview.mp4 3.4 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/01. Module Overview.mp4 3.4 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/7. ADX Pricing.mp4 3.4 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/1. Course Overview/1. Course Overview.mp4 3.3 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/02. Common Scaling Approaches.mp4 3.3 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/05. Meet the Stereotypical Technical Players.mp4 3.3 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/4. Azure Machine Learning Experiment Workflow.mp4 3.3 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/5. EventGrids Overview.mp4 3.3 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/1. Course Overview/1. Course Overview.mp4 3.3 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/4. Scaling the ADX Cluster.mp4 3.3 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/6. Feature Engineering Categorical Variables.mp4 3.3 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/1. Course Overview/1. Course Overview.mp4 3.3 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/08. Data Science Project Risks.mp4 3.2 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/06. Modality and Skewness.mp4 3.2 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/07. Disadvantages of Single Imputation Methods.mp4 3.2 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/06. Identify Your Stakeholders.mp4 3.2 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/6. Demo - Label and One-hot Encoding.mp4 3.2 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/04. Preprocessing and NLP.mp4 3.2 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/5. Multiple Data Sets.mp4 3.1 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/3. Data Ingestion in Azure.mp4 3.1 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/1. Course Overview/1. Course Overview.mp4 3.1 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/3. Outlier Detection and Imputation.mp4 3.1 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/1. Course Overview/1. Course Overview.mp4 3.1 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/19. N-grams.mp4 3.1 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/1. Course Overview/1. Course Overview.mp4 3.1 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/3. Your New Data Broker.mp4 3.1 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/06. TF-IDF Encoding.mp4 3.1 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/6. Ethical and Legal Barriers to Data Use.mp4 3.0 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/3. Role of Feature Engineering in Model Complexity.mp4 3.0 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/1. Course Overview/1. Course Overview.mp4 3.0 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/3. Long Term Planning.mp4 3.0 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/5. Azure Data Explorer Features.mp4 2.9 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/4. Build Better Models with Feature Engineering.mp4 2.9 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/2. Understanding Feature Normalization.mp4 2.9 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/06. Replace with Mean, Median, and Mode.mp4 2.9 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/02. What Is the Project Motivation Factor.mp4 2.8 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/2. The Use Case - Media Insights Solution.mp4 2.8 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/6. Standardization and Normalization.mp4 2.8 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/3. Available Data Sources.mp4 2.8 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/6. The Format of the Data.mp4 2.8 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/2. Feature Set Complexity.mp4 2.8 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/09. Demo - Reducing Data (Attribute Sampling).mp4 2.7 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/10. Summary.mp4 2.7 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/12. Assessing Stakehoders Needs.mp4 2.7 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/1. Introduction and Module Overview.mp4 2.7 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/2. Imbalanced Dataset for Classification Problems.mp4 2.7 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/6. Final Takeaway/1. Final Takeaway.mp4 2.7 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/1. Course Overview/1. Course Overview.mp4 2.6 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/7. Summary.mp4 2.6 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/1. Course Overview/1. Course Overview.mp4 2.6 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/2. Understanding Feature Selection.mp4 2.6 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/5. Feature Engineering Numeric Variables.mp4 2.6 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/3. The Key Valet Pattern.mp4 2.6 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/2. The Problem with the Internal Data.mp4 2.6 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/10. Asking the Right Questions.mp4 2.6 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/6. Azure Open Datasets.mp4 2.5 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/01. Module Overview.mp4 2.5 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/1. Introduction and Module Overview.mp4 2.5 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/7. Demo - Normalize and Standardize in Python.mp4 2.5 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/1. Performing Feature Selection.mp4 2.4 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/16. Summary.mp4 2.4 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/exercise.7z 2.4 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/04. Measures of Central Tendency.mp4 2.4 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/2. Moving from Raw Data to Features.mp4 2.4 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/02. Is This Course for You.mp4 2.4 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/3. Azure Automated Machine Learning.mp4 2.4 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/1. Overview.mp4 2.3 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/09. How MICE Works.mp4 2.3 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/7. The Big Ask.mp4 2.3 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/14. Project Gap Analysis.mp4 2.3 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/5. Summary.mp4 2.3 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/13. Stemming.mp4 2.2 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/1. Module Overview.mp4 2.2 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/8. Summary.mp4 2.2 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/1. What Is Feature Extraction.mp4 2.2 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/4. Bring in the SME.mp4 2.2 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/04. Binning.mp4 2.2 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/09. Stopword Removal.mp4 2.2 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/2. Encryption in Azure.mp4 2.2 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/4. Problems with Categorical Data.mp4 2.2 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/17. Lemmatization.mp4 2.2 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/1. Overview.mp4 2.2 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/2. Reasons For Feature Elimination.mp4 2.1 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/13. Accessing the Data.mp4 2.1 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/02. Reasons Why Data Is Missing.mp4 2.1 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/1. Introduction.mp4 2.1 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/1. Course Overview/1. Course Overview.mp4 2.1 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/15. Parts-of-speech Tagging.mp4 2.1 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/7. Summary.mp4 2.1 MBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/11. Frequency Filtering.mp4 2.0 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/08. Z-score.mp4 2.0 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/1. Overview.mp4 2.0 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/exercise.7z 2.0 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/1. Module Overview.mp4 2.0 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/1. Intro.mp4 2.0 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/1. Introduction.mp4 1.9 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/09. Data Science Project Lifecycle.mp4 1.9 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/6. Summary.mp4 1.9 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/1. Module Overview.mp4 1.9 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/5. Exploratory Data Analysis Tools.mp4 1.9 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/exercise.7z 1.9 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/7. Leave-one-out Cross Validation.mp4 1.9 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/7. Summary.mp4 1.9 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/6. Summary.mp4 1.9 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/18. Establishing Agreement to Proceed the Project Further.mp4 1.9 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/12. Demo - Entropy-based Discretization.mp4 1.9 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/01. Introduction.mp4 1.9 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/2. Data Types in Statistics.mp4 1.8 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/1. Introduction.mp4 1.8 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/17. Address and Capture Any Concerns.mp4 1.8 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/6. Summary.mp4 1.8 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/6. Summary.mp4 1.8 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/1. Overview.mp4 1.8 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/03. Skills Recommended for This Course.mp4 1.8 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/6. Course Review.mp4 1.8 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/7. Takeaway.mp4 1.8 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/8. Summary.mp4 1.8 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/4. EventHubs Overview.mp4 1.8 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/1. Module Overview.mp4 1.8 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/6. Availability on Other Azure Services.mp4 1.7 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/07. Kurtosis.mp4 1.7 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/8. Summary.mp4 1.7 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/08. Get to Know Your Stakeholders.mp4 1.7 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/8. Summary.mp4 1.7 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/6. How Algorithms Learn Models.mp4 1.7 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/6. Summary.mp4 1.7 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/1. Overview.mp4 1.7 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/8. Summary.mp4 1.7 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/11. Capturing Stakeholders Needs.mp4 1.6 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/1. Intro.mp4 1.6 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/9. Summary.mp4 1.6 MBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/7. Summary.mp4 1.6 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/8. Summary.mp4 1.6 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/1. Intro.mp4 1.6 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/4. Available KQL Demo Platforms.mp4 1.6 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/7. Review.mp4 1.6 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/7. Summary.mp4 1.6 MBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/exercise.7z 1.6 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/06. Heteroscedasticity.mp4 1.6 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/09. Managing the Initial Stakeholder Engagement.mp4 1.6 MBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/7. Summary.mp4 1.5 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/8. Summary.mp4 1.5 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/7. Summary.mp4 1.5 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/01. Introduction.mp4 1.5 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/4. Azure SQL Datawarehouse Availability.mp4 1.5 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/8. Summary.mp4 1.5 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/9. Summary.mp4 1.5 MBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/6. Review.mp4 1.5 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/1. Overview.mp4 1.5 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/9. Summary.mp4 1.5 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/2. Performance Analytics.mp4 1.4 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/6. Takeaway.mp4 1.4 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/8. Summary.mp4 1.4 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/1. Module Overview.mp4 1.4 MBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/5. Review.mp4 1.4 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/1. Overview.mp4 1.3 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/3. Schema Mapping in KQL.mp4 1.3 MBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/8. Takeaway.mp4 1.3 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/1. Module Overview.mp4 1.3 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/15. Present the Proposal.mp4 1.3 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/03. Hard-skills and Soft-skills.mp4 1.3 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/1. Introduction.mp4 1.3 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/4. Module Summary.mp4 1.2 MBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/01. Access the Need of the Project to the Business.mp4 1.2 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/9. Summary.mp4 1.2 MBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/exercise.7z 1.2 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/1. Introduction.mp4 1.2 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/7. Summary.mp4 1.2 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/6. Module Summary.mp4 1.2 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/1. Module Overview.mp4 1.2 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/1. Overview.mp4 1.2 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/8. Summary.mp4 1.2 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/1. Introduction.mp4 1.2 MBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/5. One-hot Encoding.mp4 1.2 MBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/13. Summary.mp4 1.2 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/exercise.7z 1.1 MBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/9. Module Summary.mp4 1.1 MBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/1. Module Overview.mp4 1.1 MBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/1. Overview.mp4 1.1 MBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/1. Introduction.mp4 1.0 MBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/1. Intro.mp4 1.0 MBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/7. Summary.mp4 998.9 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/03. Low Risk Data Science Project Scope.mp4 986.2 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/8. Summary.mp4 951.0 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/16. Accessing the Teams Reaction.mp4 876.8 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/04. External vs. Internal Facing Project Scope.mp4 859.0 kBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/6. Takeaway.mp4 843.6 kBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/9. Review.mp4 812.4 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/10. Summary.mp4 803.6 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/02. High Risk Data Science Project Scope.mp4 787.0 kBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/1. Intro.mp4 785.4 kBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/1. Intro.mp4 717.0 kBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/1. Intro.mp4 685.4 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/02. Data Preprocessing Methods.mp4 661.4 kBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/6. Review.mp4 656.0 kBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/1. Overview.mp4 635.1 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/5. Prerequisites.mp4 580.6 kBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/5. Review.mp4 550.4 kB
scr 2022-08.png 505.2 kBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/exercise.7z 460.6 kB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/4. Tools.vtt 31.4 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/5. Demo - Azure Databricks with Azure Data Lake Storage Gen2.vtt 23.4 kBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/4. Creating Pipelines.vtt 22.5 kBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/2. Data Structures.vtt 19.3 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/6. Demo - Working with KQL - Timeseries.vtt 18.6 kBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/5. Creating and Deploying.vtt 18.6 kBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/4. Extracting and Matching Features with SIFT.vtt 18.5 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/5. Demo - Working with KQL - Basic.vtt 18.3 kBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/2. Data.vtt 17.3 kBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/2. Introduction to Data Science.vtt 16.1 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/4. Demo - Configuring and Working with Azure Databricks.vtt 14.9 kBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/3. Convolutional Neural Network Overview.vtt 14.9 kBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/3. SIFT Introduction.vtt 14.6 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/6. Demo - Create Model and Perform Predictive Analytics Part3.vtt 14.3 kBE5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/5. Creating a CNN for Classification.vtt 13.6 kBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/7. Bad Data.vtt 13.1 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/3. 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Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/2. What Is Machine Learning.vtt 4.3 kBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/3. Creating a DSVM.vtt 4.3 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/3. Model Training Techniques.vtt 4.3 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/3. How k-means Works.vtt 4.2 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/10. Summary.vtt 4.2 kBB3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/5. Multiple Data Sets.vtt 4.2 kBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/3. Creating Azure Machine Learning.vtt 4.2 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/03. Demo - Common Scaling Approaches.vtt 4.1 kBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/2. Availability on Blob Storage.vtt 4.1 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/06. Modality and Skewness.vtt 4.1 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/1. Module Overview.vtt 4.0 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/01. Module Overview.vtt 4.0 kBD2. 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Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/3. Azure Notebooks Demo.vtt 3.8 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/8. Demo - Exploring the Dataset.vtt 3.8 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/6. Outliers in Regression Models.vtt 3.8 kBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/6. Principal Component Analysis Demo.vtt 3.8 kBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/3. Fliter Based Feature Selection Demo.vtt 3.8 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/4. Dictionary Learning.vtt 3.8 kBF3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/4. Deploying Models.vtt 3.7 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/5. Model Evaluation.vtt 3.7 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/5. PCA Limitations.vtt 3.7 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/14. Demo - Stemming.vtt 3.7 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/7. Problem with High-dimensional Datasets.vtt 3.7 kBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/2. Detecting and Preventing Overfitting.vtt 3.7 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/02. Common Scaling Approaches.vtt 3.7 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/6. Demo.vtt 3.7 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/1. Introduction and Module Overview.vtt 3.7 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/6. Demo.vtt 3.6 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/playlist.m3u 3.6 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/2. Understanding the Kusto Query Language (KQL).vtt 3.6 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/1. Introduction.vtt 3.5 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/3. Introduction to Azure Machine Learning.vtt 3.5 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/09. Demo - Z-score.vtt 3.5 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/12. Locality-sensitive Hashing.vtt 3.5 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/8. Specialized Roles in Data Science.vtt 3.5 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/07. Managing Technical Metrics.vtt 3.5 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/5. Create ADX Cluster Using Command Line Interface.vtt 3.5 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/01. Module Overview.vtt 3.5 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/11. Entropy-based Discretization.vtt 3.5 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/3. Sources of Model Error.vtt 3.5 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/1. Introduction and Module Overview.vtt 3.5 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/08. Word Embeddings.vtt 3.4 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/10. Demo - Discretizing Data.vtt 3.4 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/1. Course Overview/1. Course Overview.vtt 3.4 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/7. Demo - Label Encoding and XGBoost.vtt 3.4 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/01. What Is Data Science.vtt 3.4 kBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/2. Microsofts Guiding Principles for Responsible AI.vtt 3.4 kBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/3. Demo - Azure Security, Privacy, and Compliance.vtt 3.3 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/21. Module Summary.vtt 3.3 kBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/5. Normalize Data Demo.vtt 3.3 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/6. Multivariate Techniques.vtt 3.3 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/06. Business Metrics Classifications.vtt 3.3 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/2. PCA.vtt 3.3 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/4. Model Training.vtt 3.3 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/2. Why Feature Engineering.vtt 3.2 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/01. Module Overview.vtt 3.2 kBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/5. Personally Identifiable Information (PII).vtt 3.2 kBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/6. Azure Open Datasets.vtt 3.2 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/07. Demo - Correcting Heteroscedasticity.vtt 3.2 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/7. Demo - Modifying the Metadata of Datasets.vtt 3.2 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/playlist.m3u 3.2 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/7. Demo - Creating an Azure Machine Learning Service Workspace.vtt 3.2 kBF1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/3. Setting up Run Object.vtt 3.2 kBE1. 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Processing and Simplifying Text to Simplify Feature Creation/04. Preprocessing and NLP.vtt 3.1 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/08. Data Science Project Risks.vtt 3.1 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/4. Azure Machine Learning Experiment Workflow.vtt 3.1 kBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/6. Correlation vs. Causation.vtt 3.1 kBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/4. The Complete Media Insights Solution.vtt 3.1 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/04. Problems in Deleting Rows.vtt 3.0 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/4. Data Scale Issues in Distance-based Models.vtt 3.0 kBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/2. A New Problem to Be Solved.vtt 3.0 kBB2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/7. Azure Open Datasets Demo.vtt 3.0 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/06. Replace with Mean, Median, and Mode.vtt 3.0 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/10. Summary.vtt 3.0 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/2. k-means Model Stacking.vtt 3.0 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/6. Demo - Label and One-hot Encoding.vtt 2.9 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/02. What Is the Project Motivation Factor.vtt 2.9 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/4. Build Better Models with Feature Engineering.vtt 2.9 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/6. Standardization and Normalization.vtt 2.9 kBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/2. Understanding Feature Normalization.vtt 2.9 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/5. Manifold Learning.vtt 2.9 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/3. Role of Feature Engineering in Model Complexity.vtt 2.9 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/18. Demo - Lemmatization.vtt 2.9 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/5. Multicollinearity Problem in Regression Models.vtt 2.9 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/07. Disadvantages of Single Imputation Methods.vtt 2.9 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/01. Module Overview.vtt 2.9 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/playlist.m3u 2.9 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/05. Demo - Bag-of-n-grams.vtt 2.9 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/playlist.m3u 2.9 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/8. Summary.vtt 2.9 kBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/2. The Use Case - Media Insights Solution.vtt 2.8 kBE4. 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Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/06. TF-IDF Encoding.vtt 2.7 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/6. Feature Engineering Categorical Variables.vtt 2.7 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/04. Demo - Data Cleaning (Erroneous Data).vtt 2.7 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/playlist.m3u 2.7 kBE6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/4. Measuring and Detecting Problems Due to Feature Set Complexity.vtt 2.7 kBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/2. Understanding Feature Selection.vtt 2.7 kBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/1. Performing Feature Selection.vtt 2.7 kBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/3. The Key Valet Pattern.vtt 2.7 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/5. EventGrids Overview.vtt 2.6 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/05. Demo - Binning.vtt 2.6 kBF4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/2. Communicating Knowledge and Insights.vtt 2.6 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/10. Asking the Right Questions.vtt 2.6 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/2. Imbalanced Dataset for Classification Problems.vtt 2.5 kBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/3. Your New Data Broker.vtt 2.5 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/3. ADX Health and Performance Monitoring.vtt 2.5 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/5. Azure Data Explorer Features.vtt 2.5 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/03. Skills Recommended for This Course.vtt 2.5 kBC2. Experimental Design for Data Analysis (Janani Ravi, 2019)/~i.txt 2.5 kBA2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/1. Course Overview/1. Course Overview.vtt 2.5 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/2. Managing ADX Database Permissions.vtt 2.5 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/1. Course Overview/1. Course Overview.vtt 2.5 kBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/1. Exploring Your Dataset for Feature Selection and Extraction.vtt 2.5 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/09. How MICE Works.vtt 2.5 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/08. Z-score.vtt 2.4 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/1. Course Overview/1. Course Overview.vtt 2.4 kBE1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/05. Demo - Using Indicator Variables.vtt 2.4 kBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/3. Azure Automated Machine Learning.vtt 2.4 kBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/6. Ethical and Legal Barriers to Data Use.vtt 2.4 kBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/6. Course Review.vtt 2.4 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/04. Binning.vtt 2.4 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/16. Summary.vtt 2.4 kBA3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/3. Available Data Sources.vtt 2.4 kBD2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/4. Scaling the ADX Cluster.vtt 2.3 kBE2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/07. Kurtosis.vtt 2.3 kBA1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/09. Data Science Project Lifecycle.vtt 2.3 kBF2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/5. Demo - Interpreting the Experiment Results.vtt 2.3 kBE4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/08. Demo - Token Cleaning.vtt 2.3 kBC1. Summarizing Data and Deducing Probabilities (Janani Ravi, 2021)/~i.txt 2.3 kBD1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/1. Introduction.vtt 2.3 kBE3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/4. Determining the Feature Structure Appropriate for the Algorithm.vtt 2.3 kBE2. 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