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16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 168.2 MB40. Part 6 Mathematics/16. Why is Linear Algebra Useful.mp4 151.4 MB5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4 145.0 MB10. Combinatorics/20. A Practical Example of Combinatorics.mp4 140.7 MB3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 133.0 MB5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 131.2 MB5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 129.5 MB53. Software Integration/5. Taking a Closer Look at APIs.mp4 121.2 MB20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 118.1 MB2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4 114.3 MB53. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4 109.1 MB6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 108.5 MB51. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4 108.4 MB19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 107.7 MB5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 104.2 MB13. Probability in Other Fields/1. Probability in Finance.mp4 103.9 MB35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).mp4 101.8 MB12. Probability Distributions/3. Types of Probability Distributions.mp4 96.8 MB20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp4 96.5 MB5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 94.3 MB51. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp4 91.9 MB36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 90.7 MB9. Part 2 Probability/1. The Basic Probability Formula.mp4 90.1 MB2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp4 85.4 MB4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4 85.1 MB55. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp4 85.0 MB12. Probability Distributions/15. Characteristics of Continuous Distributions.mp4 83.7 MB18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4 82.0 MB13. Probability in Other Fields/2. Probability in Statistics.mp4 81.0 MB51. Deep Learning - Business Case Example/6. Creating a Data Provider.mp4 80.1 MB9. Part 2 Probability/3. Computing Expected Values.mp4 79.4 MB5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 79.2 MB22. Part 4 Introduction to Python/3. Why Python.mp4 78.7 MB55. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp4 78.2 MB38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp4 78.1 MB12. Probability Distributions/1. Fundamentals of Probability Distributions.mp4 77.0 MB8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 76.4 MB53. Software Integration/9. Software Integration - Explained.mp4 76.2 MB15. Statistics - Descriptive Statistics/1. Types of Data.mp4 76.0 MB37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 75.0 MB18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp4 73.9 MB21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4 72.9 MB53. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp4 72.4 MB2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4 71.0 MB12. Probability Distributions/11. Discrete Distributions The Binomial Distribution.mp4 68.7 MB2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 67.7 MB13. Probability in Other Fields/3. Probability in Data Science.mp4 66.6 MB17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp4 65.9 MB50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4 65.8 MB1. Part 1 Introduction/2. What Does the Course Cover.mp4 65.3 MB55. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp4 64.9 MB55. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp4 64.8 MB9. Part 2 Probability/5. Frequency.mp4 64.7 MB17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 64.6 MB43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 64.1 MB53. Software Integration/7. Communication between Software Products through Text Files.mp4 63.3 MB11. Bayesian Inference/20. Bayes' Law.mp4 62.5 MB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4 62.2 MB58. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp4 62.2 MB18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp4 62.0 MB9. Part 2 Probability/7. Events and Their Complements.mp4 62.0 MB52. Deep Learning - Conclusion/3. An overview of CNNs.mp4 61.7 MB22. Part 4 Introduction to Python/1. Introduction to Programming.mp4 61.4 MB12. Probability Distributions/13. Discrete Distributions The Poisson Distribution.mp4 61.3 MB14. Part 3 Statistics/1. Population and Sample.mp4 60.9 MB35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).mp4 60.7 MB32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.mp4 60.2 MB10. Combinatorics/11. Solving Combinations.mp4 60.1 MB55. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp4 60.1 MB11. Bayesian Inference/7. Union of Sets.mp4 60.0 MB18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp4 59.8 MB58. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp4 59.3 MB50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4 59.1 MB38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp4 58.8 MB35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).mp4 58.8 MB20. Statistics - Hypothesis Testing/10. p-value.mp4 58.6 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.mp4 58.4 MB42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 58.3 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.mp4 57.5 MB15. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 57.0 MB7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4 57.0 MB57. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp4 56.9 MB20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4 56.9 MB2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp4 56.2 MB11. Bayesian Inference/1. Sets and Events.mp4 56.1 MB37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4 56.0 MB51. Deep Learning - Business Case Example/7. Business Case Model Outline.mp4 55.7 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp4 55.3 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp4 54.9 MB54. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 54.8 MB38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4 54.3 MB22. Part 4 Introduction to Python/7. Installing Python and Jupyter.mp4 53.5 MB49. Deep Learning - Preprocessing/3. Standardization.mp4 53.5 MB15. Statistics - Descriptive Statistics/22. Variance.mp4 53.4 MB20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4 52.8 MB18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4 52.4 MB17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4 52.3 MB40. Part 6 Mathematics/5. Linear Algebra and Geometry.mp4 52.2 MB32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.mp4 52.1 MB40. Part 6 Mathematics/15. Dot Product of Matrices.mp4 51.8 MB34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp4 51.6 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp4 51.5 MB1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4 51.4 MB12. Probability Distributions/17. Continuous Distributions The Normal Distribution.mp4 50.6 MB12. Probability Distributions/19. Continuous Distributions The Standard Normal Distribution.mp4 50.2 MB17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp4 50.2 MB55. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp4 50.1 MB44. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp4 50.0 MB12. Probability Distributions/27. Continuous Distributions The Logistic Distribution.mp4 49.3 MB50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp4 49.0 MB35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).mp4 48.2 MB11. Bayesian Inference/13. The Conditional Probability Formula.mp4 48.1 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp4 48.0 MB11. Bayesian Inference/3. Ways Sets Can Interact.mp4 47.6 MB15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp4 47.3 MB42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4 47.3 MB52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4 46.9 MB32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.mp4 46.8 MB39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4 46.7 MB32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.mp4 46.7 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp4 46.6 MB22. Part 4 Introduction to Python/5. Why Jupyter.mp4 46.5 MB38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp4 46.3 MB20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4 46.1 MB50. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp4 46.0 MB10. Combinatorics/9. Solving Variations without Repetition.mp4 45.2 MB38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp4 45.1 MB42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4 45.0 MB11. Bayesian Inference/18. The Multiplication Law.mp4 45.0 MB5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4 44.9 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.mp4 44.8 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp4 43.6 MB51. Deep Learning - Business Case Example/8. Business Case Optimization.mp4 43.5 MB10. Combinatorics/3. Permutations and How to Use Them.mp4 43.5 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 43.2 MB32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.mp4 43.0 MB10. Combinatorics/19. A Recap of Combinatorics.mp4 42.9 MB54. Case Study - What's Next in the Course/3. Introducing the Data Set.mp4 42.9 MB58. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp4 42.6 MB32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.mp4 42.6 MB55. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp4 42.5 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp4 42.4 MB20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4 42.2 MB12. Probability Distributions/25. Continuous Distributions The Exponential Distribution.mp4 42.2 MB15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp4 41.7 MB52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp4 41.7 MB55. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp4 41.5 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp4 41.5 MB42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4 41.3 MB51. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp4 41.3 MB10. Combinatorics/17. Combinatorics in Real-Life The Lottery.mp4 41.3 MB54. Case Study - What's Next in the Course/2. The Business Task.mp4 41.1 MB34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp4 41.0 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp4 40.8 MB55. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp4 40.6 MB10. Combinatorics/13. Symmetry of Combinations.mp4 40.6 MB44. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 40.4 MB15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 40.3 MB36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp4 40.3 MB42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 40.2 MB40. Part 6 Mathematics/13. Transpose of a Matrix.mp4 39.9 MB38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp4 39.5 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp4 39.3 MB44. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp4 39.2 MB42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4 39.0 MB15. Statistics - Descriptive Statistics/17. Mean, median and mode.mp4 38.9 MB5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4 38.6 MB20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp4 38.2 MB51. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp4 38.2 MB37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4 37.9 MB10. Combinatorics/5. Simple Operations with Factorials.mp4 37.9 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.mp4 37.4 MB18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp4 37.2 MB11. Bayesian Inference/15. The Law of Total Probability.mp4 36.9 MB43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4 36.6 MB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4 36.6 MB34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp4 36.6 MB11. Bayesian Inference/11. Dependence and Independence of Sets.mp4 36.5 MB34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.mp4 36.5 MB36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4 36.4 MB12. Probability Distributions/9. Discrete Distributions The Bernoulli Distribution.mp4 35.8 MB10. Combinatorics/7. Solving Variations with Repetition.mp4 35.7 MB40. Part 6 Mathematics/3. Scalars and Vectors.mp4 35.5 MB40. Part 6 Mathematics/1. What is a matrix.mp4 35.2 MB30. Python - Advanced Python Tools/1. Object Oriented Programming.mp4 35.2 MB26. Python - Conditional Statements/4. The ELIF Statement.mp4 34.8 MB10. Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp4 34.8 MB36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp4 34.5 MB46. Deep Learning - Overfitting/3. What is Validation.mp4 34.3 MB40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.mp4 34.2 MB44. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp4 34.1 MB36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp4 33.8 MB36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp4 33.8 MB18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp4 33.8 MB34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 33.6 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.mp4 33.1 MB41. Part 7 Deep Learning/1. What to Expect from this Part.mp4 32.6 MB46. Deep Learning - Overfitting/1. What is Overfitting.mp4 32.6 MB34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.mp4 32.4 MB28. Python - Sequences/5. List Slicing.mp4 32.3 MB23. Python - Variables and Data Types/5. Python Strings.mp4 32.3 MB22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4 32.1 MB36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp4 32.0 MB
10. Combinatorics/5. Simple Operations with Factorials.vtt 31.8 MB38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp4 31.6 MB25. Python - Other Python Operators/3. Logical and Identity Operators.mp4 31.5 MB20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4 31.4 MB5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4 31.4 MB39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 31.1 MB5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 31.0 MB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4 31.0 MB34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp4 31.0 MB55. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp4 31.0 MB15. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp4 30.8 MB48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 30.5 MB39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4 30.5 MB49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4 30.3 MB18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp4 30.2 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/16. A5 No Multicollinearity.mp4 30.1 MB42. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4 30.1 MB48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp4 30.1 MB42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4 29.8 MB32. Advanced Statistical Methods - Linear regression with StatsModels/15. What is the OLS.mp4 29.7 MB55. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp4 29.3 MB55. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp4 29.2 MB49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4 29.1 MB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4 29.0 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp4 28.9 MB15. Statistics - Descriptive Statistics/27. Covariance.mp4 28.8 MB38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4 28.6 MB34. Advanced Statistical Methods - Linear Regression with sklearn/1. What is sklearn and How is it Different from Other Packages.mp4 28.6 MB12. Probability Distributions/21. Continuous Distributions The Students' T Distribution.mp4 28.5 MB36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4 28.4 MB11. Bayesian Inference/5. Intersection of Sets.mp4 28.3 MB18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp4 28.1 MB40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 28.0 MB12. Probability Distributions/23. Continuous Distributions The Chi-Squared Distribution.mp4 27.6 MB48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 27.6 MB34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp4 27.2 MB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4 27.2 MB50. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp4 27.1 MB15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp4 27.1 MB11. Bayesian Inference/16. The Additive Rule.mp4 27.0 MB51. Deep Learning - Business Case Example/9. Business Case Interpretation.mp4 27.0 MB55. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp4 26.9 MB57. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp4 26.7 MB11. Bayesian Inference/9. Mutually Exclusive Sets.mp4 26.6 MB23. Python - Variables and Data Types/1. Variables.mp4 26.5 MB52. Deep Learning - Conclusion/5. An Overview of RNNs.mp4 26.5 MB46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp4 26.4 MB42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp4 26.3 MB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp4 26.3 MB46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp4 26.3 MB28. Python - Sequences/7. Dictionaries.mp4 26.3 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20. Making Predictions with the Linear Regression.mp4 25.9 MB43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp4 25.6 MB12. Probability Distributions/7. Discrete Distributions The Uniform Distribution.mp4 25.6 MB46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4 25.3 MB40. Part 6 Mathematics/14. Dot Product.mp4 25.2 MB27. Python - Python Functions/2. How to Create a Function with a Parameter.mp4 25.0 MB35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).mp4 24.8 MB42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4 24.4 MB55. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp4 24.3 MB36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp4 24.2 MB17. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp4 23.9 MB12. Probability Distributions/5. Characteristics of Discrete Distributions.mp4 23.8 MB42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp4 23.8 MB50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4 23.7 MB40. Part 6 Mathematics/8. What is a Tensor.mp4 23.6 MB17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp4 23.6 MB48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp4 23.4 MB36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp4 23.4 MB5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp4 23.1 MB27. Python - Python Functions/7. Built-in Functions in Python.mp4 23.1 MB28. Python - Sequences/1. Lists.mp4 23.1 MB28. Python - Sequences/3. Using Methods.mp4 23.0 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/7. OLS Assumptions.mp4 22.9 MB47. Deep Learning - Initialization/1. What is Initialization.mp4 22.8 MB55. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp4 22.7 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/1. Multiple Linear Regression.mp4 22.6 MB38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp4 22.3 MB46. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp4 21.7 MB43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp4 21.6 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp4 21.6 MB44. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.mp4 21.3 MB55. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp4 21.2 MB52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp4 21.1 MB34. Advanced Statistical Methods - Linear Regression with sklearn/7. Multiple Linear Regression with sklearn.mp4 21.1 MB18. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).mp4 20.9 MB30. Python - Advanced Python Tools/7. Importing Modules in Python.mp4 20.9 MB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp4 20.5 MB34. Advanced Statistical Methods - Linear Regression with sklearn/2. How are Going to Approach this Section.mp4 20.3 MB15. Statistics - Descriptive Statistics/19. Skewness.mp4 20.3 MB24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp4 19.8 MB50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp4 19.8 MB49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp4 19.5 MB30. Python - Advanced Python Tools/5. What is the Standard Library.mp4 18.9 MB42. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp4 18.8 MB50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp4 18.7 MB44. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.mp4 18.3 MB31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp4 18.2 MB47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 18.0 MB36. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp4 17.9 MB23. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp4 17.9 MB29. Python - Iterations/8. How to Iterate over Dictionaries.mp4 17.8 MB34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.mp4 17.8 MB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.mp4 17.6 MB28. Python - Sequences/6. Tuples.mp4 17.5 MB48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp4 17.2 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/6. Test for Significance of the Model (F-Test).mp4 17.2 MB10. Combinatorics/1. Fundamentals of Combinatorics.mp4 17.0 MB29. Python - Iterations/6. Conditional Statements and Loops.mp4 16.9 MB27. Python - Python Functions/5. Conditional Statements and Functions.mp4 16.4 MB17. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp4 16.3 MB29. Python - Iterations/3. While Loops and Incrementing.mp4 16.2 MB27. Python - Python Functions/3. Defining a Function in Python - Part II.mp4 15.5 MB32. Advanced Statistical Methods - Linear regression with StatsModels/3. Correlation vs Regression.mp4 15.5 MB44. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.mp4 15.3 MB37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp4 15.3 MB47. Deep Learning - Initialization/2. Types of Simple Initializations.mp4 15.0 MB55. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp4 14.7 MB22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 14.5 MB15. Statistics - Descriptive Statistics/11. The Histogram.mp4 14.4 MB55. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.vtt 14.4 MB55. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp4 14.4 MB26. Python - Conditional Statements/1. The IF Statement.mp4 14.3 MB26. Python - Conditional Statements/3. The ELSE Statement.mp4 14.3 MB50. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp4 13.5 MB33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/9. A1 Linearity.mp4 13.2 MB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp4 13.1 MB34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with p-values.mp4 12.9 MB32. Advanced Statistical Methods - Linear regression with StatsModels/10. Using Seaborn for Graphs.mp4 12.8 MB51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp4 12.8 MB49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp4 12.4 MB29. Python - Iterations/1. For Loops.mp4 12.4 MB29. Python - Iterations/4. Lists with the range() Function.mp4 12.0 MB22. Part 4 Introduction to Python/11. Python 2 vs Python 3.mp4 11.8 MB26. Python - Conditional Statements/5. A Note on Boolean Values.mp4 11.8 MB51. Deep Learning - Business Case Example/10. Business Case Testing the Model.mp4 11.7 MB40. Part 6 Mathematics/12. Errors when Adding Matrices.mp4 11.7 MB48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp4 11.6 MB25. Python - Other Python Operators/1. Comparison Operators.mp4 10.7 MB38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp4 10.4 MB29. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp4 9.9 MB48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp4 9.5 MB30. Python - Advanced Python Tools/3. Modules and Packages.mp4 8.9 MB27. Python - Python Functions/4. How to Use a Function within a Function.mp4 8.5 MB27. Python - Python Functions/1. Defining a Function in Python.mp4 8.1 MB27. Python - Python Functions/6. Functions Containing a Few Arguments.mp4 7.9 MB
2. The Field of Data Science - The Various Data Science Disciplines/7.1 365_DataScience.png.png 7.3 MB2. The Field of Data Science - The Various Data Science Disciplines/9.1 365_DataScience.png.png 7.3 MB24. Python - Basic Python Syntax/12. Structuring with Indentation.mp4 7.2 MB24. Python - Basic Python Syntax/3. The Double Equality Sign.mp4 6.3 MB24. Python - Basic Python Syntax/10. Indexing Elements.mp4 6.2 MB32. Advanced Statistical Methods - Linear regression with StatsModels/5. Geometrical Representation of the Linear Regression Model.mp4 5.4 MB24. Python - Basic Python Syntax/7. Add Comments.mp4 5.2 MB24. Python - Basic Python Syntax/5. How to Reassign Values.mp4 4.2 MB24. Python - Basic Python Syntax/9. Understanding Line Continuation.mp4 2.5 MB
22. Part 4 Introduction to Python/11.1 Python Introduction - Course Notes.pdf.pdf 2.1 MB23. Python - Variables and Data Types/1.2 Python Introduction - Course Notes.pdf.pdf 2.1 MB19. Statistics - Practical Example Inferential Statistics/1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx 1.8 MB19. Statistics - Practical Example Inferential Statistics/2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx 1.8 MB19. Statistics - Practical Example Inferential Statistics/2.1 3.17. Practical example. Confidence intervals_exercise.xlsx.xlsx 1.8 MB20. Statistics - Hypothesis Testing/10.1 Online p-value calculator.pdf.pdf 1.2 MB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1.1 Course Notes - Section 6.pdf.pdf 958.9 kB45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2.1 Course Notes - Section 6.pdf.pdf 958.9 kB51. Deep Learning - Business Case Example/1.1 Audiobooks_data.csv.csv 727.8 kB20. 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Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.vtt 5.1 kB15. Statistics - Descriptive Statistics/17. Mean, median and mode.vtt 5.1 kB25. Python - Other Python Operators/3. Logical and Identity Operators.vtt 5.1 kB55. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.vtt 5.1 kB10. Combinatorics/11. Solving Combinations.vtt 5.1 kB5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.vtt 5.1 kB11. Bayesian Inference/7. Union of Sets.vtt 5.1 kB17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.vtt 5.1 kB46. Deep Learning - Overfitting/1. What is Overfitting.vtt 5.1 kB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.vtt 5.0 kB20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.vtt 5.0 kB34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.vtt 5.0 kB32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.vtt 5.0 kB56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.vtt 5.0 kB36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.vtt 5.0 kB28. Python - Sequences/5. List Slicing.vtt 4.9 kB18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.vtt 4.9 kB14. Part 3 Statistics/1. Population and Sample.vtt 4.9 kB53. Software Integration/7. Communication between Software Products through Text Files.vtt 4.9 kB54. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt 4.9 kB20. Statistics - Hypothesis Testing/16. Test for the mean. 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Statistics - Descriptive Statistics/6. Categorical Variables - Visualization Techniques.html 158 Bytes15. Statistics - Descriptive Statistics/9. Numerical Variables - Frequency Distribution Table.html 158 Bytes17. Statistics - Inferential Statistics Fundamentals/10. Central Limit Theorem.html 158 Bytes17. Statistics - Inferential Statistics Fundamentals/12. Standard Error.html 158 Bytes17. Statistics - Inferential Statistics Fundamentals/14. Estimators and Estimates.html 158 Bytes17. Statistics - Inferential Statistics Fundamentals/3. What is a Distribution.html 158 Bytes17. Statistics - Inferential Statistics Fundamentals/5. The Normal Distribution.html 158 Bytes17. Statistics - Inferential Statistics Fundamentals/7. The Standard Normal Distribution.html 158 Bytes18. Statistics - Inferential Statistics Confidence Intervals/11. Margin of Error.html 158 Bytes18. Statistics - Inferential Statistics Confidence Intervals/2. What are Confidence Intervals.html 158 Bytes18. Statistics - Inferential Statistics Confidence Intervals/7. Student's T Distribution.html 158 Bytes2. The Field of Data Science - The Various Data Science Disciplines/10. A Breakdown of our Data Science Infographic.html 158 Bytes2. The Field of Data Science - The Various Data Science Disciplines/2. Data Science and Business Buzzwords Why are there so many.html 158 Bytes2. The Field of Data Science - The Various Data Science Disciplines/4. What is the difference between Analysis and Analytics.html 158 Bytes2. The Field of Data Science - The Various Data Science Disciplines/6. Business Analytics, Data Analytics, and Data Science An Introduction.html 158 Bytes2. The Field of Data Science - The Various Data Science Disciplines/8. Continuing with BI, ML, and AI.html 158 Bytes20. Statistics - Hypothesis Testing/11. p-value.html 158 Bytes20. Statistics - Hypothesis Testing/19. Test for the mean. Independent samples (Part 2).html 158 Bytes20. Statistics - Hypothesis Testing/3. 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