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- 【影视】 [ FreeCourseWeb.com ] Udemy - Machine Learning on AWS SageMaker for Beginners
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~Get Your Files Here !/30-lab_018a_univariate_and_multivariate_analysis.mkv 88.1 MB
~Get Your Files Here !/10-009___supervised_learning.mkv 70.4 MB
~Get Your Files Here !/38-lab_25___accessing_pca_model_attributes.mkv 66.1 MB
~Get Your Files Here !/29-lab_017___exploratory_data_analysis.mkv 65.1 MB
~Get Your Files Here !/35-lab_22___problem_statement_and_data_import.mkv 65.1 MB
~Get Your Files Here !/04-003_cloud_computing_services.mkv 57.9 MB
~Get Your Files Here !/31-lab_018b_univariate_and_multivariate_analysis.mkv 55.3 MB
~Get Your Files Here !/28-lab__016___problem_statement_and_data_import.mkv 54.9 MB
~Get Your Files Here !/23-lab_11b___data_cleaning___part___ii.mkv 50.6 MB
~Get Your Files Here !/36-lab_23___exploratory_data_analysis.mkv 49.5 MB
~Get Your Files Here !/41-lab_28__accessing_k_means_model_attributes.mkv 48.8 MB
~Get Your Files Here !/15-lab_003___create_a_notebook_instance.mkv 43.3 MB
~Get Your Files Here !/22-lab_11a___data_cleaning_part___i.mkv 41.5 MB
~Get Your Files Here !/03-002__what_is_cloud_computing.mkv 41.0 MB
~Get Your Files Here !/25-lab_13___training_model_in_sagemaker.mkv 38.7 MB
~Get Your Files Here !/21-lab_10___exploratory_data_analysis.mkv 37.3 MB
~Get Your Files Here !/39-lab_26___model_deployment_and_conclusion_analysis.mkv 36.6 MB
~Get Your Files Here !/01-introduction_to_course.mkv 34.2 MB
~Get Your Files Here !/13-lab_001___create_aws_account.mkv 33.5 MB
~Get Your Files Here !/20-lab_09___importing_dataset_to_jupyter_notebook.mkv 31.9 MB
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Modulo 1 - Fundamentos de Deep Learning/Aulas práticas I/Aula Prática 6.mp4 325.6 MB
Modulo 1 - Fundamentos de Deep Learning/Aulas práticas I/Aula Prática 1.mp4 164.0 MB
Modulo 1 - Fundamentos de Deep Learning/Aula 02 - Redes Neurais Artificiais/Aula 2.3. Funcionamento básico.mp4 161.6 MB
Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 3.3.mp4 154.1 MB
Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 1.1.mp4 138.1 MB
Modulo 1 - Fundamentos de Deep Learning/Aula 01 - Introdução ao Deep Learning/Aula 1.1. Aprendizado de máquina.mkv 136.3 MB
Modulo 2 - Processamento de Linguagem Natural/Aula 1 - Introdução/Aula 1.5. Python Tutorial.mp4 135.2 MB
Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 1.2.mp4 133.4 MB
Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 2.5.mp4 129.2 MB
Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 2.1.mp4 127.7 MB
Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 1.4.mp4 123.4 MB
Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 2.3.mp4 120.3 MB
Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 3.2.mp4 115.8 MB
Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 3.5.2.mp4 109.0 MB
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Módulo 4 - Aplicações de Aprendizado de Máquina para IoT/AML 4.2.mp4 101.6 MB
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06 Descriptive statistics/039 Code_ data from different distributions.mp4 318.3 MB
16 Clustering and dimension-reduction/193 Code_ dbscan.mp4 302.7 MB
12 Correlation/140 Code_ correlation matrix.mp4 296.5 MB
06 Descriptive statistics/047 Code_ Computing dispersion.mp4 279.5 MB
10 The t-test family/126 Code_ permutation testing.mp4 253.0 MB
16 Clustering and dimension-reduction/189 Code_ k-means clustering.mp4 241.9 MB
12 Correlation/137 Code_ correlation coefficient.mp4 225.1 MB
10 The t-test family/119 Code_ Two-samples t-test.mp4 221.9 MB
12 Correlation/152 Code_ Kendall correlation.mp4 193.4 MB
13 Analysis of Variance (ANOVA)/163 Code_ One-way ANOVA (independent samples).mp4 181.3 MB
14 Regression/175 Code_ Multiple regression.mp4 179.6 MB
08 Probability theory/098 Code_ Law of Large Numbers in action.mp4 174.0 MB
10 The t-test family/122 Code_ Signed-rank test.mp4 170.0 MB
10 The t-test family/116 Code_ One-sample t-test.mp4 165.9 MB
08 Probability theory/092 Code_ sampling variability.mp4 162.7 MB
11 Confidence intervals on parameters/130 Code_ compute confidence intervals by formula.mp4 156.9 MB
13 Analysis of Variance (ANOVA)/156 ANOVA intro, part1.mp4 144.6 MB
07 Data normalizations and outliers/070 Code_ z-score for outlier removal.mp4 143.9 MB
08 Probability theory/081 Code_ compute probabilities.mp4 143.8 MB
11 Confidence intervals on parameters/132 Code_ bootstrapping confidence intervals.mp4 143.4 MB
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~Get Your Files Here !/1. Introduction to Machine Learning with Real Hearth Attack Prediction Project/6. Recognizing Variables In Dataset.mp4 120.9 MB
~Get Your Files Here !/1. Introduction to Machine Learning with Real Hearth Attack Prediction Project/1. First Step to the Hearth Attack Prediction Project.mp4 113.8 MB
~Get Your Files Here !/1. Introduction to Machine Learning with Real Hearth Attack Prediction Project/3. Notebook Design to be Used in the Project.mp4 102.4 MB
~Get Your Files Here !/3. Preparation For Exploratory Data Analysis (EDA) in Data Science/4. Examining Statistics of Variables.mp4 88.4 MB
~Get Your Files Here !/5. Exploratory Data Analysis (EDA) - Bi-variate Analysis/14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 86.5 MB
~Get Your Files Here !/4. Exploratory Data Analysis (EDA) - Uni-variate Analysis/4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 81.8 MB
~Get Your Files Here !/4. Exploratory Data Analysis (EDA) - Uni-variate Analysis/1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 78.2 MB
~Get Your Files Here !/1. Introduction to Machine Learning with Real Hearth Attack Prediction Project/5. Examining the Project Topic.mp4 75.1 MB
~Get Your Files Here !/4. Exploratory Data Analysis (EDA) - Uni-variate Analysis/3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 72.4 MB
~Get Your Files Here !/5. Exploratory Data Analysis (EDA) - Bi-variate Analysis/10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 67.1 MB
~Get Your Files Here !/2. First Organization/1. Required Python Libraries.mp4 61.6 MB
~Get Your Files Here !/2. First Organization/3. Initial analysis on the dataset.mp4 61.5 MB
~Get Your Files Here !/7. Modelling for Machine Learning/4. Hyperparameter Optimization (with GridSearchCV).mp4 57.4 MB
~Get Your Files Here !/5. Exploratory Data Analysis (EDA) - Bi-variate Analysis/4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 54.9 MB
~Get Your Files Here !/4. Exploratory Data Analysis (EDA) - Uni-variate Analysis/5. Examining the Missing Data According to the Analysis Result.mp4 52.4 MB
~Get Your Files Here !/5. Exploratory Data Analysis (EDA) - Bi-variate Analysis/8. Creating a New DataFrame with the Melt() Function.mp4 51.1 MB
~Get Your Files Here !/7. Modelling for Machine Learning/8. Hyperparameter Optimization (with GridSearchCV).mp4 50.9 MB
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[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/4. Auto Sklearn Part2/4. Model Building.mp4 109.3 MB
[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/7. Auto Keras Models/9. Text classification using Autokeras.mp4 108.0 MB
[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/7. Auto Keras Models/5. Image classification using CNN Autokeras.mp4 105.7 MB
[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/3. Auto Sklearn Part 1/3. Data Preprocessing And Data Visualization.mp4 97.6 MB
[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/7. Auto Keras Models/7. Object recognition using autokeras.mp4 87.1 MB
[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/3. Auto Sklearn Part 1/4. Building a model.mp4 79.3 MB
[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/7. Auto Keras Models/1. Image classification using AutoKeras.mp4 72.1 MB
[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/4. Auto Sklearn Part2/2.1 Credit_card_fraud_detection.zip 69.9 MB
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[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/5. TPOT Models Part 1/3. Data preprocessing and visualization.mp4 63.2 MB
[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)/6. TPOT Models Part 2/4. Model building.mp4 60.3 MB
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01.Getting ready!/0104.Setting Up Our Machine Learning Environment.mp4 100.4 MB
05.Finding Hidden Value in Unlabeled Data/0504.Analyzing Plant Species Using K-means Clustering.mp4 79.5 MB
04. Using Support Vector Machine and Random Forests for Complex Problems/0404.Making Better Decision with Decision Trees.mp4 74.0 MB
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06.Deep Neural Networks/0603.Neural Networks in Tensorflow.js.mp4 71.7 MB
06.Deep Neural Networks/0604.Multiclass Classification Using TensorFlow.js.mp4 63.6 MB
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06.Deep Neural Networks/0602.How a Neural Network Works.mp4 60.9 MB
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04. Using Support Vector Machine and Random Forests for Complex Problems/0403.Image Classifier Using SVM.mp4 35.0 MB
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