Building Machine Learning Solutions with scikit-learn | Path [2019, ENG]

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Building Machine Learning Solutions with scikit-learn | Path [2019, ENG] (Size: 2.88 GB)

		A1. Building Your First scikit-learn Solution (Janani Ravi, 2019)
		1. Course Overview
		1. Course Overview.mp4	3.09 MB
		1. Course Overview.vtt	2.25 KB
		2. Exploring scikit-learn for Machine Learning
		01. Module Overview.mp4	1.53 MB
		01. Module Overview.vtt	1.61 KB
		02. Prerequisites and Course Outline.mp4	1.22 MB
		02. Prerequisites and Course Outline.vtt	1.74 KB
		03. Introducing Machine Learning.mp4	4.8 MB
		03. Introducing Machine Learning.vtt	5.89 KB
		04. Learning from Data - Training and Prediction.mp4	7.88 MB
		04. Learning from Data - Training and Prediction.vtt	9.29 KB
		05. Traditional and Representation ML Models.mp4	11.46 MB
		05. Traditional and Representation ML Models.vtt	10.78 KB
		06. The Niche of scikit-learn in ML.mp4	7.94 MB
		06. The Niche of scikit-learn in ML.vtt	8.36 KB
		07. Exploring scikit-learn Libraries.mp4	35.84 MB
		07. Exploring scikit-learn Libraries.vtt	10.56 KB
		08. Supervised and Unsupervised Learning.mp4	9.21 MB
		08. Supervised and Unsupervised Learning.vtt	10.25 KB
		09. Installing scikit-learn Libraries.mp4	5.55 MB
		09. Installing scikit-learn Libraries.vtt	5.33 KB
		10. Summary.mp4	1.86 MB
		10. Summary.vtt	1.96 KB
		3. Understanding the Machine Learning Workflow with scikit-learn
		01. Module Overview.mp4	1.41 MB
		01. Module Overview.vtt	1.74 KB
		02. The Machine Learning Workflow.mp4	6.66 MB
		02. The Machine Learning Workflow.vtt	6.9 KB
		03. Using scikit-learn in the Machine Learning Workflow.mp4	9.06 MB
		03. Using scikit-learn in the Machine Learning Workflow.vtt	10.04 KB
		04. Choosing the Right Estimator - Classification.mp4	5.38 MB
		04. Choosing the Right Estimator - Classification.vtt	5.6 KB
		05. Choosing the Right Estimator - Clustering.mp4	3.22 MB
		05. Choosing the Right Estimator - Clustering.vtt	3.45 KB
		06. Choosing the Right Estimator - Regression and Dimensionality Reduction.mp4	4.75 MB
		06. Choosing the Right Estimator - Regression and Dimensionality Reduction.vtt	4.91 KB
		07. Exploring Built-in Datasets in scikit-learn.mp4	10.03 MB
		07. Exploring Built-in Datasets in scikit-learn.vtt	9.02 KB
		08. Exploring the Boston Newsgroups and Digits Datasets.mp4	8.25 MB
		08. Exploring the Boston Newsgroups and Digits Datasets.vtt	4.94 KB
		09. California Housing Dataset - Exploring Numeric and Categorical Features.mp4	11.01 MB
		09. California Housing Dataset - Exploring Numeric and Categorical Features.vtt	8.63 KB
		10. California Housing Dataset - Exploring Relationships in Data.mp4	10.72 MB
		10. California Housing Dataset - Exploring Relationships in Data.vtt	7.49 KB
		11. Summary.mp4	1.97 MB
		11. Summary.vtt	2.53 KB
		4. Building a Simple Machine Learning Model with scikit-learn
		1. Module Overview.mp4	1.28 MB
		1. Module Overview.vtt	1.44 KB
		2. Understanding Linear Regression.mp4	5.99 MB
		2. Understanding Linear Regression.vtt	5.99 KB
		3. Data Preparation for Machine Learning.mp4	13.28 MB
		3. Data Preparation for Machine Learning.vtt	11.44 KB
		4. Training and Prediction Using Linear Regression.mp4	13.62 MB
		4. Training and Prediction Using Linear Regression.vtt	11.24 KB
		5. Understanding Logistic Regression.mp4	9.58 MB
		5. Understanding Logistic Regression.vtt	10.42 KB
		6. Training and Prediction Using a Logistic Regression Classifier.mp4	14.82 MB
		6. Training and Prediction Using a Logistic Regression Classifier.vtt	12.02 KB
		7. Summary and Further Study.mp4	1.8 MB
		7. Summary and Further Study.vtt	2.12 KB
		exercise.7z	2.31 MB
		playlist.m3u	2.87 KB
		~i.txt	1.9 KB
		A2. Building Classification Models with scikit-learn (Janani Ravi, 2019)
		1. Course Overview
		1. Course Overview.mp4	3.07 MB
		1. Course Overview.vtt	2.63 KB
		2. Understanding Classification as a Machine Learning Problem
		1. Module Overview.mp4	1.84 MB
		1. Module Overview.vtt	2.03 KB
		2. Prerequisites and Course Outline.mp4	1.99 MB
		2. Prerequisites and Course Outline.vtt	2.37 KB
		3. Classification as a Machine Learning Problem.mp4	5.53 MB
		3. Classification as a Machine Learning Problem.vtt	5.96 KB
		4. Logistic Regression Intuition.mp4	8.34 MB
		4. Logistic Regression Intuition.vtt	8.19 KB
		5. Cross Entropy Intuition.mp4	3.43 MB
		5. Cross Entropy Intuition.vtt	2.99 KB
		6. Accuracy, Precision, and Recall.mp4	8.75 MB
		6. Accuracy, Precision, and Recall.vtt	8.81 KB
		7. Determining Decision Threshold Using ROC Curves.mp4	9.52 MB
		7. Determining Decision Threshold Using ROC Curves.vtt	9.38 KB
		8. Types of Classification.mp4	5.92 MB
		8. Types of Classification.vtt	5.33 KB
		9. Module Summary.mp4	1.58 MB
		9. Module Summary.vtt	1.83 KB
		3. Building a Simple Classification Model
		1. Module Overview.mp4	1.55 MB
		1. Module Overview.vtt	1.68 KB
		2. Installing and Setting up scikit-learn.mp4	5.39 MB
		2. Installing and Setting up scikit-learn.vtt	4.41 KB
		3. Exploring the Titanic Dataset.mp4	14.48 MB
		3. Exploring the Titanic Dataset.vtt	10.98 KB
		4. Visualizing Relationships in the Data.mp4	9.09 MB
		4. Visualizing Relationships in the Data.vtt	7.76 KB
		5. Preprocessing the Data.mp4	9.09 MB
		5. Preprocessing the Data.vtt	6.22 KB
		6. Training a Logistic Regression Binary Classifier.mp4	9.81 MB
		6. Training a Logistic Regression Binary Classifier.vtt	7.38 KB
		7. Calculating Accuracy, Precision and Recall for the Classification Model.mp4	9.82 MB
		7. Calculating Accuracy, Precision and Recall for the Classification Model.vtt	6.96 KB
		8. Defining Helper Functions to Train and Evaluate Classification Models.mp4	12.67 MB
		8. Defining Helper Functions to Train and Evaluate Classification Models.vtt	10.39 KB
		9. Module Summary.mp4	1.89 MB
		9. Module Summary.vtt	1.96 KB
		4. Performing Classification Using Multiple Techniques
		01. Module Overview.mp4	1.91 MB
		01. Module Overview.vtt	2.19 KB
		02. Choosing Classification Algorithms.mp4	3.37 MB
		02. Choosing Classification Algorithms.vtt	3.21 KB
		03. Linear Discriminant Analysis and Quadratic Discriminant Analysis.mp4	11.35 MB
		03. Linear Discriminant Analysis and Quadratic Discriminant Analysis.vtt	10.6 KB
		04. Implementing Linear Discriminant Analysis Classification.mp4	8.9 MB
		04. Implementing Linear Discriminant Analysis Classification.vtt	5.5 KB
		05. Implementing Quadratic Discriminant Analysis Classification.mp4	4.11 MB
		05. Implementing Quadratic Discriminant Analysis Classification.vtt	2.64 KB
		06. Stochastic Gradient Descent.mp4	3.87 MB
		06. Stochastic Gradient Descent.vtt	3.97 KB
		07. Implementing Stochastic Gradient Descent Classification.mp4	5.55 MB
		07. Implementing Stochastic Gradient Descent Classification.vtt	3.65 KB
		08. Support Vector Machines.mp4	11.49 MB
		08. Support Vector Machines.vtt	10.51 KB
		09. Implementing Support Vector Classification.mp4	5.62 MB
		09. Implementing Support Vector Classification.vtt	3.92 KB
		10. Nearest Neighbors.mp4	5.03 MB
		10. Nearest Neighbors.vtt	4.56 KB
		11. Implementing K-nearest-neighbors Classification.mp4	2.8 MB
		11. Implementing K-nearest-neighbors Classification.vtt	2.05 KB
		12. Decision Trees.mp4	4.54 MB
		12. Decision Trees.vtt	4.53 KB
		13. Implementing Decision Tree Classification.mp4	5.54 MB
		13. Implementing Decision Tree Classification.vtt	3.73 KB
		14. Naive Bayes.mp4	5.9 MB
		14. Naive Bayes.vtt	6.21 KB
		15. Implementing Naive Bayes Classification.mp4	3.44 MB
		15. Implementing Naive Bayes Classification.vtt	2.16 KB
		16. Module Summary.mp4	2.22 MB
		16. Module Summary.vtt	2.51 KB
		5. Hyperparameter Tuning for Classification Models
		1. Module Overview.mp4	1.41 MB
		1. Module Overview.vtt	1.54 KB
		2. Hyperparameter Tuning.mp4	5.64 MB
		2. Hyperparameter Tuning.vtt	5.87 KB
		3. Hyperparameter Tuning a Decision Tree Clasifier Using Grid Search.mp4	13.61 MB
		3. Hyperparameter Tuning a Decision Tree Clasifier Using Grid Search.vtt	9.45 KB
		4. Hyperparameter Tuning a Logistic Regression Classifier Using Grid Search.mp4	4.91 MB
		4. Hyperparameter Tuning a Logistic Regression Classifier Using Grid Search.vtt	3.54 KB
		5. Module Summary.mp4	1.36 MB
		5. Module Summary.vtt	1.65 KB
		6. Applying Classification Models to Images and Text Data
		1. Module Overview.mp4	1.48 MB
		1. Module Overview.vtt	1.57 KB
		2. Representing Images as Matrices.mp4	4.48 MB
		2. Representing Images as Matrices.vtt	4.19 KB
		3. Exploring the Fashion MNIST Dataset.mp4	12.03 MB
		3. Exploring the Fashion MNIST Dataset.vtt	8.36 KB
		4. Classifying Images Using Logistic Regression.mp4	9.96 MB
		4. Classifying Images Using Logistic Regression.vtt	5.72 KB
		5. Summary and Further Study.mp4	1.74 MB
		5. Summary and Further Study.vtt	2.08 KB
		exercise.7z	28.95 MB
		playlist.m3u	4.17 KB
		~i.txt	1.9 KB
		A3. Building Regression Models with scikit-learn (Janani Ravi, 2019)
		1. Course Overview
		1. Course Overview.mp4	3.63 MB
		1. Course Overview.vtt	2.83 KB
		2. Understanding Linear Regression as a Machine Learning Problem
		01. Module Overview.mp4	1.79 MB
		01. Module Overview.vtt	1.78 KB
		02. Prerequisites and Course Outline.mp4	1.98 MB
		02. Prerequisites and Course Outline.vtt	2.39 KB
		03. Connecting the Dots with Linear Regression.mp4	10.09 MB
		03. Connecting the Dots with Linear Regression.vtt	10.4 KB
		04. Minimizing Least Square Error.mp4	6.22 MB
		04. Minimizing Least Square Error.vtt	5.58 KB
		05. Installing and Setting up scikit-learn.mp4	4.64 MB
		05. Installing and Setting up scikit-learn.vtt	4.02 KB
		06. Exploring the Automobile Mpg Dataset.mp4	14.51 MB
		06. Exploring the Automobile Mpg Dataset.vtt	10.43 KB
		07. Visualizing Relationships and Correlations in Features.mp4	11.19 MB
		07. Visualizing Relationships and Correlations in Features.vtt	8.46 KB
		08. Mitigating Risks in Simple and Multiple Regression.mp4	8.7 MB
		08. Mitigating Risks in Simple and Multiple Regression.vtt	8.87 KB
		09. R-squared and Adjusted R-squared.mp4	2.21 MB
		09. R-squared and Adjusted R-squared.vtt	2.26 KB
		10. Regression with Categorical Variables.mp4	5.71 MB
		10. Regression with Categorical Variables.vtt	5.46 KB
		11. Module Summary.mp4	1.61 MB
		11. Module Summary.vtt	1.81 KB
		3. Building a Simple Linear Model
		1. Module Overview.mp4	1.51 MB
		1. Module Overview.vtt	1.61 KB
		2. Simple Linear Regression.mp4	16.22 MB
		2. Simple Linear Regression.vtt	10.82 KB
		3. Linear Regression with Multiple Features.mp4	13.14 MB
		3. Linear Regression with Multiple Features.vtt	8.75 KB
		4. Standardizing Numeric Data.mp4	10.02 MB
		4. Standardizing Numeric Data.vtt	6.25 KB
		5. Label Encoding and One-hot Encoding Categorical Data.mp4	10.95 MB
		5. Label Encoding and One-hot Encoding Categorical Data.vtt	6.67 KB
		6. Linear Regression and the Dummy Trap.mp4	12.13 MB
		6. Linear Regression and the Dummy Trap.vtt	7.29 KB
		7. Module Summary.mp4	1.52 MB
		7. Module Summary.vtt	1.66 KB
		4. Building Regularized Regression Models
		01. Module Overview.mp4	1.59 MB
		01. Module Overview.vtt	1.76 KB
		02. Overview of Regression Models in scikit-learn.mp4	3.34 MB
		02. Overview of Regression Models in scikit-learn.vtt	3.36 KB
		03. Overfitting and Regularization.mp4	6.13 MB
		03. Overfitting and Regularization.vtt	6.72 KB
		04. Lasso, Ridge and Elastic Net Regression.mp4	7.43 MB
		04. Lasso, Ridge and Elastic Net Regression.vtt	8.15 KB
		05. Defining Helper Functions to Build and Train Models and Compare Results.mp4	11.14 MB
		05. Defining Helper Functions to Build and Train Models and Compare Results.vtt	8.78 KB
		06. Single Feature, Kitchen Sink, and Parsimonious Linear Regression.mp4	6.86 MB
		06. Single Feature, Kitchen Sink, and Parsimonious Linear Regression.vtt	5.71 KB
		07. Lasso Regression.mp4	6.43 MB
		07. Lasso Regression.vtt	4.37 KB
		08. Ridge Regression.mp4	4.11 MB
		08. Ridge Regression.vtt	2.68 KB
		09. Elastic Net Regression.mp4	13.86 MB
		09. Elastic Net Regression.vtt	8.69 KB
		10. Module Summary.mp4	1.82 MB
		10. Module Summary.vtt	2.05 KB
		5. Performing Regression Using Multiple Techniques
		01. Module Overview.mp4	1.96 MB
		01. Module Overview.vtt	1.98 KB
		02. Choosing Regression Algorithms.mp4	4.15 MB
		02. Choosing Regression Algorithms.vtt	4.14 KB
		03. Support Vector Regression.mp4	8.64 MB
		03. Support Vector Regression.vtt	7.94 KB
		04. Implementing Support Vector Regression.mp4	6.06 MB
		04. Implementing Support Vector Regression.vtt	3.63 KB
		05. Nearest Neighbors Regression.mp4	6.36 MB
		05. Nearest Neighbors Regression.vtt	6.26 KB
		06. Implementing K-nearest-neighbors Regression.mp4	4.42 MB
		06. Implementing K-nearest-neighbors Regression.vtt	2.6 KB
		07. Stochastic Gradient Descent Regression.mp4	4.32 MB
		07. Stochastic Gradient Descent Regression.vtt	4.22 KB
		08. Implementing Stochastic Gradient Descent Regression.mp4	4.51 MB
		08. Implementing Stochastic Gradient Descent Regression.vtt	3.09 KB
		09. Decision Tree Regression.mp4	6.35 MB
		09. Decision Tree Regression.vtt	6.65 KB
		10. Implementing Decision Tree Regression.mp4	3.1 MB
		10. Implementing Decision Tree Regression.vtt	1.94 KB
		11. Least Angle Regression.mp4	5.23 MB
		11. Least Angle Regression.vtt	5.12 KB
		12. Implementing Least Angle Regression.mp4	2.39 MB
		12. Implementing Least Angle Regression.vtt	1.69 KB
		13. Regression with Polynomial Relationships.mp4	2.5 MB
		13. Regression with Polynomial Relationships.vtt	2.87 KB
		14. Module Summary.mp4	2.08 MB
		14. Module Summary.vtt	2.24 KB
		6. Hyperparameter Tuning for Regression Models
		1. Module Overview.mp4	1.49 MB
		1. Module Overview.vtt	1.65 KB
		2. Hyperparameter Tuning.mp4	5.27 MB
		2. Hyperparameter Tuning.vtt	5.39 KB
		3. Hyperparameter Tuning for Lasso Regression Using Grid Search.mp4	11.58 MB
		3. Hyperparameter Tuning for Lasso Regression Using Grid Search.vtt	8.25 KB
		4. Tuning Different Regression Models Using Grid Search.mp4	9.59 MB
		4. Tuning Different Regression Models Using Grid Search.vtt	6.83 KB
		5. Summary and Further Study.mp4	1.44 MB
		5. Summary and Further Study.vtt	1.77 KB
		exercise.7z	4.05 MB
		playlist.m3u	4.2 KB
		~i.txt	1.97 KB
		A4. Building Clustering Models with scikit-learn (Janani Ravi, 2019)
		1. Course Overview
		1. Course Overview.mp4	2.68 MB
		1. Course Overview.vtt	2.21 KB
		2. Building a Simple Clustering Model in scikit-learn
		01. Module Overview.mp4	1.15 MB
		01. Module Overview.vtt	1.23 KB
		02. Prerequisites and Course Outline.mp4	1.8 MB
		02. Prerequisites and Course Outline.vtt	2.44 KB
		03. Supervised and Unsupervised Learning.mp4	6.99 MB
		03. Supervised and Unsupervised Learning.vtt	7.91 KB
		04. Clustering Objectives and Use Cases.mp4	13.54 MB
		04. Clustering Objectives and Use Cases.vtt	12.65 KB
		05. K-means Clustering.mp4	5.99 MB
		05. K-means Clustering.vtt	5.98 KB
		06. Evaluating Clustering Models.mp4	8.14 MB
		06. Evaluating Clustering Models.vtt	9.01 KB
		07. Getting Started with scikit-learn Install and Setup.mp4	5.66 MB
		07. Getting Started with scikit-learn Install and Setup.vtt	5.2 KB
		08. Performing K-means Clustering.mp4	11.14 MB
		08. Performing K-means Clustering.vtt	8.93 KB
		09. Evaluating K-means Clustering.mp4	18.01 MB
		09. Evaluating K-means Clustering.vtt	11.26 KB
		10. Exploring the Iris Dataset.mp4	6.66 MB
		10. Exploring the Iris Dataset.vtt	5.91 KB
		11. Performing K-means Clustering and Evaluation.mp4	11.25 MB
		11. Performing K-means Clustering and Evaluation.vtt	9.25 KB
		3. Performing Clustering Using Multiple Techniques
		01. Module Overview.mp4	1.53 MB
		01. Module Overview.vtt	1.84 KB
		02. Categories of Clustering Algorithms.mp4	5.45 MB
		02. Categories of Clustering Algorithms.vtt	6.26 KB
		03. Setting up Helper Functions to Perform Clustering.mp4	5.79 MB
		03. Setting up Helper Functions to Perform Clustering.vtt	5.17 KB
		04. Choosing Clustering Algorithms.mp4	9.69 MB
		04. Choosing Clustering Algorithms.vtt	10.55 KB
		05. Hierarchical Clustering.mp4	8.1 MB
		05. Hierarchical Clustering.vtt	7.62 KB
		06. Agglomerative Clustering.mp4	7.18 MB
		06. Agglomerative Clustering.vtt	7.22 KB
		07. DBSCAN Clustering.mp4	7.63 MB
		07. DBSCAN Clustering.vtt	7.74 KB
		08. Mean-shift Clustering.mp4	10.24 MB
		08. Mean-shift Clustering.vtt	10.59 KB
		09. BIRCH Clustering.mp4	4.85 MB
		09. BIRCH Clustering.vtt	4.66 KB
		10. Affinilty Propagation Clustering.mp4	6.41 MB
		10. Affinilty Propagation Clustering.vtt	6.15 KB
		11. Mini-batch K-means Clustering.mp4	4.46 MB
		11. Mini-batch K-means Clustering.vtt	4.25 KB
		12. Spectral Clustering Using a Precomputed Matrix.mp4	11.98 MB
		12. Spectral Clustering Using a Precomputed Matrix.vtt	11.25 KB
		4. Hyperparameter Tuning for Clustering Models
		1. Module Overview.mp4	860.16 KB
		1. Module Overview.vtt	1.09 KB
		2. Understanding the Silhouette Score.mp4	4.05 MB
		2. Understanding the Silhouette Score.vtt	4.42 KB
		3. K-means Number of Clusters - The Elbow Method.mp4	5.61 MB
		3. K-means Number of Clusters - The Elbow Method.vtt	6.97 KB
		4. K-means Number of Clusters - The Silhouette Method.mp4	5.02 MB
		4. K-means Number of Clusters - The Silhouette Method.vtt	5.39 KB
		5. Seeds and Distance Measures.mp4	2.23 MB
		5. Seeds and Distance Measures.vtt	2.33 KB
		6. Hyperparameter Tuning - K-means Clustering.mp4	10.39 MB
		6. Hyperparameter Tuning - K-means Clustering.vtt	8.75 KB
		7. Hyperparameter Tuning - DBSCAN Clustering.mp4	11.35 MB
		7. Hyperparameter Tuning - DBSCAN Clustering.vtt	8.29 KB
		8. Hyperparameter Tuning - Mean-shift Clustering.mp4	2.68 MB
		8. Hyperparameter Tuning - Mean-shift Clustering.vtt	2.67 KB
		5. Applying Clustering to Image Data
		1. Module Overview.mp4	1.06 MB
		1. Module Overview.vtt	1.24 KB
		2. Images as Matrices.mp4	4.22 MB
		2. Images as Matrices.vtt	4.55 KB
		3. Exploring the MNIST Handwritten Digits Dataset.mp4	5.43 MB
		3. Exploring the MNIST Handwritten Digits Dataset.vtt	4.62 KB
		4. Clustering Image Data.mp4	8.8 MB
		4. Clustering Image Data.vtt	6.8 KB
		5. Summary and Further Study.mp4	1.65 MB
		5. Summary and Further Study.vtt	2.11 KB
		exercise.7z	8.43 MB
		playlist.m3u	3.15 KB
		~i.txt	1.9 KB
		B1. Building Neural Networks with scikit-learn (Janani Ravi, 2019)
		1. Course Overview
		1. Course Overview.mp4	3.31 MB
		1. Course Overview.vtt	2.99 KB
		2. Introducing Neural Networks in scikit-learn
		1. Module Overview.mp4	1.55 MB
		1. Module Overview.vtt	1.9 KB
		2. Prerequisites and Course Outline.mp4	2.18 MB
		2. Prerequisites and Course Outline.vtt	2.85 KB
		3. Support for Neural Networks in scikit-learn.mp4	7.65 MB
		3. Support for Neural Networks in scikit-learn.vtt	7.44 KB
		4. Perceptrons and Neurons.mp4	10.7 MB
		4. Perceptrons and Neurons.vtt	10.51 KB
		5. Multi-layer Perceptrons and Neural Networks.mp4	4.91 MB
		5. Multi-layer Perceptrons and Neural Networks.vtt	4.04 KB
		6. Training a Neural Network.mp4	8.34 MB
		6. Training a Neural Network.vtt	7.7 KB
		7. Overfitting and Underfitting.mp4	4.06 MB
		7. Overfitting and Underfitting.vtt	4.35 KB
		8. Module Summary.mp4	1.79 MB
		8. Module Summary.vtt	2.28 KB
		3. Implementing Regression and Classification Using Neural Networks in scikit-learn
		1. Module Overview.mp4	1.62 MB
		1. Module Overview.vtt	1.51 KB
		2. Performing Regression Using Neural Networks.mp4	8.36 MB
		2. Performing Regression Using Neural Networks.vtt	8.56 KB
		3. Exploring and Preparing the Diet Dataset for Regressi.mp4	15.24 MB
		3. Exploring and Preparing the Diet Dataset for Regressi.vtt	11.47 KB
		4. Build and Train a Neural Network Using the MLPRegress.mp4	12.72 MB
		4. Build and Train a Neural Network Using the MLPRegress.vtt	9.92 KB
		5. Performing Classification Using Neural Networks.mp4	3.87 MB
		5. Performing Classification Using Neural Networks.vtt	3.92 KB
		6. Exploring and Preparing the Spine Dataset for Classif.mp4	9.2 MB
		6. Exploring and Preparing the Spine Dataset for Classif.vtt	6.49 KB
		7. Build and Train a Neural Network Using the MLPClassif.mp4	8.72 MB
		7. Build and Train a Neural Network Using the MLPClassif.vtt	6.91 KB
		8. Module Summary.mp4	1.81 MB
		8. Module Summary.vtt	2 KB
		4. Implementing Text and Image Classification Using Neural Networks in scikit-learn
		1. Module Overview.mp4	1.68 MB
		1. Module Overview.vtt	1.81 KB
		2. Encoding Text in Numeric Form.mp4	7.38 MB
		2. Encoding Text in Numeric Form.vtt	7.83 KB
		3. Loading and Exploring the Newsgroup Dataset.mp4	4.92 MB
		3. Loading and Exploring the Newsgroup Dataset.vtt	3.78 KB
		4. Creating Feature Vectors from Text Data Using Tf-Idf.mp4	5.65 MB
		4. Creating Feature Vectors from Text Data Using Tf-Idf.vtt	4.57 KB
		5. Building and Training a Classification Model on Text .mp4	5.9 MB
		5. Building and Training a Classification Model on Text .vtt	4.5 KB
		6. Encoding Images in Numeric Form.mp4	4.04 MB
		6. Encoding Images in Numeric Form.vtt	4.19 KB
		7. Loading and Visualizing the Lego Bricks Image Dataset.mp4	9.44 MB
		7. Loading and Visualizing the Lego Bricks Image Dataset.vtt	6.61 KB
		8. Building and Training a Classification Model on Image.mp4	7.4 MB
		8. Building and Training a Classification Model on Image.vtt	5.83 KB
		9. Module Summary.mp4	1.81 MB
		9. Module Summary.vtt	2.08 KB
		5. Implementing Dimensionality Reduction Using Restricted Boltzmann Machines in scikit-learn
		1. Module Overview.mp4	2.13 MB
		1. Module Overview.vtt	2.34 KB
		2. Restricted Boltzmann Machines for Dimensiona.mp4	9.14 MB
		2. Restricted Boltzmann Machines for Dimensiona.vtt	9.43 KB
		3. A Brief History of Restricted Boltzmann Mach.mp4	6.17 MB
		3. A Brief History of Restricted Boltzmann Mach.vtt	5.65 KB
		4. Training a Classifier on All Features of the.mp4	11.84 MB
		4. Training a Classifier on All Features of the.vtt	9.64 KB
		5. Dimensionality Reduction Using Restricted Bo.mp4	12.32 MB
		5. Dimensionality Reduction Using Restricted Bo.vtt	7.43 KB
		6. Summary and Further Study.mp4	1.66 MB
		6. Summary and Further Study.vtt	2.04 KB
		exercise.7z	91.79 MB
		playlist.m3u	3.74 KB
		~i.txt	2.06 KB
		B2. Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)
		1. Course Overview
		1. Course Overview.mp4	4.21 MB
		1. Course Overview.vtt	3.26 KB
		2. Getting Started with Feature Selection in scikit-learn
		01. Module Overview.mp4	1.36 MB
		01. Module Overview.vtt	1.47 KB
		02. Prerequisites and Course Outline.mp4	2.22 MB
		02. Prerequisites and Course Outline.vtt	2.39 KB
		03. The Curse of Dimensionality.mp4	7.98 MB
		03. The Curse of Dimensionality.vtt	8.64 KB
		04. Overfitted Models and Data Sparsity.mp4	5.27 MB
		04. Overfitted Models and Data Sparsity.vtt	5.83 KB
		05. Exploring Techniques for Reducing Dimensions.mp4	4.88 MB
		05. Exploring Techniques for Reducing Dimensions.vtt	5.35 KB
		06. Demo - Exploring the Classification Dataset.mp4	11.74 MB
		06. Demo - Exploring the Classification Dataset.vtt	9.31 KB
		07. Demo - Performing Classification with All Features.mp4	4.87 MB
		07. Demo - Performing Classification with All Features.vtt	4.2 KB
		08. Demo - Exploring the Regression Dataset.mp4	10.38 MB
		08. Demo - Exploring the Regression Dataset.vtt	7.92 KB
		09. Demo - Performing Kitchen Sink Regression Using ML and Non-ML Techniques.mp4	7.06 MB
		09. Demo - Performing Kitchen Sink Regression Using ML and Non-ML Techniques.vtt	5.22 KB
		10. Feature Selection and Dictionary Learning.mp4	7.3 MB
		10. Feature Selection and Dictionary Learning.vtt	7.35 KB
		11. Demo - Using Univariate Linear Regression Tests to Select Features.mp4	10.21 MB
		11. Demo - Using Univariate Linear Regression Tests to Select Features.vtt	8.64 KB
		12. Demo - Defining Helper Functions to Build and Train Multiple Models with D.mp4	6.65 MB
		12. Demo - Defining Helper Functions to Build and Train Multiple Models with D.vtt	5.68 KB
		13. Demo - Finding the Best Value of K.mp4	7.54 MB
		13. Demo - Finding the Best Value of K.vtt	6.17 KB
		14. Demo - Using Mutual Information to Select Features.mp4	6.16 MB
		14. Demo - Using Mutual Information to Select Features.vtt	4.78 KB
		15. Demo - Dictionary Learning to Find Sparse Representations of Data.mp4	12.06 MB
		15. Demo - Dictionary Learning to Find Sparse Representations of Data.vtt	10.41 KB
		16. Summary.mp4	1.49 MB
		16. Summary.vtt	1.65 KB
		3. Dimensionality Reduction in Linear Data
		01. Module Overview.mp4	1.56 MB
		01. Module Overview.vtt	1.54 KB
		02. The Intuition Behind Principal Components Analysis.mp4	10.01 MB
		02. The Intuition Behind Principal Components Analysis.vtt	10.26 KB
		03. Demo - Implementing Principal Component Analysis.mp4	11.31 MB
		03. Demo - Implementing Principal Component Analysis.vtt	9.74 KB
		04. Demo - Building Regression Models with Principal Components.mp4	4.96 MB
		04. Demo - Building Regression Models with Principal Components.vtt	4.38 KB
		05. Factor Analysis Using Singular Value Decomposition.mp4	3.09 MB
		05. Factor Analysis Using Singular Value Decomposition.vtt	3.21 KB
		06. Demo - Implementing Factor Analysis.mp4	13.18 MB
		06. Demo - Implementing Factor Analysis.vtt	10.13 KB
		07. Linear Discriminant Analysis for Dimensionality Reduction.mp4	4.02 MB
		07. Linear Discriminant Analysis for Dimensionality Reduction.vtt	4.29 KB
		08. Demo - Observing Class Seperation Boundaries on the Iris Dataset.mp4	9.45 MB
		08. Demo - Observing Class Seperation Boundaries on the Iris Dataset.vtt	8.11 KB
		09. Demo - Linear Discriminant Analysis for Classification.mp4	6.88 MB
		09. Demo - Linear Discriminant Analysis for Classification.vtt	5.81 KB
		10. Summary.mp4	2.2 MB
		10. Summary.vtt	2.22 KB
		4. Dimensionality Reduction in Non-linear Data
		01. Module Overview.mp4	1.02 MB
		01. Module Overview.vtt	1.13 KB
		02. The Manifold Hypothesis and Manifold Learning.mp4	9.13 MB
		02. The Manifold Hypothesis and Manifold Learning.vtt	8.84 KB
		03. Demo - Generate S-curve Manifold and Setup Helper Functions.mp4	10.64 MB
		03. Demo - Generate S-curve Manifold and Setup Helper Functions.vtt	8.22 KB
		04. Demo - Metric and Non-metric Multi Dimensional Scaling.mp4	4.81 MB
		04. Demo - Metric and Non-metric Multi Dimensional Scaling.vtt	3.65 KB
		05. Demo - Manifold Learning Using Spectral Embedding TSNE and Isomap.mp4	6.99 MB
		05. Demo - Manifold Learning Using Spectral Embedding TSNE and Isomap.vtt	5.78 KB
		06. Demo - Manifold Learning with Locally Linear Embedding.mp4	4.95 MB
		06. Demo - Manifold Learning with Locally Linear Embedding.vtt	3.15 KB
		07. Demo - Preparing Images to Apply Manifold Learning for Dimensionality Reduction.mp4	7.81 MB
		07. Demo - Preparing Images to Apply Manifold Learning for Dimensionality Reduction.vtt	6.06 KB
		08. Demo - Manifold Learning with Handwritten Digits.mp4	8.46 MB
		08. Demo - Manifold Learning with Handwritten Digits.vtt	5.83 KB
		09. Demo - Preparing the Olivetti Faces Dataset for Manifold Learning.mp4	7.2 MB
		09. Demo - Preparing the Olivetti Faces Dataset for Manifold Learning.vtt	5.29 KB
		10. Demo - Manifold Learning on Olivetti Faces Dataset.mp4	5.54 MB
		10. Demo - Manifold Learning on Olivetti Faces Dataset.vtt	4.49 KB
		11. Summary and Further Study.mp4	2.05 MB
		11. Summary and Further Study.vtt	2.61 KB
		exercise.7z	23.39 MB
		playlist.m3u	3.87 KB
		~i.txt	2.82 KB
		B3. Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)
		1. Course Overview
		1. Course Overview.mp4	3.33 MB
		1. Course Overview.vtt	2.93 KB
		2. Understanding Ensemble Learning Techniques
		01. Module Overview.mp4	1.82 MB
		01. Module Overview.vtt	1.8 KB
		02. Prerequisites and Course Outline.mp4	2.33 MB
		02. Prerequisites and Course Outline.vtt	2.81 KB
		03. A Quick Overview of Ensemble Learning.mp4	9.68 MB
		03. A Quick Overview of Ensemble Learning.vtt	10.11 KB
		04. Averaging and Boosting, Voting and Stacking.mp4	9.7 MB
		04. Averaging and Boosting, Voting and Stacking.vtt	10.38 KB
		05. Decision Trees in Ensemble Learning.mp4	5.17 MB
		05. Decision Trees in Ensemble Learning.vtt	5.25 KB
		06. Understanding Decision Trees.mp4	4.82 MB
		06. Understanding Decision Trees.vtt	5 KB
		07. Overfitted Models and Ensemble Learning.mp4	7.56 MB
		07. Overfitted Models and Ensemble Learning.vtt	8.32 KB
		08. Getting Started and Exploring the Environment.mp4	3.78 MB
		08. Getting Started and Exploring the Environment.vtt	3.32 KB
		09. Exploring the Classification Dataset.mp4	14.71 MB
		09. Exploring the Classification Dataset.vtt	10.17 KB
		10. Hard Voting.mp4	10.83 MB
		10. Hard Voting.vtt	7.68 KB
		11. Soft Voting.mp4	9 MB
		11. Soft Voting.vtt	6.2 KB
		12. Module Summary.mp4	1.93 MB
		12. Module Summary.vtt	2.13 KB
		3. Implementing Ensemble Learning Using Averaging Methods
		01. Module Overview.mp4	2.16 MB
		01. Module Overview.vtt	2.26 KB
		02. Bagging and Pasting.mp4	7.98 MB
		02. Bagging and Pasting.vtt	7.91 KB
		03. Random Subspaces and Random Patches.mp4	4.12 MB
		03. Random Subspaces and Random Patches.vtt	3.81 KB
		04. Extra Trees.mp4	4.64 MB
		04. Extra Trees.vtt	4.61 KB
		05. Averaging vs. Boosting.mp4	3.3 MB
		05. Averaging vs. Boosting.vtt	3.3 KB
		06. Exploring the Regression Dataset.mp4	9.72 MB
		06. Exploring the Regression Dataset.vtt	5.97 KB
		07. Regression Using Bagging and Pasting.mp4	10.67 MB
		07. Regression Using Bagging and Pasting.vtt	7.18 KB
		08. Regression Using Random Subspaces.mp4	3.27 MB
		08. Regression Using Random Subspaces.vtt	2.24 KB
		09. Classification Using Bagging and Pasting.mp4	7.05 MB
		09. Classification Using Bagging and Pasting.vtt	5.24 KB
		10. Classification Using Random Patches.mp4	3.56 MB
		10. Classification Using Random Patches.vtt	2.26 KB
		11. Regression Using Random Forest.mp4	10.64 MB
		11. Regression Using Random Forest.vtt	7.42 KB
		12. Regression Using Extra Trees.mp4	3.84 MB
		12. Regression Using Extra Trees.vtt	2.41 KB
		13. Classification Using Random Forest and Extra Trees.mp4	7.25 MB
		13. Classification Using Random Forest and Extra Trees.vtt	5.02 KB
		14. Module Summary.mp4	1.8 MB
		14. Module Summary.vtt	2.03 KB
		4. Implementing Ensemble Learning Using Boosting Methods
		1. Module Overview.mp4	2.04 MB
		1. Module Overview.vtt	2.15 KB
		2. Adaptive Boosting (AdaBoost).mp4	4.71 MB
		2. Adaptive Boosting (AdaBoost).vtt	4.71 KB
		3. Regression Using AdaBoost.mp4	12.38 MB
		3. Regression Using AdaBoost.vtt	7.59 KB
		4. Classification Using AdaBoost.mp4	8.93 MB
		4. Classification Using AdaBoost.vtt	4.96 KB
		5. Gradient Boosting.mp4	4.08 MB
		5. Gradient Boosting.vtt	4.15 KB
		6. Regression Using Gradient Boosting.mp4	11.03 MB
		6. Regression Using Gradient Boosting.vtt	8.4 KB
		7. Hyperparameter Tuning of the Gradient Boosting Regressor Using Grid Search.mp4	8.38 MB
		7. Hyperparameter Tuning of the Gradient Boosting Regressor Using Grid Search.vtt	6.28 KB
		8. Hyperparameter Tuning Using Warm Start and Early Stopping.mp4	10.07 MB
		8. Hyperparameter Tuning Using Warm Start and Early Stopping.vtt	6.43 KB
		9. Module Summary.mp4	1.61 MB
		9. Module Summary.vtt	1.76 KB
		5. Implementing Ensemble Learning Using Model Stacking
		1. Module Overview.mp4	1.36 MB
		1. Module Overview.vtt	1.54 KB
		2. Stacking.mp4	4.9 MB
		2. Stacking.vtt	5.19 KB
		3. Classification Using a Stacking Ensemble.mp4	12.32 MB
		3. Classification Using a Stacking Ensemble.vtt	9.16 KB
		4. Summary and Further Study.mp4	2.09 MB
		4. Summary and Further Study.vtt	2.63 KB
		exercise.7z	1.99 MB
		playlist.m3u	3.52 KB
		~i.txt	1.81 KB
		C1. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)
		1. Course Overview
		1. Course Overview.mp4	3.24 MB
		1. Course Overview.vtt	2.71 KB
		2. Preparing Numeric Data for Machine Learning
		01. Version Check.mp4	581.33 KB
		01. Version Check.vtt	7 B
		02. Module Overview.mp4	1.96 MB
		02. Module Overview.vtt	2.04 KB
		03. Prerequisites and Course Outline.mp4	2.28 MB
		03. Prerequisites and Course Outline.vtt	2.7 KB
		04. Scaling and Standardization.mp4	7.04 MB
		04. Scaling and Standardization.vtt	7.61 KB
		05. Normalization.mp4	4.27 MB
		05. Normalization.vtt	3.99 KB
		06. Transforming Data to Gaussian Distributions.mp4	2.48 MB
		06. Transforming Data to Gaussian Distributions.vtt	2.38 KB
		07. Calculating and Visualizing Summary Statistics.mp4	11.4 MB
		07. Calculating and Visualizing Summary Statistics.vtt	7.67 KB
		08. Using the Standard Scaler for Standardizing Numeric Features.mp4	12.06 MB
		08. Using the Standard Scaler for Standardizing Numeric Features.vtt	9.23 KB
		09. Using the Robust Scaler to Scale Numeric Features.mp4	7.21 MB
		09. Using the Robust Scaler to Scale Numeric Features.vtt	5.83 KB
		10. Normalization and Cosine Similarity.mp4	12.67 MB
		10. Normalization and Cosine Similarity.vtt	9.19 KB
		11. Transforming Bimodally Distributed Data to a Normal Distribution Using a Quantile Tra.mp4	9.89 MB
		11. Transforming Bimodally Distributed Data to a Normal Distribution Using a Quantile Tra.vtt	7.23 KB
		12. Reducing Dimensionality Using Factor Analysis.mp4	14.16 MB
		12. Reducing Dimensionality Using Factor Analysis.vtt	9.1 KB
		13. Module Summary.mp4	1.75 MB
		13. Module Summary.vtt	2.01 KB
		3. Understanding and Implementing Novelty and Outlier Detection
		01. Module Overview.mp4	1.75 MB
		01. Module Overview.vtt	1.79 KB
		02. Outliers and Novelties.mp4	4.74 MB
		02. Outliers and Novelties.vtt	5.36 KB
		03. Detecting and Coping with Outlier Data.mp4	6.58 MB
		03. Detecting and Coping with Outlier Data.vtt	6.72 KB
		04. Local Outlier Factor.mp4	5.31 MB
		04. Local Outlier Factor.vtt	4.89 KB
		05. Elliptic Envelope.mp4	4.78 MB
		05. Elliptic Envelope.vtt	4.76 KB
		06. Isolation Forest.mp4	5.88 MB
		06. Isolation Forest.vtt	5.8 KB
		07. Outlier Detection Using Local Outlier Factor.mp4	15.47 MB
		07. Outlier Detection Using Local Outlier Factor.vtt	10.07 KB
		08. Outlier Detection Using Isolation Forest.mp4	11.86 MB
		08. Outlier Detection Using Isolation Forest.vtt	7.31 KB
		09. Outlier Detection Using Elliptic Envelope.mp4	6.14 MB
		09. Outlier Detection Using Elliptic Envelope.vtt	3.85 KB
		10. Novelty Detection Using Local Outlier Factor.mp4	11.93 MB
		10. Novelty Detection Using Local Outlier Factor.vtt	8.06 KB
		11. Using the Predict Score Samples and Decision Function.mp4	7.02 MB
		11. Using the Predict Score Samples and Decision Function.vtt	3.99 KB
		12. Outlier Detection Using the Head Brain Dataset.mp4	9.59 MB
		12. Outlier Detection Using the Head Brain Dataset.vtt	5.58 KB
		13. Module Summary.mp4	1.73 MB
		13. Module Summary.vtt	1.91 KB
		4. Preparing Text Data for Machine Learning
		01. Module Overview.mp4	1.66 MB
		01. Module Overview.vtt	1.6 KB
		02. Representing Text Data in Numeric Form.mp4	7.6 MB
		02. Representing Text Data in Numeric Form.vtt	7.83 KB
		03. Bag-of-words and Bag-of-n-grams Models.mp4	3.84 MB
		03. Bag-of-words and Bag-of-n-grams Models.vtt	3.96 KB
		04. Vectorize Text Using the Bag-of-words Model.mp4	12.03 MB
		04. Vectorize Text Using the Bag-of-words Model.vtt	7.24 KB
		05. Vectorize Text Using the Bag-of-n-grams Model.mp4	8.32 MB
		05. Vectorize Text Using the Bag-of-n-grams Model.vtt	4.43 KB
		06. Vectorize Text Using Tf-Idf Scores.mp4	6.65 MB
		06. Vectorize Text Using Tf-Idf Scores.vtt	3.64 KB
		07. Hashing for Dimensionality Reduction.mp4	4.94 MB
		07. Hashing for Dimensionality Reduction.vtt	4.56 KB
		08. Reducing Dimensions Using the Hashing Vectorizer.mp4	6.64 MB
		08. Reducing Dimensions Using the Hashing Vectorizer.vtt	4.34 KB
		09. Performing Feature Extraction on a Python Dictionary.mp4	4.65 MB
		09. Performing Feature Extraction on a Python Dictionary.vtt	3.24 KB
		10. Module Summary.mp4	1.95 MB
		10. Module Summary.vtt	1.91 KB
		5. Preparing Image Data for Machine Learning
		1. Module Overview.mp4	1.65 MB
		1. Module Overview.vtt	1.53 KB
		2. Representing Images as Matrices.mp4	4.06 MB
		2. Representing Images as Matrices.vtt	4.14 KB
		3. Feature Extraction from Images.mp4	8.32 MB
		3. Feature Extraction from Images.vtt	8.59 KB
		4. Extracting Patches from Image Data.mp4	11.27 MB
		4. Extracting Patches from Image Data.vtt	6.24 KB
		5. Using Dictionary Learning to Denoise and Reconstruct Images.mp4	16.4 MB
		5. Using Dictionary Learning to Denoise and Reconstruct Images.vtt	9.51 KB
		6. Clustering Image Data Using a Pixel Connectivity Graph.mp4	16.08 MB
		6. Clustering Image Data Using a Pixel Connectivity Graph.vtt	9.9 KB
		7. Clustering Images Using a Gradient Connectivity Graph.mp4	14.19 MB
		7. Clustering Images Using a Gradient Connectivity Graph.vtt	8.72 KB
		8. Module Summary.mp4	1.89 MB
		8. Module Summary.vtt	1.83 KB
		6. Working with Specialized Datasets
		1. Module Overview.mp4	1.96 MB
		1. Module Overview.vtt	2.18 KB
		2. Internal, Artificial, and External Datasets in Scikit Learn.mp4	3.71 MB
		2. Internal, Artificial, and External Datasets in Scikit Learn.vtt	4.3 KB
		3. Exploring Internal Datasets.mp4	18.67 MB
		3. Exploring Internal Datasets.vtt	10.04 KB
		4. Creating Artificial Datasets for Regression, Classification, Clustering, and Dimensionality Reduc.mp4	17.28 MB
		4. Creating Artificial Datasets for Regression, Classification, Clustering, and Dimensionality Reduc.vtt	11.71 KB
		5. Generating Manifold Data.mp4	16.41 MB
		5. Generating Manifold Data.vtt	11.01 KB
		6. Module Summary.mp4	1.64 MB
		6. Module Summary.vtt	1.68 KB
		7. Performing Kernel Approximations
		1. Module Overview.mp4	1.62 MB
		1. Module Overview.vtt	1.64 KB
		2. Support Vector Classifiers and the Kernel Trick.mp4	5.84 MB
		2. Support Vector Classifiers and the Kernel Trick.vtt	6.19 KB
		3. Kernel Approximations.mp4	10.8 MB
		3. Kernel Approximations.vtt	10.06 KB
		4. Preparing Image Data.mp4	9.72 MB
		4. Preparing Image Data.vtt	6.86 KB
		5. Comparing Classifiers Trained Using Implicit and Explict Features.mp4	17.61 MB
		5. Comparing Classifiers Trained Using Implicit and Explict Features.vtt	10.74 KB
		6. Comparing Accuracy and Runtime for Different Sample Sizes.mp4	14.63 MB
		6. Comparing Accuracy and Runtime for Different Sample Sizes.vtt	9.36 KB
		7. Summary and Further Study.mp4	2.78 MB
		7. Summary and Further Study.vtt	2.94 KB
		exercise.7z	45.32 MB
		playlist.m3u	5.16 KB
		~i.txt	1.89 KB
		C2. Scaling scikit-learn Solutions (Janani Ravi, 2019)
		1. Course Overview
		1. Course Overview.mp4	3.52 MB
		1. Course Overview.vtt	2.78 KB
		2. Understanding Strategies for Computational Scaling
		01. Version Check.mp4	541.74 KB
		01. Version Check.vtt	7 B
		02. Module Overview.mp4	1.92 MB
		02. Module Overview.vtt	2.1 KB
		03. Prerequisites and Course Outline.mp4	2.09 MB
		03. Prerequisites and Course Outline.vtt	2.4 KB
		04. Dimensions of Scaling.mp4	2.78 MB
		04. Dimensions of Scaling.vtt	3.17 KB
		05. Measuring Performance in Scaling.mp4	9.45 MB
		05. Measuring Performance in Scaling.vtt	9.8 KB
		06. Influence of Number of Features.mp4	7.2 MB
		06. Influence of Number of Features.vtt	8.3 KB
		07. Influence of Feature Extraction Techniques.mp4	6.41 MB
		07. Influence of Feature Extraction Techniques.vtt	7.43 KB
		08. Influence of Feature Representation.mp4	4.39 MB
		08. Influence of Feature Representation.vtt	4.26 KB
		09. Demo - Helper Functions to Generate Datasets and Train Models.mp4	9.5 MB
		09. Demo - Helper Functions to Generate Datasets and Train Models.vtt	7.78 KB
		10. Demo - Measuring Training Latencies for Different Models.mp4	8.8 MB
		10. Demo - Measuring Training Latencies for Different Models.vtt	6.21 KB
		11. Module Summary.mp4	2.02 MB
		11. Module Summary.vtt	2.3 KB
		3. Observing the Factors Affecting Prediction Latency
		01. Module Overview.mp4	2.08 MB
		01. Module Overview.vtt	2.16 KB
		02. Demo - Measuring Bulk and Atomic Prediction Latencies for Different Models.mp4	14.86 MB
		02. Demo - Measuring Bulk and Atomic Prediction Latencies for Different Models.vtt	11.67 KB
		03. Demo - Influence of Number of Features on Bulk Prediction Latency.mp4	10.58 MB
		03. Demo - Influence of Number of Features on Bulk Prediction Latency.vtt	8.53 KB
		04. Optimizations to Improve Prediction Latency.mp4	10.48 MB
		04. Optimizations to Improve Prediction Latency.vtt	11.32 KB
		05. Optimizations to Improve Prediction Throughput.mp4	2.91 MB
		05. Optimizations to Improve Prediction Throughput.vtt	3.09 KB
		06. Demo - Observing the Influence of Model Complexity.mp4	17.35 MB
		06. Demo - Observing the Influence of Model Complexity.vtt	13.33 KB
		07. Demo - Using Optimized Libraries and Reducing Validation Overhead.mp4	6.12 MB
		07. Demo - Using Optimized Libraries and Reducing Validation Overhead.vtt	4.21 KB
		08. Demo - Training Models Using Dense and Sparse Input Representation.mp4	12.46 MB
		08. Demo - Training Models Using Dense and Sparse Input Representation.vtt	9.59 KB
		09. Demo - Prediction with Sparse Data and Memory Profiling.mp4	11.84 MB
		09. Demo - Prediction with Sparse Data and Memory Profiling.vtt	9.44 KB
		10. Module Summary.mp4	2.02 MB
		10. Module Summary.vtt	2.07 KB
		4. Implementing Scaling of Instances Using Out-of-core Learning
		1. Module Overview.mp4	1.8 MB
		1. Module Overview.vtt	1.95 KB
		2. Streaming Data.mp4	5.46 MB
		2. Streaming Data.vtt	5.65 KB
		3. Incremental Learning for Large Datasets.mp4	11.33 MB
		3. Incremental Learning for Large Datasets.vtt	11.83 KB
		4. Demo - Preparing Text Data for out of Core Learning.mp4	11.9 MB
		4. Demo - Preparing Text Data for out of Core Learning.vtt	9.36 KB
		5. Demo - Using Partial Fit to Perform out of Core Learning.mp4	9.32 MB
		5. Demo - Using Partial Fit to Perform out of Core Learning.vtt	6.94 KB
		6. Demo - Visualizing Latencies and Accuracies.mp4	9.34 MB
		6. Demo - Visualizing Latencies and Accuracies.vtt	7.61 KB
		7. Demo - Using the Passive Aggressive, Perceptron, and BernoulliNB Classifiers.mp4	8.05 MB
		7. Demo - Using the Passive Aggressive, Perceptron, and BernoulliNB Classifiers.vtt	6.24 KB
		8. Module Summary.mp4	1.9 MB
		8. Module Summary.vtt	2.21 KB
		5. Implementing Multicore Parallelism in scikit-learn
		01. Module Overview.mp4	1.61 MB
		01. Module Overview.vtt	1.68 KB
		02. Parallelizing Computation Using Joblib.mp4	6.5 MB
		02. Parallelizing Computation Using Joblib.vtt	6.75 KB
		03. Demo - Introducing Joblib.mp4	7.87 MB
		03. Demo - Introducing Joblib.vtt	6.23 KB
		04. Demo - Running Concurrent Workers Using Joblib.mp4	8.9 MB
		04. Demo - Running Concurrent Workers Using Joblib.vtt	6.64 KB
		05. Demo - Cross Validation Using Concurrent Workers.mp4	6.84 MB
		05. Demo - Cross Validation Using Concurrent Workers.vtt	5.25 KB
		06. Demo - Integrating Joblib with Dask ML.mp4	7.37 MB
		06. Demo - Integrating Joblib with Dask ML.vtt	3.84 KB
		07. Demo - Grid Search with Concurrent Workers.mp4	5.92 MB
		07. Demo - Grid Search with Concurrent Workers.vtt	4.7 KB
		08. Demo - Preparing Data for Multi-label Classification.mp4	15.98 MB
		08. Demo - Preparing Data for Multi-label Classification.vtt	12.55 KB
		09. Demo - Performing Multi-label Classification.mp4	7.46 MB
		09. Demo - Performing Multi-label Classification.vtt	5.76 KB
		10. Module Summary.mp4	1.52 MB
		10. Module Summary.vtt	1.66 KB
		6. Autoscaling of scikit-learn with Apache Spark
		1. Module Overview.mp4	2.07 MB
		1. Module Overview.vtt	2.07 KB
		2. Integrating Apache Spark and scikit-learn.mp4	7.25 MB
		2. Integrating Apache Spark and scikit-learn.vtt	6.82 KB
		3. Demo - Working with Spark Using spark-sklearn.mp4	13.59 MB
		3. Demo - Working with Spark Using spark-sklearn.vtt	10.14 KB
		4. Demo - Working with Spark Using scikit-spark.mp4	8.57 MB
		4. Demo - Working with Spark Using scikit-spark.vtt	6.41 KB
		5. Summary and Further Study.mp4	2.4 MB
		5. Summary and Further Study.vtt	2.79 KB
		exercise.7z	134.17 MB
		playlist.m3u	4.43 KB
		~i.txt	1.93 KB
		C3. Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)
		1. Course Overview
		1. Course Overview.mp4	2.82 MB
		1. Course Overview.vtt	2.38 KB
		2. What Is Model Evaluation and Selection
		1. Model Evaluation and Selection.mp4	20.06 MB
		1. Model Evaluation and Selection.vtt	12.65 KB
		3. Evaluation Methods for Classification Models
		1. Introduction.mp4	1.14 MB
		1. Introduction.vtt	1.17 KB
		2. Classification Model Refresher.mp4	1.62 MB
		2. Classification Model Refresher.vtt	1.79 KB
		3. Confusion Matrix.mp4	2.92 MB
		3. Confusion Matrix.vtt	3.06 KB
		4. Accuracy, Precision, Recall, and F1 Score.mp4	5.38 MB
		4. Accuracy, Precision, Recall, and F1 Score.vtt	4.88 KB
		5. Choosing the Right Metric.mp4	7.16 MB
		5. Choosing the Right Metric.vtt	6.46 KB
		6. ROC Curves and AUC.mp4	6.17 MB
		6. ROC Curves and AUC.vtt	5.15 KB
		7. Demo.mp4	20.16 MB
		7. Demo.vtt	11.55 KB
		4. Evaluation Methods for Regression Models
		1. Introduction.mp4	885 KB
		1. Introduction.vtt	881 B
		2. Regression Model Refresher.mp4	1.98 MB
		2. Regression Model Refresher.vtt	2.3 KB
		3. Mean Square Error and Root Mean Square Error.mp4	4.4 MB
		3. Mean Square Error and Root Mean Square Error.vtt	5.33 KB
		4. Mean Absolute Error.mp4	1.49 MB
		4. Mean Absolute Error.vtt	1.59 KB
		5. R-squared and Adjusted R-squared.mp4	7.54 MB
		5. R-squared and Adjusted R-squared.vtt	8.28 KB
		6. Choosing the Right Metric.mp4	3.08 MB
		6. Choosing the Right Metric.vtt	3.56 KB
		7. Demo.mp4	8.78 MB
		7. Demo.vtt	6.33 KB
		8. Summary.mp4	799.58 KB
		8. Summary.vtt	1.05 KB
		5. Model Selection Techniques
		1. Model Selection Techniques.mp4	26.55 MB
		1. Model Selection Techniques.vtt	23.98 KB
		6. Putting It All Together
		1. Revisiting the Data Scientists Dilemma.mp4	2.81 MB
		1. Revisiting the Data Scientists Dilemma.vtt	3.11 KB
		2. Model Evaluation Methods.mp4	2.67 MB
		2. Model Evaluation Methods.vtt	3.02 KB
		3. Model Selection Techniques.mp4	2.27 MB
		3. Model Selection Techniques.vtt	2.55 KB
		4. Demo - Using the Patient Dataset.mp4	9.9 MB
		4. Demo - Using the Patient Dataset.vtt	5.81 KB
		exercise.7z	1.01 MB
		playlist.m3u	1.53 KB
		~i.txt	1.58 KB
		scr.png	206.14 KB
		~i.txt	822 B

Description

Обучающие видео » Компьютерные видеоуроки и обучающие интерактивные DVD » Программирование (видеоуроки)

Building Machine Learning Solutions with scikit-learn | Path

Год выпуска: 2019
Производитель: Pluralsight
Сайт производителя://app.pluralsight.com/paths/skill/building-machine-learning-solutions-with-scikit-learn
Автор: Janani Ravi / Chetan Prabhu
Продолжительность: 24h 30m
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание: This skill teaches learners how to build machine learning solutions using Python and the pervasive scikit-learn package, including application of classifcation, regression, and clustering.

What you will learn: Design and implement common machine learning solutions using scikit-learn
Evaluation and validation of scikit-learn machine learning models

Prerequisites: Statistics and Probability
Data Analytics Literacy
Python programming

Related Topics: Deep Learning Literacy
Machine Learning Literacy
Other Machine Learning Libraries, such as PyTorch
Computer Vision
Image Recognition
[spoiler="Содержание"]
Beginner
Experience the machine learning workflow as implemented in scikit-learn, and use that workflow to build simple classification, regression, and clustering models. Building Your First scikit-learn Solution (Janani Ravi, 2019)
Building Classification Models with scikit-learn (Janani Ravi, 2019)
Building Regression Models with scikit-learn (Janani Ravi, 2019)
Building Clustering Models with scikit-learn (Janani Ravi, 2019)

Intermediate
Build sophisticated neural network models, apply dimension reduction techniques, and combine model approaches. Building Neural Networks with scikit-learn (Janani Ravi, 2019)
Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)
Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)

Advanced
Select the appropriate model for your business problem and data, and evaluate the effectiveness of that model. Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)
Scaling scikit-learn Solutions (Janani Ravi, 2019)
Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)
[/spoiler]
Файлы примеров: присутствуют
Субтитры: присутствуют
Формат видео: MP4
Видео: H.264/AVC, 1280x720, 16:9, 30fps, 100 kb/s
Аудио: AAC 48000Hz 2.0 chn 96 kbit/s
[spoiler="Скриншоты"]
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[/spoiler]

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Building Machine Learning Solutions with scikit-learn | Path [2019, ENG]

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