Download Free Download : Pluralsight - Building Machine Learning Solutions with scikit-learn
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:1.91 GB
Files Included :
1 Course Overview.mp4 (3.09 MB)
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01 Module Overview.mp4 (1.53 MB)
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02 Prerequisites and Course Outline.mp4 (1.22 MB)
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03 Introducing Machine Learning.mp4 (4.8 MB)
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04 Learning from Data - Training and Prediction.mp4 (7.88 MB)
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05 Traditional and Representation ML Models.mp4 (11.46 MB)
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06 The Niche of scikit-learn in ML.mp4 (7.94 MB)
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07 Exploring scikit-learn Libraries.mp4 (35.84 MB)
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08 Supervised and Unsupervised Learning.mp4 (9.21 MB)
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09 Installing scikit-learn Libraries.mp4 (5.55 MB)
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10 Summary.mp4 (1.86 MB)
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01 Module Overview.mp4 (1.41 MB)
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02 The Machine Learning Workflow.mp4 (6.66 MB)
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05 Choosing the Right Estimator - Clustering.mp4 (3.22 MB)
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11 Summary.mp4 (1.97 MB)
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1 Module Overview.mp4 (1.28 MB)
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2 Understanding Linear Regression.mp4 (5.99 MB)
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3 Data Preparation for Machine Learning.mp4 (13.28 MB)
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5 Understanding Logistic Regression.mp4 (9.58 MB)
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7 Summary and Further Study.mp4 (1.8 MB)
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1 Course Overview.mp4 (3.07 MB)
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1 Module Overview.mp4 (1.84 MB)
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2 Prerequisites and Course Outline.mp4 (1.99 MB)
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4 Logistic Regression Intuition.mp4 (8.34 MB)
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5 Cross Entropy Intuition.mp4 (3.43 MB)
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6 Accuracy, Precision, and Recall.mp4 (8.75 MB)
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8 Types of Classification.mp4 (5.92 MB)
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9 Module Summary.mp4 (1.58 MB)
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1 Module Overview.mp4 (1.55 MB)
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2 Installing and Setting up scikit-learn.mp4 (5.39 MB)
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3 Exploring the Titanic Dataset.mp4 (14.48 MB)
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4 Visualizing Relationships in the Data.mp4 (9.09 MB)
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5 Preprocessing the Data.mp4 (9.09 MB)
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6 Training a Logistic Regression Binary Classifier.mp4 (9.81 MB)
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9 Module Summary.mp4 (1.89 MB)
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01 Module Overview.mp4 (1.91 MB)
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02 Choosing Classification Algorithms.mp4 (3.37 MB)
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06 Stochastic Gradient Descent.mp4 (3.87 MB)
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08 Support Vector Machines.mp4 (11.49 MB)
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09 Implementing Support Vector Classification.mp4 (5.62 MB)
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10 Nearest Neighbors.mp4 (5.03 MB)
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12 Decision Trees.mp4 (4.54 MB)
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13 Implementing Decision Tree Classification.mp4 (5.54 MB)
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14 Naive Bayes.mp4 (5.9 MB)
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15 Implementing Naive Bayes Classification.mp4 (3.44 MB)
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16 Module Summary.mp4 (2.22 MB)
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1 Module Overview.mp4 (1.41 MB)
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2 Hyperparameter Tuning.mp4 (5.64 MB)
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5 Module Summary.mp4 (1.36 MB)
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1 Module Overview.mp4 (1.48 MB)
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2 Representing Images as Matrices.mp4 (4.48 MB)
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3 Exploring the Fashion MNIST Dataset.mp4 (12.03 MB)
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5 Summary and Further Study.mp4 (1.74 MB)
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1 Course Overview.mp4 (3.63 MB)
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01 Module Overview.mp4 (1.79 MB)
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02 Prerequisites and Course Outline.mp4 (1.98 MB)
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04 Minimizing Least Square Error.mp4 (6.22 MB)
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05 Installing and Setting up scikit-learn.mp4 (4.64 MB)
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06 Exploring the Automobile Mpg Dataset.mp4 (14.51 MB)
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09 R-squared and Adjusted R-squared.mp4 (2.21 MB)
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10 Regression with Categorical Variables.mp4 (5.71 MB)
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11 Module Summary.mp4 (1.61 MB)
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1 Module Overview.mp4 (1.51 MB)
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2 Simple Linear Regression.mp4 (16.22 MB)
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3 Linear Regression with Multiple Features.mp4 (13.14 MB)
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4 Standardizing Numeric Data.mp4 (10.02 MB)
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5 Label Encoding and One-hot Encoding Categorical Data.mp4 (10.95 MB)
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6 Linear Regression and the Dummy Trap.mp4 (12.13 MB)
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7 Module Summary.mp4 (1.52 MB)
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01 Module Overview.mp4 (1.59 MB)
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02 Overview of Regression Models in scikit-learn.mp4 (3.34 MB)
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03 Overfitting and Regularization.mp4 (6.13 MB)
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04 Lasso, Ridge and Elastic Net Regression.mp4 (7.43 MB)
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07 Lasso Regression.mp4 (6.43 MB)
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08 Ridge Regression.mp4 (4.11 MB)
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09 Elastic Net Regression.mp4 (13.86 MB)
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10 Module Summary.mp4 (1.82 MB)
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01 Module Overview.mp4 (1.96 MB)
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02 Choosing Regression Algorithms.mp4 (4.15 MB)
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03 Support Vector Regression.mp4 (8.64 MB)
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04 Implementing Support Vector Regression.mp4 (6.06 MB)
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05 Nearest Neighbors Regression.mp4 (6.36 MB)
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06 Implementing K-nearest-neighbors Regression.mp4 (4.42 MB)
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07 Stochastic Gradient Descent Regression.mp4 (4.32 MB)
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08 Implementing Stochastic Gradient Descent Regression.mp4 (4.51 MB)
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09 Decision Tree Regression.mp4 (6.35 MB)
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10 Implementing Decision Tree Regression.mp4 (3.1 MB)
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11 Least Angle Regression.mp4 (5.23 MB)
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12 Implementing Least Angle Regression.mp4 (2.39 MB)
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13 Regression with Polynomial Relationships.mp4 (2.5 MB)
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14 Module Summary.mp4 (2.08 MB)
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1 Module Overview.mp4 (1.49 MB)
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2 Hyperparameter Tuning.mp4 (5.27 MB)
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4 Tuning Different Regression Models Using Grid Search.mp4 (9.59 MB)
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5 Summary and Further Study.mp4 (1.44 MB)
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1 Course Overview.mp4 (2.68 MB)
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01 Module Overview.mp4 (1.15 MB)
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02 Prerequisites and Course Outline.mp4 (1.8 MB)
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03 Supervised and Unsupervised Learning.mp4 (6.99 MB)
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04 Clustering Objectives and Use Cases.mp4 (13.54 MB)
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05 K-means Clustering.mp4 (5.99 MB)
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06 Evaluating Clustering Models.mp4 (8.14 MB)
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08 Performing K-means Clustering.mp4 (11.14 MB)
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09 Evaluating K-means Clustering.mp4 (18.01 MB)
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10 Exploring the Iris Dataset.mp4 (6.66 MB)
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11 Performing K-means Clustering and Evaluation.mp4 (11.25 MB)
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01 Module Overview.mp4 (1.53 MB)
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02 Categories of Clustering Algorithms.mp4 (5.45 MB)
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03 Setting up Helper Functions to Perform Clustering.mp4 (5.79 MB)
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04 Choosing Clustering Algorithms.mp4 (9.69 MB)
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05 Hierarchical Clustering.mp4 (8.1 MB)
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06 Agglomerative Clustering.mp4 (7.18 MB)
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07 DBSCAN Clustering.mp4 (7.63 MB)
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08 Mean-shift Clustering.mp4 (10.24 MB)
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09 BIRCH Clustering.mp4 (4.85 MB)
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10 Affinilty Propagation Clustering.mp4 (6.41 MB)
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11 Mini-batch K-means Clustering.mp4 (4.46 MB)
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12 Spectral Clustering Using a Precomputed Matrix.mp4 (11.98 MB)
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1 Module Overview.mp4 (860.16 KB)
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2 Understanding the Silhouette Score.mp4 (4.05 MB)
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3 K-means Number of Clusters - The Elbow Method.mp4 (5.61 MB)
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4 K-means Number of Clusters - The Silhouette Method.mp4 (5.02 MB)
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5 Seeds and Distance Measures.mp4 (2.23 MB)
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6 Hyperparameter Tuning - K-means Clustering.mp4 (10.39 MB)
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7 Hyperparameter Tuning - DBSCAN Clustering.mp4 (11.35 MB)
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8 Hyperparameter Tuning - Mean-shift Clustering.mp4 (2.68 MB)
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1 Module Overview.mp4 (1.06 MB)
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2 Images as Matrices.mp4 (4.22 MB)
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3 Exploring the MNIST Handwritten Digits Dataset.mp4 (5.43 MB)
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4 Clustering Image Data.mp4 (8.8 MB)
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5 Summary and Further Study.mp4 (1.65 MB)
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1 Course Overview.mp4 (3.31 MB)
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1 Module Overview.mp4 (1.55 MB)
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2 Prerequisites and Course Outline.mp4 (2.18 MB)
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3 Support for Neural Networks in scikit-learn.mp4 (7.65 MB)
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4 Perceptrons and Neurons.mp4 (10.7 MB)
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5 Multi-layer Perceptrons and Neural Networks.mp4 (4.91 MB)
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6 Training a Neural Network.mp4 (8.34 MB)
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7 Overfitting and Underfitting.mp4 (4.06 MB)
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8 Module Summary.mp4 (1.79 MB)
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1 Module Overview.mp4 (1.62 MB)
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8 Module Summary.mp4 (1.81 MB)
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1 Module Overview.mp4 (1.68 MB)
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9 Module Summary.mp4 (1.81 MB)
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1 Course Overview.mp4 (4.21 MB)
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01 Module Overview.mp4 (1.36 MB)
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02 Prerequisites and Course Outline.mp4 (2.22 MB)
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03 The Curse of Dimensionality.mp4 (7.98 MB)
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04 Overfitted Models and Data Sparsity.mp4 (5.27 MB)
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05 Exploring Techniques for Reducing Dimensions.mp4 (4.88 MB)
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06 Demo - Exploring the Classification Dataset.mp4 (11.74 MB)
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08 Demo - Exploring the Regression Dataset.mp4 (10.38 MB)
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10 Feature Selection and Dictionary Learning.mp4 (7.3 MB)
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13 Demo - Finding the Best Value of K.mp4 (7.54 MB)
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16 Summary.mp4 (1.49 MB)
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01 Module Overview.mp4 (1.56 MB)
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02 The Intuition Behind Principal Components Analysis.mp4 (10.01 MB)
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03 Demo - Implementing Principal Component Analysis.mp4 (11.31 MB)
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04 Demo - Building Regression Models with Principal Components.mp4 (4.96 MB)
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05 Factor Analysis Using Singular Value Decomposition.mp4 (3.09 MB)
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06 Demo - Implementing Factor Analysis.mp4 (13.18 MB)
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07 Linear Discriminant Analysis for Dimensionality Reduction.mp4 (4.02 MB)
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09 Demo - Linear Discriminant Analysis for Classification.mp4 (6.88 MB)
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10 Summary.mp4 (2.2 MB)
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01 Module Overview.mp4 (1.02 MB)
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02 The Manifold Hypothesis and Manifold Learning.mp4 (9.13 MB)
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04 Demo - Metric and Non-metric Multi Dimensional Scaling.mp4 (4.81 MB)
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06 Demo - Manifold Learning with Locally Linear Embedding.mp4 (4.95 MB)
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08 Demo - Manifold Learning with Handwritten Digits.mp4 (8.46 MB)
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10 Demo - Manifold Learning on Olivetti Faces Dataset.mp4 (5.54 MB)
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11 Summary and Further Study.mp4 (2.05 MB)
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1 Course Overview.mp4 (3.33 MB)
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01 Module Overview.mp4 (1.82 MB)
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02 Prerequisites and Course Outline.mp4 (2.33 MB)
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03 A Quick Overview of Ensemble Learning.mp4 (9.68 MB)
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04 Averaging and Boosting, Voting and Stacking.mp4 (9.7 MB)
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05 Decision Trees in Ensemble Learning.mp4 (5.17 MB)
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06 Understanding Decision Trees.mp4 (4.82 MB)
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07 Overfitted Models and Ensemble Learning.mp4 (7.56 MB)
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08 Getting Started and Exploring the Environment.mp4 (3.78 MB)
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09 Exploring the Classification Dataset.mp4 (14.71 MB)
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10 Hard Voting.mp4 (10.83 MB)
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11 Soft Voting.mp4 (9 MB)
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12 Module Summary.mp4 (1.93 MB)
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01 Module Overview.mp4 (2.16 MB)
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02 Bagging and Pasting.mp4 (7.98 MB)
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03 Random Subspaces and Random Patches.mp4 (4.12 MB)
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04 Extra Trees.mp4 (4.64 MB)
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05 Averaging vs Boosting.mp4 (3.3 MB)
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06 Exploring the Regression Dataset.mp4 (9.72 MB)
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07 Regression Using Bagging and Pasting.mp4 (10.67 MB)
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08 Regression Using Random Subspaces.mp4 (3.27 MB)
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09 Classification Using Bagging and Pasting.mp4 (7.05 MB)
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10 Classification Using Random Patches.mp4 (3.56 MB)
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11 Regression Using Random Forest.mp4 (10.64 MB)
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12 Regression Using Extra Trees.mp4 (3.84 MB)
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14 Module Summary.mp4 (1.8 MB)
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1 Module Overview.mp4 (2.04 MB)
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2 Adaptive Boosting (AdaBoost).mp4 (4.71 MB)
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3 Regression Using AdaBoost.mp4 (12.38 MB)
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4 Classification Using AdaBoost.mp4 (8.93 MB)
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5 Gradient Boosting.mp4 (4.08 MB)
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6 Regression Using Gradient Boosting.mp4 (11.03 MB)
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9 Module Summary.mp4 (1.61 MB)
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1 Module Overview.mp4 (1.36 MB)
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2 Stacking.mp4 (4.9 MB)
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3 Classification Using a Stacking Ensemble.mp4 (12.32 MB)
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4 Summary and Further Study.mp4 (2.09 MB)
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1 Course Overview.mp4 (3.24 MB)
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01 Version Check.mp4 (581.33 KB)
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02 Module Overview.mp4 (1.96 MB)
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03 Prerequisites and Course Outline.mp4 (2.28 MB)
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04 Scaling and Standardization.mp4 (7.04 MB)
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05 Normalization.mp4 (4.27 MB)
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06 Transforming Data to Gaussian Distributions.mp4 (2.48 MB)
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07 Calculating and Visualizing Summary Statistics.mp4 (11.4 MB)
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09 Using the Robust Scaler to Scale Numeric Features.mp4 (7.21 MB)
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10 Normalization and Cosine Similarity.mp4 (12.67 MB)
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12 Reducing Dimensionality Using Factor Analysis.mp4 (14.16 MB)
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13 Module Summary.mp4 (1.75 MB)
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01 Module Overview.mp4 (1.75 MB)
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02 Outliers and Novelties.mp4 (4.74 MB)
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03 Detecting and Coping with Outlier Data.mp4 (6.58 MB)
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04 Local Outlier Factor.mp4 (5.31 MB)
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05 Elliptic Envelope.mp4 (4.78 MB)
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06 Isolation Forest.mp4 (5.88 MB)
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13 Module Summary.mp4 (1.73 MB)
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01 Module Overview.mp4 (1.66 MB)
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02 Representing Text Data in Numeric Form.mp4 (7.6 MB)
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03 Bag-of-words and Bag-of-n-grams Models.mp4 (3.84 MB)
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04 Vectorize Text Using the Bag-of-words Model.mp4 (12.03 MB)
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05 Vectorize Text Using the Bag-of-n-grams Model.mp4 (8.32 MB)
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06 Vectorize Text Using Tf-Idf Scores.mp4 (6.65 MB)
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07 Hashing for Dimensionality Reduction.mp4 (4.94 MB)
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08 Reducing Dimensions Using the Hashing Vectorizer.mp4 (6.64 MB)
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09 Performing Feature Extraction on a Python Dictionary.mp4 (4.65 MB)
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10 Module Summary.mp4 (1.95 MB)
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1 Module Overview.mp4 (1.65 MB)
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2 Representing Images as Matrices.mp4 (4.06 MB)
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3 Feature Extraction from Images.mp4 (8.32 MB)
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4 Extracting Patches from Image Data.mp4 (11.27 MB)
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6 Clustering Image Data Using a Pixel Connectivity Graph.mp4 (16.08 MB)
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7 Clustering Images Using a Gradient Connectivity Graph.mp4 (14.19 MB)
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8 Module Summary.mp4 (1.89 MB)
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1 Module Overview.mp4 (1.96 MB)
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2 Internal, Artificial, and External Datasets in Scikit Learn.mp4 (3.71 MB)
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3 Exploring Internal Datasets.mp4 (18.67 MB)
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5 Generating Manifold Data.mp4 (16.41 MB)
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6 Module Summary.mp4 (1.64 MB)
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1 Module Overview.mp4 (1.62 MB)
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2 Support Vector Classifiers and the Kernel Trick.mp4 (5.84 MB)
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3 Kernel Approximations.mp4 (10.8 MB)
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4 Preparing Image Data.mp4 (9.72 MB)
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5 Comparing Classifiers Trained Using Implicit and Explict Features.mp4 (17.61 MB)
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6 Comparing Accuracy and Runtime for Different Sample Sizes.mp4 (14.63 MB)
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7 Summary and Further Study.mp4 (2.78 MB)
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1 Course Overview.mp4 (3.52 MB)
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01 Version Check.mp4 (541.74 KB)
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02 Module Overview.mp4 (1.92 MB)
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03 Prerequisites and Course Outline.mp4 (2.09 MB)
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04 Dimensions of Scaling.mp4 (2.78 MB)
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05 Measuring Performance in Scaling.mp4 (9.45 MB)
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06 Influence of Number of Features.mp4 (7.2 MB)
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07 Influence of Feature Extraction Techniques.mp4 (6.41 MB)
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08 Influence of Feature Representation.mp4 (4.39 MB)
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09 Demo - Helper Functions to Generate Datasets and Train Models.mp4 (9.5 MB)
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10 Demo - Measuring Training Latencies for Different Models.mp4 (8.8 MB)
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11 Module Summary.mp4 (2.02 MB)
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01 Module Overview.mp4 (2.08 MB)
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04 Optimizations to Improve Prediction Latency.mp4 (10.48 MB)
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05 Optimizations to Improve Prediction Throughput.mp4 (2.91 MB)
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06 Demo - Observing the Influence of Model Complexity.mp4 (17.35 MB)
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09 Demo - Prediction with Sparse Data and Memory Profiling.mp4 (11.84 MB)
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10 Module Summary.mp4 (2.02 MB)
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1 Module Overview.mp4 (1.8 MB)
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2 Streaming Data.mp4 (5.46 MB)
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3 Incremental Learning for Large Datasets.mp4 (11.33 MB)
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4 Demo - Preparing Text Data for out of Core Learning.mp4 (11.9 MB)
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6 Demo - Visualizing Latencies and Accuracies.mp4 (9.34 MB)
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8 Module Summary.mp4 (1.9 MB)
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01 Module Overview.mp4 (1.61 MB)
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02 Parallelizing Computation Using Joblib.mp4 (6.5 MB)
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03 Demo - Introducing Joblib.mp4 (7.87 MB)
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04 Demo - Running Concurrent Workers Using Joblib.mp4 (8.9 MB)
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05 Demo - Cross Validation Using Concurrent Workers.mp4 (6.84 MB)
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06 Demo - Integrating Joblib with Dask ML.mp4 (7.37 MB)
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07 Demo - Grid Search with Concurrent Workers.mp4 (5.92 MB)
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08 Demo - Preparing Data for Multi-label Classification.mp4 (15.98 MB)
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09 Demo - Performing Multi-label Classification.mp4 (7.46 MB)
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10 Module Summary.mp4 (1.52 MB)
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1 Module Overview.mp4 (2.07 MB)
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2 Integrating Apache Spark and scikit-learn.mp4 (7.25 MB)
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3 Demo - Working with Spark Using spark-sklearn.mp4 (13.59 MB)
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4 Demo - Working with Spark Using scikit-spark.mp4 (8.57 MB)
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5 Summary and Further Study.mp4 (2.4 MB)
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1 Course Overview.mp4 (2.82 MB)
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1 Model Evaluation and Selection.mp4 (20.06 MB)
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1 Introduction.mp4 (1.14 MB)
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2 Classification Model Refresher.mp4 (1.62 MB)
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3 Confusion Matrix.mp4 (2.92 MB)
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4 Accuracy, Precision, Recall, and F1 Score.mp4 (5.38 MB)
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5 Choosing the Right Metric.mp4 (7.16 MB)
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6 ROC Curves and AUC.mp4 (6.17 MB)
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7 Demo.mp4 (20.16 MB)
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1 Introduction.mp4 (885 KB)
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2 Regression Model Refresher.mp4 (1.98 MB)
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3 Mean Square Error and Root Mean Square Error.mp4 (4.4 MB)
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4 Mean Absolute Error.mp4 (1.49 MB)
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5 R-squared and Adjusted R-squared.mp4 (7.54 MB)
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6 Choosing the Right Metric.mp4 (3.08 MB)
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7 Demo.mp4 (8.78 MB)
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8 Summary.mp4 (799.58 KB)
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1 Model Selection Techniques.mp4 (26.55 MB)
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1 Revisiting the Data Scientists Dilemma.mp4 (2.81 MB)
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2 Model Evaluation Methods.mp4 (2.67 MB)
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3 Model Selection Techniques.mp4 (2.27 MB)
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4 Demo - Using the Patient Dataset.mp4 (9.9 MB)
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01 Module Overview.mp4 (1.53 MB)
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02 Prerequisites and Course Outline.mp4 (1.22 MB)
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03 Introducing Machine Learning.mp4 (4.8 MB)
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04 Learning from Data - Training and Prediction.mp4 (7.88 MB)
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05 Traditional and Representation ML Models.mp4 (11.46 MB)
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06 The Niche of scikit-learn in ML.mp4 (7.94 MB)
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07 Exploring scikit-learn Libraries.mp4 (35.84 MB)
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08 Supervised and Unsupervised Learning.mp4 (9.21 MB)
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09 Installing scikit-learn Libraries.mp4 (5.55 MB)
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10 Summary.mp4 (1.86 MB)
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01 Module Overview.mp4 (1.41 MB)
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02 The Machine Learning Workflow.mp4 (6.66 MB)
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05 Choosing the Right Estimator - Clustering.mp4 (3.22 MB)
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11 Summary.mp4 (1.97 MB)
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1 Module Overview.mp4 (1.28 MB)
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2 Understanding Linear Regression.mp4 (5.99 MB)
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3 Data Preparation for Machine Learning.mp4 (13.28 MB)
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5 Understanding Logistic Regression.mp4 (9.58 MB)
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7 Summary and Further Study.mp4 (1.8 MB)
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1 Course Overview.mp4 (3.07 MB)
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1 Module Overview.mp4 (1.84 MB)
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2 Prerequisites and Course Outline.mp4 (1.99 MB)
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4 Logistic Regression Intuition.mp4 (8.34 MB)
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5 Cross Entropy Intuition.mp4 (3.43 MB)
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6 Accuracy, Precision, and Recall.mp4 (8.75 MB)
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8 Types of Classification.mp4 (5.92 MB)
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9 Module Summary.mp4 (1.58 MB)
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1 Module Overview.mp4 (1.55 MB)
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2 Installing and Setting up scikit-learn.mp4 (5.39 MB)
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3 Exploring the Titanic Dataset.mp4 (14.48 MB)
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4 Visualizing Relationships in the Data.mp4 (9.09 MB)
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5 Preprocessing the Data.mp4 (9.09 MB)
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6 Training a Logistic Regression Binary Classifier.mp4 (9.81 MB)
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9 Module Summary.mp4 (1.89 MB)
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01 Module Overview.mp4 (1.91 MB)
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02 Choosing Classification Algorithms.mp4 (3.37 MB)
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06 Stochastic Gradient Descent.mp4 (3.87 MB)
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08 Support Vector Machines.mp4 (11.49 MB)
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09 Implementing Support Vector Classification.mp4 (5.62 MB)
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10 Nearest Neighbors.mp4 (5.03 MB)
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12 Decision Trees.mp4 (4.54 MB)
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13 Implementing Decision Tree Classification.mp4 (5.54 MB)
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14 Naive Bayes.mp4 (5.9 MB)
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15 Implementing Naive Bayes Classification.mp4 (3.44 MB)
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16 Module Summary.mp4 (2.22 MB)
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1 Module Overview.mp4 (1.41 MB)
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2 Hyperparameter Tuning.mp4 (5.64 MB)
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5 Module Summary.mp4 (1.36 MB)
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1 Module Overview.mp4 (1.48 MB)
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2 Representing Images as Matrices.mp4 (4.48 MB)
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3 Exploring the Fashion MNIST Dataset.mp4 (12.03 MB)
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5 Summary and Further Study.mp4 (1.74 MB)
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1 Course Overview.mp4 (3.63 MB)
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01 Module Overview.mp4 (1.79 MB)
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02 Prerequisites and Course Outline.mp4 (1.98 MB)
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04 Minimizing Least Square Error.mp4 (6.22 MB)
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05 Installing and Setting up scikit-learn.mp4 (4.64 MB)
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06 Exploring the Automobile Mpg Dataset.mp4 (14.51 MB)
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09 R-squared and Adjusted R-squared.mp4 (2.21 MB)
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10 Regression with Categorical Variables.mp4 (5.71 MB)
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11 Module Summary.mp4 (1.61 MB)
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1 Module Overview.mp4 (1.51 MB)
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2 Simple Linear Regression.mp4 (16.22 MB)
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3 Linear Regression with Multiple Features.mp4 (13.14 MB)
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4 Standardizing Numeric Data.mp4 (10.02 MB)
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5 Label Encoding and One-hot Encoding Categorical Data.mp4 (10.95 MB)
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6 Linear Regression and the Dummy Trap.mp4 (12.13 MB)
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7 Module Summary.mp4 (1.52 MB)
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01 Module Overview.mp4 (1.59 MB)
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02 Overview of Regression Models in scikit-learn.mp4 (3.34 MB)
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03 Overfitting and Regularization.mp4 (6.13 MB)
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04 Lasso, Ridge and Elastic Net Regression.mp4 (7.43 MB)
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07 Lasso Regression.mp4 (6.43 MB)
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08 Ridge Regression.mp4 (4.11 MB)
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09 Elastic Net Regression.mp4 (13.86 MB)
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10 Module Summary.mp4 (1.82 MB)
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01 Module Overview.mp4 (1.96 MB)
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02 Choosing Regression Algorithms.mp4 (4.15 MB)
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03 Support Vector Regression.mp4 (8.64 MB)
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04 Implementing Support Vector Regression.mp4 (6.06 MB)
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05 Nearest Neighbors Regression.mp4 (6.36 MB)
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06 Implementing K-nearest-neighbors Regression.mp4 (4.42 MB)
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07 Stochastic Gradient Descent Regression.mp4 (4.32 MB)
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08 Implementing Stochastic Gradient Descent Regression.mp4 (4.51 MB)
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09 Decision Tree Regression.mp4 (6.35 MB)
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10 Implementing Decision Tree Regression.mp4 (3.1 MB)
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11 Least Angle Regression.mp4 (5.23 MB)
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12 Implementing Least Angle Regression.mp4 (2.39 MB)
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13 Regression with Polynomial Relationships.mp4 (2.5 MB)
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14 Module Summary.mp4 (2.08 MB)
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1 Module Overview.mp4 (1.49 MB)
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2 Hyperparameter Tuning.mp4 (5.27 MB)
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4 Tuning Different Regression Models Using Grid Search.mp4 (9.59 MB)
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5 Summary and Further Study.mp4 (1.44 MB)
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1 Course Overview.mp4 (2.68 MB)
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01 Module Overview.mp4 (1.15 MB)
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02 Prerequisites and Course Outline.mp4 (1.8 MB)
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03 Supervised and Unsupervised Learning.mp4 (6.99 MB)
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04 Clustering Objectives and Use Cases.mp4 (13.54 MB)
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05 K-means Clustering.mp4 (5.99 MB)
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06 Evaluating Clustering Models.mp4 (8.14 MB)
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08 Performing K-means Clustering.mp4 (11.14 MB)
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09 Evaluating K-means Clustering.mp4 (18.01 MB)
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10 Exploring the Iris Dataset.mp4 (6.66 MB)
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11 Performing K-means Clustering and Evaluation.mp4 (11.25 MB)
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01 Module Overview.mp4 (1.53 MB)
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02 Categories of Clustering Algorithms.mp4 (5.45 MB)
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03 Setting up Helper Functions to Perform Clustering.mp4 (5.79 MB)
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04 Choosing Clustering Algorithms.mp4 (9.69 MB)
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05 Hierarchical Clustering.mp4 (8.1 MB)
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06 Agglomerative Clustering.mp4 (7.18 MB)
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07 DBSCAN Clustering.mp4 (7.63 MB)
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08 Mean-shift Clustering.mp4 (10.24 MB)
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09 BIRCH Clustering.mp4 (4.85 MB)
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10 Affinilty Propagation Clustering.mp4 (6.41 MB)
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11 Mini-batch K-means Clustering.mp4 (4.46 MB)
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12 Spectral Clustering Using a Precomputed Matrix.mp4 (11.98 MB)
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1 Module Overview.mp4 (860.16 KB)
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2 Understanding the Silhouette Score.mp4 (4.05 MB)
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3 K-means Number of Clusters - The Elbow Method.mp4 (5.61 MB)
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4 K-means Number of Clusters - The Silhouette Method.mp4 (5.02 MB)
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5 Seeds and Distance Measures.mp4 (2.23 MB)
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6 Hyperparameter Tuning - K-means Clustering.mp4 (10.39 MB)
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7 Hyperparameter Tuning - DBSCAN Clustering.mp4 (11.35 MB)
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8 Hyperparameter Tuning - Mean-shift Clustering.mp4 (2.68 MB)
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1 Module Overview.mp4 (1.06 MB)
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2 Images as Matrices.mp4 (4.22 MB)
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3 Exploring the MNIST Handwritten Digits Dataset.mp4 (5.43 MB)
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4 Clustering Image Data.mp4 (8.8 MB)
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5 Summary and Further Study.mp4 (1.65 MB)
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1 Course Overview.mp4 (3.31 MB)
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1 Module Overview.mp4 (1.55 MB)
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2 Prerequisites and Course Outline.mp4 (2.18 MB)
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3 Support for Neural Networks in scikit-learn.mp4 (7.65 MB)
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4 Perceptrons and Neurons.mp4 (10.7 MB)
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5 Multi-layer Perceptrons and Neural Networks.mp4 (4.91 MB)
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6 Training a Neural Network.mp4 (8.34 MB)
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7 Overfitting and Underfitting.mp4 (4.06 MB)
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8 Module Summary.mp4 (1.79 MB)
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1 Module Overview.mp4 (1.62 MB)
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8 Module Summary.mp4 (1.81 MB)
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1 Module Overview.mp4 (1.68 MB)
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9 Module Summary.mp4 (1.81 MB)
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1 Course Overview.mp4 (4.21 MB)
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01 Module Overview.mp4 (1.36 MB)
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02 Prerequisites and Course Outline.mp4 (2.22 MB)
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03 The Curse of Dimensionality.mp4 (7.98 MB)
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04 Overfitted Models and Data Sparsity.mp4 (5.27 MB)
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05 Exploring Techniques for Reducing Dimensions.mp4 (4.88 MB)
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06 Demo - Exploring the Classification Dataset.mp4 (11.74 MB)
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08 Demo - Exploring the Regression Dataset.mp4 (10.38 MB)
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10 Feature Selection and Dictionary Learning.mp4 (7.3 MB)
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13 Demo - Finding the Best Value of K.mp4 (7.54 MB)
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16 Summary.mp4 (1.49 MB)
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01 Module Overview.mp4 (1.56 MB)
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02 The Intuition Behind Principal Components Analysis.mp4 (10.01 MB)
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03 Demo - Implementing Principal Component Analysis.mp4 (11.31 MB)
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04 Demo - Building Regression Models with Principal Components.mp4 (4.96 MB)
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05 Factor Analysis Using Singular Value Decomposition.mp4 (3.09 MB)
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06 Demo - Implementing Factor Analysis.mp4 (13.18 MB)
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07 Linear Discriminant Analysis for Dimensionality Reduction.mp4 (4.02 MB)
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09 Demo - Linear Discriminant Analysis for Classification.mp4 (6.88 MB)
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10 Summary.mp4 (2.2 MB)
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01 Module Overview.mp4 (1.02 MB)
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02 The Manifold Hypothesis and Manifold Learning.mp4 (9.13 MB)
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04 Demo - Metric and Non-metric Multi Dimensional Scaling.mp4 (4.81 MB)
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06 Demo - Manifold Learning with Locally Linear Embedding.mp4 (4.95 MB)
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08 Demo - Manifold Learning with Handwritten Digits.mp4 (8.46 MB)
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10 Demo - Manifold Learning on Olivetti Faces Dataset.mp4 (5.54 MB)
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11 Summary and Further Study.mp4 (2.05 MB)
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1 Course Overview.mp4 (3.33 MB)
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01 Module Overview.mp4 (1.82 MB)
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02 Prerequisites and Course Outline.mp4 (2.33 MB)
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03 A Quick Overview of Ensemble Learning.mp4 (9.68 MB)
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04 Averaging and Boosting, Voting and Stacking.mp4 (9.7 MB)
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05 Decision Trees in Ensemble Learning.mp4 (5.17 MB)
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06 Understanding Decision Trees.mp4 (4.82 MB)
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07 Overfitted Models and Ensemble Learning.mp4 (7.56 MB)
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08 Getting Started and Exploring the Environment.mp4 (3.78 MB)
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09 Exploring the Classification Dataset.mp4 (14.71 MB)
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10 Hard Voting.mp4 (10.83 MB)
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11 Soft Voting.mp4 (9 MB)
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12 Module Summary.mp4 (1.93 MB)
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01 Module Overview.mp4 (2.16 MB)
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02 Bagging and Pasting.mp4 (7.98 MB)
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03 Random Subspaces and Random Patches.mp4 (4.12 MB)
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04 Extra Trees.mp4 (4.64 MB)
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05 Averaging vs Boosting.mp4 (3.3 MB)
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06 Exploring the Regression Dataset.mp4 (9.72 MB)
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07 Regression Using Bagging and Pasting.mp4 (10.67 MB)
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08 Regression Using Random Subspaces.mp4 (3.27 MB)
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09 Classification Using Bagging and Pasting.mp4 (7.05 MB)
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10 Classification Using Random Patches.mp4 (3.56 MB)
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11 Regression Using Random Forest.mp4 (10.64 MB)
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12 Regression Using Extra Trees.mp4 (3.84 MB)
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14 Module Summary.mp4 (1.8 MB)
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1 Module Overview.mp4 (2.04 MB)
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2 Adaptive Boosting (AdaBoost).mp4 (4.71 MB)
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3 Regression Using AdaBoost.mp4 (12.38 MB)
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4 Classification Using AdaBoost.mp4 (8.93 MB)
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5 Gradient Boosting.mp4 (4.08 MB)
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6 Regression Using Gradient Boosting.mp4 (11.03 MB)
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9 Module Summary.mp4 (1.61 MB)
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1 Module Overview.mp4 (1.36 MB)
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2 Stacking.mp4 (4.9 MB)
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3 Classification Using a Stacking Ensemble.mp4 (12.32 MB)
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4 Summary and Further Study.mp4 (2.09 MB)
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1 Course Overview.mp4 (3.24 MB)
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01 Version Check.mp4 (581.33 KB)
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02 Module Overview.mp4 (1.96 MB)
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03 Prerequisites and Course Outline.mp4 (2.28 MB)
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04 Scaling and Standardization.mp4 (7.04 MB)
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05 Normalization.mp4 (4.27 MB)
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06 Transforming Data to Gaussian Distributions.mp4 (2.48 MB)
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07 Calculating and Visualizing Summary Statistics.mp4 (11.4 MB)
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09 Using the Robust Scaler to Scale Numeric Features.mp4 (7.21 MB)
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10 Normalization and Cosine Similarity.mp4 (12.67 MB)
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12 Reducing Dimensionality Using Factor Analysis.mp4 (14.16 MB)
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13 Module Summary.mp4 (1.75 MB)
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01 Module Overview.mp4 (1.75 MB)
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02 Outliers and Novelties.mp4 (4.74 MB)
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03 Detecting and Coping with Outlier Data.mp4 (6.58 MB)
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04 Local Outlier Factor.mp4 (5.31 MB)
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05 Elliptic Envelope.mp4 (4.78 MB)
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06 Isolation Forest.mp4 (5.88 MB)
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13 Module Summary.mp4 (1.73 MB)
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01 Module Overview.mp4 (1.66 MB)
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02 Representing Text Data in Numeric Form.mp4 (7.6 MB)
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03 Bag-of-words and Bag-of-n-grams Models.mp4 (3.84 MB)
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04 Vectorize Text Using the Bag-of-words Model.mp4 (12.03 MB)
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05 Vectorize Text Using the Bag-of-n-grams Model.mp4 (8.32 MB)
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06 Vectorize Text Using Tf-Idf Scores.mp4 (6.65 MB)
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07 Hashing for Dimensionality Reduction.mp4 (4.94 MB)
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08 Reducing Dimensions Using the Hashing Vectorizer.mp4 (6.64 MB)
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09 Performing Feature Extraction on a Python Dictionary.mp4 (4.65 MB)
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10 Module Summary.mp4 (1.95 MB)
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1 Module Overview.mp4 (1.65 MB)
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2 Representing Images as Matrices.mp4 (4.06 MB)
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3 Feature Extraction from Images.mp4 (8.32 MB)
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4 Extracting Patches from Image Data.mp4 (11.27 MB)
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6 Clustering Image Data Using a Pixel Connectivity Graph.mp4 (16.08 MB)
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7 Clustering Images Using a Gradient Connectivity Graph.mp4 (14.19 MB)
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8 Module Summary.mp4 (1.89 MB)
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1 Module Overview.mp4 (1.96 MB)
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2 Internal, Artificial, and External Datasets in Scikit Learn.mp4 (3.71 MB)
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3 Exploring Internal Datasets.mp4 (18.67 MB)
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5 Generating Manifold Data.mp4 (16.41 MB)
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6 Module Summary.mp4 (1.64 MB)
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1 Module Overview.mp4 (1.62 MB)
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2 Support Vector Classifiers and the Kernel Trick.mp4 (5.84 MB)
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3 Kernel Approximations.mp4 (10.8 MB)
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4 Preparing Image Data.mp4 (9.72 MB)
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5 Comparing Classifiers Trained Using Implicit and Explict Features.mp4 (17.61 MB)
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6 Comparing Accuracy and Runtime for Different Sample Sizes.mp4 (14.63 MB)
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7 Summary and Further Study.mp4 (2.78 MB)
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1 Course Overview.mp4 (3.52 MB)
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01 Version Check.mp4 (541.74 KB)
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02 Module Overview.mp4 (1.92 MB)
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03 Prerequisites and Course Outline.mp4 (2.09 MB)
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04 Dimensions of Scaling.mp4 (2.78 MB)
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05 Measuring Performance in Scaling.mp4 (9.45 MB)
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06 Influence of Number of Features.mp4 (7.2 MB)
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07 Influence of Feature Extraction Techniques.mp4 (6.41 MB)
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08 Influence of Feature Representation.mp4 (4.39 MB)
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09 Demo - Helper Functions to Generate Datasets and Train Models.mp4 (9.5 MB)
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10 Demo - Measuring Training Latencies for Different Models.mp4 (8.8 MB)
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11 Module Summary.mp4 (2.02 MB)
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01 Module Overview.mp4 (2.08 MB)
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04 Optimizations to Improve Prediction Latency.mp4 (10.48 MB)
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05 Optimizations to Improve Prediction Throughput.mp4 (2.91 MB)
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06 Demo - Observing the Influence of Model Complexity.mp4 (17.35 MB)
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09 Demo - Prediction with Sparse Data and Memory Profiling.mp4 (11.84 MB)
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10 Module Summary.mp4 (2.02 MB)
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1 Module Overview.mp4 (1.8 MB)
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2 Streaming Data.mp4 (5.46 MB)
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3 Incremental Learning for Large Datasets.mp4 (11.33 MB)
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4 Demo - Preparing Text Data for out of Core Learning.mp4 (11.9 MB)
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6 Demo - Visualizing Latencies and Accuracies.mp4 (9.34 MB)
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8 Module Summary.mp4 (1.9 MB)
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01 Module Overview.mp4 (1.61 MB)
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02 Parallelizing Computation Using Joblib.mp4 (6.5 MB)
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03 Demo - Introducing Joblib.mp4 (7.87 MB)
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04 Demo - Running Concurrent Workers Using Joblib.mp4 (8.9 MB)
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05 Demo - Cross Validation Using Concurrent Workers.mp4 (6.84 MB)
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06 Demo - Integrating Joblib with Dask ML.mp4 (7.37 MB)
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07 Demo - Grid Search with Concurrent Workers.mp4 (5.92 MB)
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08 Demo - Preparing Data for Multi-label Classification.mp4 (15.98 MB)
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09 Demo - Performing Multi-label Classification.mp4 (7.46 MB)
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10 Module Summary.mp4 (1.52 MB)
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1 Module Overview.mp4 (2.07 MB)
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2 Integrating Apache Spark and scikit-learn.mp4 (7.25 MB)
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3 Demo - Working with Spark Using spark-sklearn.mp4 (13.59 MB)
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4 Demo - Working with Spark Using scikit-spark.mp4 (8.57 MB)
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5 Summary and Further Study.mp4 (2.4 MB)
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1 Course Overview.mp4 (2.82 MB)
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1 Model Evaluation and Selection.mp4 (20.06 MB)
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1 Introduction.mp4 (1.14 MB)
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2 Classification Model Refresher.mp4 (1.62 MB)
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3 Confusion Matrix.mp4 (2.92 MB)
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4 Accuracy, Precision, Recall, and F1 Score.mp4 (5.38 MB)
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5 Choosing the Right Metric.mp4 (7.16 MB)
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6 ROC Curves and AUC.mp4 (6.17 MB)
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7 Demo.mp4 (20.16 MB)
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1 Introduction.mp4 (885 KB)
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2 Regression Model Refresher.mp4 (1.98 MB)
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3 Mean Square Error and Root Mean Square Error.mp4 (4.4 MB)
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4 Mean Absolute Error.mp4 (1.49 MB)
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5 R-squared and Adjusted R-squared.mp4 (7.54 MB)
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6 Choosing the Right Metric.mp4 (3.08 MB)
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7 Demo.mp4 (8.78 MB)
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8 Summary.mp4 (799.58 KB)
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1 Model Selection Techniques.mp4 (26.55 MB)
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1 Revisiting the Data Scientists Dilemma.mp4 (2.81 MB)
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2 Model Evaluation Methods.mp4 (2.67 MB)
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3 Model Selection Techniques.mp4 (2.27 MB)
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4 Demo - Using the Patient Dataset.mp4 (9.9 MB)
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