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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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