1.49 GB | 00:06:39 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
1 Course Overview.mp4 (3.09 MB)
01 Module Overview.mp4 (1.53 MB)
02 Prerequisites and Course Outline.mp4 (1.22 MB)
03 Introducing Machine Learning.mp4 (4.8 MB)
05 Traditional and Representation ML Models.mp4 (11.46 MB)
06 The Niche of scikit-learn in ML.mp4 (7.94 MB)
07 Exploring scikit-learn Libraries.mp4 (35.84 MB)
08 Supervised and Unsupervised Learning.mp4 (9.21 MB)
09 Installing scikit-learn Libraries.mp4 (5.55 MB)
10 Summary.mp4 (1.86 MB)
01 Module Overview.mp4 (1.41 MB)
11 Summary.mp4 (1.97 MB)
1 Module Overview.mp4 (1.28 MB)
7 Summary and Further Study.mp4 (1.8 MB)
1 Course Overview.mp4 (3.07 MB)
1 Module Overview.mp4 (1.84 MB)
9 Module Summary.mp4 (1.58 MB)
1 Module Overview.mp4 (1.55 MB)
2 Installing and Setting up scikit-learn.mp4 (5.39 MB)
3 Exploring the Titanic Dataset.mp4 (14.48 MB)
4 Visualizing Relationships in the Data.mp4 (9.09 MB)
5 Preprocessing the Data.mp4 (9.09 MB)
9 Module Summary.mp4 (1.89 MB)
01 Module Overview.mp4 (1.91 MB)
06 Stochastic Gradient Descent.mp4 (3.87 MB)
08 Support Vector Machines.mp4 (11.49 MB)
10 Nearest Neighbors.mp4 (5.03 MB)
12 Decision Trees.mp4 (4.54 MB)
14 Naive Bayes.mp4 (5.9 MB)
16 Module Summary.mp4 (2.22 MB)
1 Module Overview.mp4 (1.41 MB)
2 Hyperparameter Tuning.mp4 (5.64 MB)
5 Module Summary.mp4 (1.36 MB)
1 Module Overview.mp4 (1.48 MB)
5 Summary and Further Study.mp4 (1.74 MB)
1 Course Overview.mp4 (3.63 MB)
01 Module Overview.mp4 (1.79 MB)
11 Module Summary.mp4 (1.61 MB)
1 Module Overview.mp4 (1.51 MB)
2 Simple Linear Regression.mp4 (16.22 MB)
3 Linear Regression with Multiple Features.mp4 (13.14 MB)
4 Standardizing Numeric Data.mp4 (10.02 MB)
5 Label Encoding and One-hot Encoding Categorical Data.mp4 (10.95 MB)
6 Linear Regression and the Dummy Trap.mp4 (12.13 MB)
7 Module Summary.mp4 (1.52 MB)
01 Module Overview.mp4 (1.59 MB)
02 Overview of Regression Models in scikit-learn.mp4 (3.34 MB)
03 Overfitting and Regularization.mp4 (6.13 MB)
04 Lasso, Ridge and Elastic Net Regression.mp4 (7.43 MB)
07 Lasso Regression.mp4 (6.43 MB)
08 Ridge Regression.mp4 (4.11 MB)
09 Elastic Net Regression.mp4 (13.86 MB)
10 Module Summary.mp4 (1.82 MB)
01 Module Overview.mp4 (1.96 MB)
02 Choosing Regression Algorithms.mp4 (4.15 MB)
03 Support Vector Regression.mp4 (8.64 MB)
05 Nearest Neighbors Regression.mp4 (6.36 MB)
09 Decision Tree Regression.mp4 (6.35 MB)
11 Least Angle Regression.mp4 (5.23 MB)
12 Implementing Least Angle Regression.mp4 (2.39 MB)
14 Module Summary.mp4 (2.08 MB)
1 Module Overview.mp4 (1.49 MB)
2 Hyperparameter Tuning.mp4 (5.27 MB)
5 Summary and Further Study.mp4 (1.44 MB)
1 Course Overview.mp4 (2.68 MB)
01 Module Overview.mp4 (1.15 MB)
02 Prerequisites and Course Outline.mp4 (1.8 MB)
05 K-means Clustering.mp4 (5.99 MB)
06 Evaluating Clustering Models.mp4 (8.14 MB)
08 Performing K-means Clustering.mp4 (11.14 MB)
09 Evaluating K-means Clustering.mp4 (18.01 MB)
10 Exploring the Iris Dataset.mp4 (6.66 MB)
01 Module Overview.mp4 (1.53 MB)
02 Categories of Clustering Algorithms.mp4 (5.45 MB)
04 Choosing Clustering Algorithms.mp4 (9.69 MB)
05 Hierarchical Clustering.mp4 (8.1 MB)
06 Agglomerative Clustering.mp4 (7.18 MB)
07 DBSCAN Clustering.mp4 (7.63 MB)
08 Mean-shift Clustering.mp4 (10.24 MB)
09 BIRCH Clustering.mp4 (4.85 MB)
10 Affinilty Propagation Clustering.mp4 (6.41 MB)
11 Mini-batch K-means Clustering.mp4 (4.46 MB)
1 Module Overview.mp4 (860.16 KB)
2 Understanding the Silhouette Score.mp4 (4.05 MB)
5 Seeds and Distance Measures.mp4 (2.23 MB)
7 Hyperparameter Tuning - DBSCAN Clustering.mp4 (11.35 MB)
1 Module Overview.mp4 (1.06 MB)
2 Images as Matrices.mp4 (4.22 MB)
3 Exploring the MNIST Handwritten Digits Dataset.mp4 (5.43 MB)
4 Clustering Image Data.mp4 (8.8 MB)
5 Summary and Further Study.mp4 (1.65 MB)
1 Course Overview.mp4 (3.31 MB)
1 Module Overview.mp4 (1.55 MB)
2 Prerequisites and Course Outline.mp4 (2.18 MB)
3 Support for Neural Networks in scikit-learn.mp4 (7.65 MB)
4 Perceptrons and Neurons.mp4 (10.7 MB)
5 Multi-layer Perceptrons and Neural Networks.mp4 (4.91 MB)
6 Training a Neural Network.mp4 (8.34 MB)
7 Overfitting and Underfitting.mp4 (4.06 MB)
8 Module Summary.mp4 (1.79 MB)
1 Course Overview.mp4 (4.21 MB)
01 Module Overview.mp4 (1.36 MB)
03 The Curse of Dimensionality.mp4 (7.98 MB)
16 Summary.mp4 (1.49 MB)
01 Module Overview.mp4 (1.56 MB)
06 Demo - Implementing Factor Analysis.mp4 (13.18 MB)
10 Summary.mp4 (2.2 MB)
01 Module Overview.mp4 (1.02 MB)
11 Summary and Further Study.mp4 (2.05 MB)
1 Course Overview.mp4 (3.33 MB)
01 Module Overview.mp4 (1.82 MB)
02 Prerequisites and Course Outline.mp4 (2.33 MB)
03 A Quick Overview of Ensemble Learning.mp4 (9.68 MB)
05 Decision Trees in Ensemble Learning.mp4 (5.17 MB)
06 Understanding Decision Trees.mp4 (4.82 MB)
07 Overfitted Models and Ensemble Learning.mp4 (7.56 MB)
09 Exploring the Classification Dataset.mp4 (14.71 MB)
10 Hard Voting.mp4 (10.83 MB)
11 Soft Voting.mp4 (9 MB)
12 Module Summary.mp4 (1.93 MB)
01 Module Overview.mp4 (2.16 MB)
02 Bagging and Pasting.mp4 (7.98 MB)
04 Extra Trees.mp4 (4.64 MB)
05 Averaging vs Boosting.mp4 (3.3 MB)
12 Regression Using Extra Trees.mp4 (3.84 MB)
14 Module Summary.mp4 (1.8 MB)
1 Module Overview.mp4 (2.04 MB)
2 Adaptive Boosting (AdaBoost).mp4 (4.71 MB)
3 Regression Using AdaBoost.mp4 (12.38 MB)
4 Classification Using AdaBoost.mp4 (8.93 MB)
5 Gradient Boosting.mp4 (4.08 MB)
9 Module Summary.mp4 (1.61 MB)
1 Module Overview.mp4 (1.36 MB)
2 Stacking.mp4 (4.9 MB)
4 Summary and Further Study.mp4 (2.09 MB)
1 Course Overview.mp4 (3.24 MB)
01 Version Check.mp4 (581.33 KB)
02 Module Overview.mp4 (1.96 MB)
03 Prerequisites and Course Outline.mp4 (2.28 MB)
04 Scaling and Standardization.mp4 (7.04 MB)
05 Normalization.mp4 (4.27 MB)
10 Normalization and Cosine Similarity.mp4 (12.67 MB)
13 Module Summary.mp4 (1.75 MB)
01 Module Overview.mp4 (1.75 MB)
02 Outliers and Novelties.mp4 (4.74 MB)
04 Local Outlier Factor.mp4 (5.31 MB)
05 Elliptic Envelope.mp4 (4.78 MB)
06 Isolation Forest.mp4 (5.88 MB)
13 Module Summary.mp4 (1.73 MB)
01 Module Overview.mp4 (1.66 MB)
02 Representing Text Data in Numeric Form.mp4 (7.6 MB)
03 Bag-of-words and Bag-of-n-grams Models.mp4 (3.84 MB)
06 Vectorize Text Using Tf-Idf Scores.mp4 (6.65 MB)
07 Hashing for Dimensionality Reduction.mp4 (4.94 MB)
10 Module Summary.mp4 (1.95 MB)
1 Module Overview.mp4 (1.65 MB)
2 Representing Images as Matrices.mp4 (4.06 MB)
3 Feature Extraction from Images.mp4 (8.32 MB)
4 Extracting Patches from Image Data.mp4 (11.27 MB)
8 Module Summary.mp4 (1.89 MB)
1 Module Overview.mp4 (1.96 MB)
3 Exploring Internal Datasets.mp4 (18.67 MB)
5 Generating Manifold Data.mp4 (16.41 MB)
6 Module Summary.mp4 (1.64 MB)
1 Module Overview.mp4 (1.62 MB)
2 Support Vector Classifiers and the Kernel Trick.mp4 (5.84 MB)
3 Kernel Approximations.mp4 (10.8 MB)
4 Preparing Image Data.mp4 (9.72 MB)
7 Summary and Further Study.mp4 (2.78 MB)
1 Course Overview.mp4 (3.52 MB)
01 Version Check.mp4 (541.74 KB)
02 Module Overview.mp4 (1.92 MB)
03 Prerequisites and Course Outline.mp4 (2.09 MB)
04 Dimensions of Scaling.mp4 (2.78 MB)
05 Measuring Performance in Scaling.mp4 (9.45 MB)
06 Influence of Number of Features.mp4 (7.2 MB)
07 Influence of Feature Extraction Techniques.mp4 (6.41 MB)
08 Influence of Feature Representation.mp4 (4.39 MB)
11 Module Summary.mp4 (2.02 MB)
01 Module Overview.mp4 (2.08 MB)
04 Optimizations to Improve Prediction Latency.mp4 (10.48 MB)
05 Optimizations to Improve Prediction Throughput.mp4 (2.91 MB)
10 Module Summary.mp4 (2.02 MB)
1 Module Overview.mp4 (1.8 MB)
2 Streaming Data.mp4 (5.46 MB)
8 Module Summary.mp4 (1.9 MB)
01 Module Overview.mp4 (1.61 MB)
02 Parallelizing Computation Using Joblib.mp4 (6.5 MB)
03 Demo - Introducing Joblib.mp4 (7.87 MB)
04 Demo - Running Concurrent Workers Using Joblib.mp4 (8.9 MB)
06 Demo - Integrating Joblib with Dask ML.mp4 (7.37 MB)
07 Demo - Grid Search with Concurrent Workers.mp4 (5.92 MB)
09 Demo - Performing Multi-label Classification.mp4 (7.46 MB)
10 Module Summary.mp4 (1.52 MB)
1 Module Overview.mp4 (2.07 MB)
2 Integrating Apache Spark and scikit-learn.mp4 (7.25 MB)
3 Demo - Working with Spark Using spark-sklearn.mp4 (13.59 MB)
4 Demo - Working with Spark Using scikit-spark.mp4 (8.57 MB)
5 Summary and Further Study.mp4 (2.4 MB)
1 Course Overview.mp4 (2.82 MB)
1 Model Evaluation and Selection.mp4 (20.06 MB)
1 Introduction.mp4 (1.14 MB)
2 Classification Model Refresher.mp4 (1.62 MB)
3 Confusion Matrix.mp4 (2.92 MB)
5 Choosing the Right Metric.mp4 (7.16 MB)
6 ROC Curves and AUC.mp4 (6.17 MB)
7 Demo.mp4 (20.16 MB)
1 Introduction.mp4 (885 KB)
2 Regression Model Refresher.mp4 (1.98 MB)
4 Mean Absolute Error.mp4 (1.49 MB)
5 R-squared and Adjusted R-squared.mp4 (7.54 MB)
6 Choosing the Right Metric.mp4 (3.08 MB)
7 Demo.mp4 (8.78 MB)
8 Summary.mp4 (799.58 KB)
1 Model Selection Techniques.mp4 (26.55 MB)
1 Revisiting the Data Scientists Dilemma.mp4 (2.81 MB)
2 Model Evaluation Methods.mp4 (2.67 MB)
3 Model Selection Techniques.mp4 (2.27 MB)
4 Demo - Using the Patient Dataset.mp4 (9.9 MB)
01 Module Overview.mp4 (1.53 MB)
02 Prerequisites and Course Outline.mp4 (1.22 MB)
03 Introducing Machine Learning.mp4 (4.8 MB)
05 Traditional and Representation ML Models.mp4 (11.46 MB)
06 The Niche of scikit-learn in ML.mp4 (7.94 MB)
07 Exploring scikit-learn Libraries.mp4 (35.84 MB)
08 Supervised and Unsupervised Learning.mp4 (9.21 MB)
09 Installing scikit-learn Libraries.mp4 (5.55 MB)
10 Summary.mp4 (1.86 MB)
01 Module Overview.mp4 (1.41 MB)
11 Summary.mp4 (1.97 MB)
1 Module Overview.mp4 (1.28 MB)
7 Summary and Further Study.mp4 (1.8 MB)
1 Course Overview.mp4 (3.07 MB)
1 Module Overview.mp4 (1.84 MB)
9 Module Summary.mp4 (1.58 MB)
1 Module Overview.mp4 (1.55 MB)
2 Installing and Setting up scikit-learn.mp4 (5.39 MB)
3 Exploring the Titanic Dataset.mp4 (14.48 MB)
4 Visualizing Relationships in the Data.mp4 (9.09 MB)
5 Preprocessing the Data.mp4 (9.09 MB)
9 Module Summary.mp4 (1.89 MB)
01 Module Overview.mp4 (1.91 MB)
06 Stochastic Gradient Descent.mp4 (3.87 MB)
08 Support Vector Machines.mp4 (11.49 MB)
10 Nearest Neighbors.mp4 (5.03 MB)
12 Decision Trees.mp4 (4.54 MB)
14 Naive Bayes.mp4 (5.9 MB)
16 Module Summary.mp4 (2.22 MB)
1 Module Overview.mp4 (1.41 MB)
2 Hyperparameter Tuning.mp4 (5.64 MB)
5 Module Summary.mp4 (1.36 MB)
1 Module Overview.mp4 (1.48 MB)
5 Summary and Further Study.mp4 (1.74 MB)
1 Course Overview.mp4 (3.63 MB)
01 Module Overview.mp4 (1.79 MB)
11 Module Summary.mp4 (1.61 MB)
1 Module Overview.mp4 (1.51 MB)
2 Simple Linear Regression.mp4 (16.22 MB)
3 Linear Regression with Multiple Features.mp4 (13.14 MB)
4 Standardizing Numeric Data.mp4 (10.02 MB)
5 Label Encoding and One-hot Encoding Categorical Data.mp4 (10.95 MB)
6 Linear Regression and the Dummy Trap.mp4 (12.13 MB)
7 Module Summary.mp4 (1.52 MB)
01 Module Overview.mp4 (1.59 MB)
02 Overview of Regression Models in scikit-learn.mp4 (3.34 MB)
03 Overfitting and Regularization.mp4 (6.13 MB)
04 Lasso, Ridge and Elastic Net Regression.mp4 (7.43 MB)
07 Lasso Regression.mp4 (6.43 MB)
08 Ridge Regression.mp4 (4.11 MB)
09 Elastic Net Regression.mp4 (13.86 MB)
10 Module Summary.mp4 (1.82 MB)
01 Module Overview.mp4 (1.96 MB)
02 Choosing Regression Algorithms.mp4 (4.15 MB)
03 Support Vector Regression.mp4 (8.64 MB)
05 Nearest Neighbors Regression.mp4 (6.36 MB)
09 Decision Tree Regression.mp4 (6.35 MB)
11 Least Angle Regression.mp4 (5.23 MB)
12 Implementing Least Angle Regression.mp4 (2.39 MB)
14 Module Summary.mp4 (2.08 MB)
1 Module Overview.mp4 (1.49 MB)
2 Hyperparameter Tuning.mp4 (5.27 MB)
5 Summary and Further Study.mp4 (1.44 MB)
1 Course Overview.mp4 (2.68 MB)
01 Module Overview.mp4 (1.15 MB)
02 Prerequisites and Course Outline.mp4 (1.8 MB)
05 K-means Clustering.mp4 (5.99 MB)
06 Evaluating Clustering Models.mp4 (8.14 MB)
08 Performing K-means Clustering.mp4 (11.14 MB)
09 Evaluating K-means Clustering.mp4 (18.01 MB)
10 Exploring the Iris Dataset.mp4 (6.66 MB)
01 Module Overview.mp4 (1.53 MB)
02 Categories of Clustering Algorithms.mp4 (5.45 MB)
04 Choosing Clustering Algorithms.mp4 (9.69 MB)
05 Hierarchical Clustering.mp4 (8.1 MB)
06 Agglomerative Clustering.mp4 (7.18 MB)
07 DBSCAN Clustering.mp4 (7.63 MB)
08 Mean-shift Clustering.mp4 (10.24 MB)
09 BIRCH Clustering.mp4 (4.85 MB)
10 Affinilty Propagation Clustering.mp4 (6.41 MB)
11 Mini-batch K-means Clustering.mp4 (4.46 MB)
1 Module Overview.mp4 (860.16 KB)
2 Understanding the Silhouette Score.mp4 (4.05 MB)
5 Seeds and Distance Measures.mp4 (2.23 MB)
7 Hyperparameter Tuning - DBSCAN Clustering.mp4 (11.35 MB)
1 Module Overview.mp4 (1.06 MB)
2 Images as Matrices.mp4 (4.22 MB)
3 Exploring the MNIST Handwritten Digits Dataset.mp4 (5.43 MB)
4 Clustering Image Data.mp4 (8.8 MB)
5 Summary and Further Study.mp4 (1.65 MB)
1 Course Overview.mp4 (3.31 MB)
1 Module Overview.mp4 (1.55 MB)
2 Prerequisites and Course Outline.mp4 (2.18 MB)
3 Support for Neural Networks in scikit-learn.mp4 (7.65 MB)
4 Perceptrons and Neurons.mp4 (10.7 MB)
5 Multi-layer Perceptrons and Neural Networks.mp4 (4.91 MB)
6 Training a Neural Network.mp4 (8.34 MB)
7 Overfitting and Underfitting.mp4 (4.06 MB)
8 Module Summary.mp4 (1.79 MB)
1 Course Overview.mp4 (4.21 MB)
01 Module Overview.mp4 (1.36 MB)
03 The Curse of Dimensionality.mp4 (7.98 MB)
16 Summary.mp4 (1.49 MB)
01 Module Overview.mp4 (1.56 MB)
06 Demo - Implementing Factor Analysis.mp4 (13.18 MB)
10 Summary.mp4 (2.2 MB)
01 Module Overview.mp4 (1.02 MB)
11 Summary and Further Study.mp4 (2.05 MB)
1 Course Overview.mp4 (3.33 MB)
01 Module Overview.mp4 (1.82 MB)
02 Prerequisites and Course Outline.mp4 (2.33 MB)
03 A Quick Overview of Ensemble Learning.mp4 (9.68 MB)
05 Decision Trees in Ensemble Learning.mp4 (5.17 MB)
06 Understanding Decision Trees.mp4 (4.82 MB)
07 Overfitted Models and Ensemble Learning.mp4 (7.56 MB)
09 Exploring the Classification Dataset.mp4 (14.71 MB)
10 Hard Voting.mp4 (10.83 MB)
11 Soft Voting.mp4 (9 MB)
12 Module Summary.mp4 (1.93 MB)
01 Module Overview.mp4 (2.16 MB)
02 Bagging and Pasting.mp4 (7.98 MB)
04 Extra Trees.mp4 (4.64 MB)
05 Averaging vs Boosting.mp4 (3.3 MB)
12 Regression Using Extra Trees.mp4 (3.84 MB)
14 Module Summary.mp4 (1.8 MB)
1 Module Overview.mp4 (2.04 MB)
2 Adaptive Boosting (AdaBoost).mp4 (4.71 MB)
3 Regression Using AdaBoost.mp4 (12.38 MB)
4 Classification Using AdaBoost.mp4 (8.93 MB)
5 Gradient Boosting.mp4 (4.08 MB)
9 Module Summary.mp4 (1.61 MB)
1 Module Overview.mp4 (1.36 MB)
2 Stacking.mp4 (4.9 MB)
4 Summary and Further Study.mp4 (2.09 MB)
1 Course Overview.mp4 (3.24 MB)
01 Version Check.mp4 (581.33 KB)
02 Module Overview.mp4 (1.96 MB)
03 Prerequisites and Course Outline.mp4 (2.28 MB)
04 Scaling and Standardization.mp4 (7.04 MB)
05 Normalization.mp4 (4.27 MB)
10 Normalization and Cosine Similarity.mp4 (12.67 MB)
13 Module Summary.mp4 (1.75 MB)
01 Module Overview.mp4 (1.75 MB)
02 Outliers and Novelties.mp4 (4.74 MB)
04 Local Outlier Factor.mp4 (5.31 MB)
05 Elliptic Envelope.mp4 (4.78 MB)
06 Isolation Forest.mp4 (5.88 MB)
13 Module Summary.mp4 (1.73 MB)
01 Module Overview.mp4 (1.66 MB)
02 Representing Text Data in Numeric Form.mp4 (7.6 MB)
03 Bag-of-words and Bag-of-n-grams Models.mp4 (3.84 MB)
06 Vectorize Text Using Tf-Idf Scores.mp4 (6.65 MB)
07 Hashing for Dimensionality Reduction.mp4 (4.94 MB)
10 Module Summary.mp4 (1.95 MB)
1 Module Overview.mp4 (1.65 MB)
2 Representing Images as Matrices.mp4 (4.06 MB)
3 Feature Extraction from Images.mp4 (8.32 MB)
4 Extracting Patches from Image Data.mp4 (11.27 MB)
8 Module Summary.mp4 (1.89 MB)
1 Module Overview.mp4 (1.96 MB)
3 Exploring Internal Datasets.mp4 (18.67 MB)
5 Generating Manifold Data.mp4 (16.41 MB)
6 Module Summary.mp4 (1.64 MB)
1 Module Overview.mp4 (1.62 MB)
2 Support Vector Classifiers and the Kernel Trick.mp4 (5.84 MB)
3 Kernel Approximations.mp4 (10.8 MB)
4 Preparing Image Data.mp4 (9.72 MB)
7 Summary and Further Study.mp4 (2.78 MB)
1 Course Overview.mp4 (3.52 MB)
01 Version Check.mp4 (541.74 KB)
02 Module Overview.mp4 (1.92 MB)
03 Prerequisites and Course Outline.mp4 (2.09 MB)
04 Dimensions of Scaling.mp4 (2.78 MB)
05 Measuring Performance in Scaling.mp4 (9.45 MB)
06 Influence of Number of Features.mp4 (7.2 MB)
07 Influence of Feature Extraction Techniques.mp4 (6.41 MB)
08 Influence of Feature Representation.mp4 (4.39 MB)
11 Module Summary.mp4 (2.02 MB)
01 Module Overview.mp4 (2.08 MB)
04 Optimizations to Improve Prediction Latency.mp4 (10.48 MB)
05 Optimizations to Improve Prediction Throughput.mp4 (2.91 MB)
10 Module Summary.mp4 (2.02 MB)
1 Module Overview.mp4 (1.8 MB)
2 Streaming Data.mp4 (5.46 MB)
8 Module Summary.mp4 (1.9 MB)
01 Module Overview.mp4 (1.61 MB)
02 Parallelizing Computation Using Joblib.mp4 (6.5 MB)
03 Demo - Introducing Joblib.mp4 (7.87 MB)
04 Demo - Running Concurrent Workers Using Joblib.mp4 (8.9 MB)
06 Demo - Integrating Joblib with Dask ML.mp4 (7.37 MB)
07 Demo - Grid Search with Concurrent Workers.mp4 (5.92 MB)
09 Demo - Performing Multi-label Classification.mp4 (7.46 MB)
10 Module Summary.mp4 (1.52 MB)
1 Module Overview.mp4 (2.07 MB)
2 Integrating Apache Spark and scikit-learn.mp4 (7.25 MB)
3 Demo - Working with Spark Using spark-sklearn.mp4 (13.59 MB)
4 Demo - Working with Spark Using scikit-spark.mp4 (8.57 MB)
5 Summary and Further Study.mp4 (2.4 MB)
1 Course Overview.mp4 (2.82 MB)
1 Model Evaluation and Selection.mp4 (20.06 MB)
1 Introduction.mp4 (1.14 MB)
2 Classification Model Refresher.mp4 (1.62 MB)
3 Confusion Matrix.mp4 (2.92 MB)
5 Choosing the Right Metric.mp4 (7.16 MB)
6 ROC Curves and AUC.mp4 (6.17 MB)
7 Demo.mp4 (20.16 MB)
1 Introduction.mp4 (885 KB)
2 Regression Model Refresher.mp4 (1.98 MB)
4 Mean Absolute Error.mp4 (1.49 MB)
5 R-squared and Adjusted R-squared.mp4 (7.54 MB)
6 Choosing the Right Metric.mp4 (3.08 MB)
7 Demo.mp4 (8.78 MB)
8 Summary.mp4 (799.58 KB)
1 Model Selection Techniques.mp4 (26.55 MB)
1 Revisiting the Data Scientists Dilemma.mp4 (2.81 MB)
2 Model Evaluation Methods.mp4 (2.67 MB)
3 Model Selection Techniques.mp4 (2.27 MB)
4 Demo - Using the Patient Dataset.mp4 (9.9 MB)
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