Advanced Predictive Techniques with Scikit-Learn and TensorFlow

Ensemble Methods for Regression and Classification 4 lectures 40:49 The Course Overview This video provides an overview of the entire course. How Ensemble Methods Work? Explain the general idea behind ensemble methods and discuss at a high level the intuition of the main ensemble methods - bagging, random forest and boosting. • Explain the general idea on which all ensemble methods are based • Explain at a high level the idea of bagging • Explain at a high level the idea of random forests and boosting Bagging, Random Forests, and Boosting for Regression Present with a practical example the procedure to build ensemble methods for regression tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train 3 different ensemble methods • Compare the results of the models and show the performance of the different methods Bagging, Random Forests, and Boosting for Classification Present with a practical example the procedure to build ensemble methods for classification tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train three different ensemble methods • Compare the results of the models and show the performance of the different methods Ensemble Methods for Regression and Classification 4 lectures 40:49 The Course Overview This video provides an overview of the entire course. How Ensemble Methods Work? Explain the general idea behind ensemble methods and discuss at a high level the intuition of the main ensemble methods - bagging, random forest and boosting. • Explain the general idea on which all ensemble methods are based • Explain at a high level the idea of bagging • Explain at a high level the idea of random forests and boosting Bagging, Random Forests, and Boosting for Regression Present with a practical example the procedure to build ensemble methods for regression tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train 3 different ensemble methods • Compare the results of the models and show the performance of the different methods Bagging, Random Forests, and Boosting for Classification Present with a practical example the procedure to build ensemble methods for classification tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train three different ensemble methods • Compare the results of the models and show the performance of the different methods The Course Overview This video provides an overview of the entire course. The Course Overview This video provides an overview of the entire course. The Course Overview This video provides an overview of the entire course. The Course Overview This video provides an overview of the entire course. This video provides an overview of the entire course. This video provides an overview of the entire course. How Ensemble Methods Work? Explain the general idea behind ensemble methods and discuss at a high level the intuition of the main ensemble methods - bagging, random forest and boosting. • Explain the general idea on which all ensemble methods are based • Explain at a high level the idea of bagging • Explain at a high level the idea of random forests and boosting How Ensemble Methods Work? Explain the general idea behind ensemble methods and discuss at a high level the intuition of the main ensemble methods - bagging, random forest and boosting. • Explain the general idea on which all ensemble methods are based • Explain at a high level the idea of bagging • Explain at a high level the idea of random forests and boosting How Ensemble Methods Work? Explain the general idea behind ensemble methods and discuss at a high level the intuition of the main ensemble methods - bagging, random forest and boosting. • Explain the general idea on which all ensemble methods are based • Explain at a high level the idea of bagging • Explain at a high level the idea of random forests and boosting How Ensemble Methods Work? Explain the general idea behind ensemble methods and discuss at a high level the intuition of the main ensemble methods - bagging, random forest and boosting. • Explain the general idea on which all ensemble methods are based • Explain at a high level the idea of bagging • Explain at a high level the idea of random forests and boosting Explain the general idea behind ensemble methods and discuss at a high level the intuition of the main ensemble methods - bagging, random forest and boosting. • Explain the general idea on which all ensemble methods are based • Explain at a high level the idea of bagging • Explain at a high level the idea of random forests and boosting Explain the general idea behind ensemble methods and discuss at a high level the intuition of the main ensemble methods - bagging, random forest and boosting. • Explain the general idea on which all ensemble methods are based • Explain at a high level the idea of bagging • Explain at a high level the idea of random forests and boosting Bagging, Random Forests, and Boosting for Regression Present with a practical example the procedure to build ensemble methods for regression tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train 3 different ensemble methods • Compare the results of the models and show the performance of the different methods Bagging, Random Forests, and Boosting for Regression Present with a practical example the procedure to build ensemble methods for regression tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train 3 different ensemble methods • Compare the results of the models and show the performance of the different methods Bagging, Random Forests, and Boosting for Regression Present with a practical example the procedure to build ensemble methods for regression tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train 3 different ensemble methods • Compare the results of the models and show the performance of the different methods Bagging, Random Forests, and Boosting for Regression Present with a practical example the procedure to build ensemble methods for regression tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train 3 different ensemble methods • Compare the results of the models and show the performance of the different methods Present with a practical example the procedure to build ensemble methods for regression tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train 3 different ensemble methods • Compare the results of the models and show the performance of the different methods Present with a practical example the procedure to build ensemble methods for regression tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train 3 different ensemble methods • Compare the results of the models and show the performance of the different methods Bagging, Random Forests, and Boosting for Classification Present with a practical example the procedure to build ensemble methods for classification tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train three different ensemble methods • Compare the results of the models and show the performance of the different methods Bagging, Random Forests, and Boosting for Classification Present with a practical example the procedure to build ensemble methods for classification tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train three different ensemble methods • Compare the results of the models and show the performance of the different methods Bagging, Random Forests, and Boosting for Classification Present with a practical example the procedure to build ensemble methods for classification tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train three different ensemble methods • Compare the results of the models and show the performance of the different methods Bagging, Random Forests, and Boosting for Classification Present with a practical example the procedure to build ensemble methods for classification tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train three different ensemble methods • Compare the results of the models and show the performance of the different methods Present with a practical example the procedure to build ensemble methods for classification tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train three different ensemble methods • Compare the results of the models and show the performance of the different methods Present with a practical example the procedure to build ensemble methods for classification tasks and compare the results of ensemble methods with other simpler methods. • Present the dataset to be used in the example • Build and train three different ensemble methods • Compare the results of the models and show the performance of the different methods Cross-Validation and Parameter Tuning 3 lectures 31:12 K-fold Cross-Validation Explain the main problem with hold out cross validation and explain the approach of K-fold cross-validation to solve this problem. Present how to do K-fold cross validation in scikit-learn • Give a little review about the need for cross-validation • Explain the main problem with hold out cross-validation • Explain how k-fold cross validation works and show how to do it in scikit-learn Comparing Models with K-fold Cross-Validation Show how k-fold cross-validation can be used to get a better assessment of the performance of the models and hence to make better model comparison. • Build and train three models on the same dataset • Use k-fold cross-validation to get the performance metrics of the models • Use the results to compare the 3 trained models Hyper-Parameter Tuning in scikit-learn Explain the need for hyper-parameter tuning when building predictive analytics models and show how we can combine k-fold cross-validation with grid search to do it. • Explain the need for hyper-parameter tuning • Show who to use grid search with k-fold cross-validation for hyper-parameter tuning • Show how to tune hyper-parameters with a practical example Cross-Validation and Parameter Tuning. 3 lectures 31:12 K-fold Cross-Validation Explain the main problem with hold out cross validation and explain the approach of K-fold cross-validation to solve this problem. Present how to do K-fold cross validation in scikit-learn • Give a little review about the need for cross-validation • Explain the main problem with hold out cross-validation • Explain how k-fold cross validation works and show how to do it in scikit-learn Comparing Models with K-fold Cross-Validation Show how k-fold cross-validation can be used to get a better assessment of the performance of the models and hence to make better model comparison. • Build and train three models on the same dataset • Use k-fold cross-validation to get the performance metrics of the models • Use the results to compare the 3 trained models Hyper-Parameter Tuning in scikit-learn Explain the need for hyper-parameter tuning when building predictive analytics models and show how we can combine k-fold cross-validation with grid search to do it. • Explain the need for hyper-parameter tuning • Show who to use grid search with k-fold cross-validation for hyper-parameter tuning • Show how to tune hyper-parameters with a practical example K-fold Cross-Validation Explain the main problem with hold out cross validation and explain the approach of K-fold cross-validation to solve this problem. Present how to do K-fold cross validation in scikit-learn • Give a little review about the need for cross-validation • Explain the main problem with hold out cross-validation • Explain how k-fold cross validation works and show how to do it in scikit-learn K-fold Cross-Validation Explain the main problem with hold out cross validation and explain the approach of K-fold cross-validation to solve this problem. Present how to do K-fold cross validation in scikit-learn • Give a little review about the need for cross-validation • Explain the main problem with hold out cross-validation • Explain how k-fold cross validation works and show how to do it in scikit-learn K-fold Cross-Validation Explain the main problem with hold out cross validation and explain the approach of K-fold cross-validation to solve this problem. Present how to do K-fold cross validation in scikit-learn • Give a little review about the need for cross-validation • Explain the main problem with hold out cross-validation • Explain how k-fold cross validation works and show how to do it in scikit-learn K-fold Cross-Validation Explain the main problem with hold out cross validation and explain the approach of K-fold cross-validation to solve this problem. Present how to do K-fold cross validation in scikit-learn • Give a little review about the need for cross-validation • Explain the main problem with hold out cross-validation • Explain how k-fold cross validation works and show how to do it in scikit-learn Explain the main problem with hold out cross validation and explain the approach of K-fold cross-validation to solve this problem. Present how to do K-fold cross validation in scikit-learn • Give a little review about the need for cross-validation • Explain the main problem with hold out cross-validation • Explain how k-fold cross validation works and show how to do it in scikit-learn Explain the main problem with hold out cross validation and explain the approach of K-fold cross-validation to solve this problem kit-learn. • Explain the need for dimensionality reduction • Explain the PCA method for dimensionality reduction • Show how to...