Book Content
chapters • 15h8m total length
1. Giving Computers the Ability to Learn from Data
2. Training Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using Scikit-Learn
4. Building Good Training Sets – Data Pre-Processing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning To Sentiment Analysis
9. Embedding a Machine Learning Model into a Web Application
10. Predicting Continuous Target Variables with Regression Analysis
11. Working with Unlabeled Data – Clustering Analysis
12. Training Artificial Neural Networks for Image Recognition
13. Parallelizing Neural Network Training via Theano
