Book Content
chapters • 25h44m total length
1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using scikit-learn
4. Building Good Training Datasets – Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
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. Implementing a Multilayer Artificial Neural Network from Scratch
13. Parallelizing Neural Network Training with TensorFlow
14. Going Deeper – The Mechanics of TensorFlow
15. Classifying Images with Deep Convolutional Neural Networks
16. Modeling Sequential Data Using Recurrent Neural Networks
17. Generative Adversarial Networks for Synthesizing New Data
18. Reinforcement Learning for Decision Making in Complex Environments
