Book Content
chapters • 25h48m 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. Predicting Continuous Target Variables with Regression Analysis
10. Working with Unlabeled Data – Clustering Analysis
11. Implementing a Multilayer Artificial Neural Network from Scratch
12. Parallelizing Neural Network Training with PyTorch
13. Going Deeper – The Mechanics of PyTorch
14. Classifying Images with Deep Convolutional Neural Networks
15. Modeling Sequential Data Using Recurrent Neural Networks
16. Transformers – Improving Natural Language Processing with Attention Mechanisms
17. Generative Adversarial Networks for Synthesizing New Data
18. Graph Neural Networks for Capturing Dependencies in Graph Structured Data
19. Reinforcement Learning for Decision Making in Complex Environments
