Book Content
chapters • 26h36m total length
1. Machine Learning Model Fundamentals
2. Loss functions and Regularization
3. Introduction to Semi-Supervised Learning
4. Advanced Semi-Supervised Classifiation
5. Graph-based Semi-Supervised Learning
6. Clustering and Unsupervised Models
7. Advanced Clustering and Unsupervised Models
8. Clustering and Unsupervised Models for Marketing
9. Generalized Linear Models and Regression
10. Introduction to Time-Series Analysis
11. Bayesian Networks and Hidden Markov Models
12. The EM Algorithm
13. Component Analysis and Dimensionality Reduction
14. Hebbian Learning
15. Fundamentals of Ensemble Learning
16. Advanced Boosting Algorithms
17. Modeling Neural Networks
18. Optimizing Neural Networks
19. Deep Convolutional Networks
20. Recurrent Neural Networks
21. Auto-Encoders
22. Introduction to Generative Adversarial Networks
23. Deep Belief Networks
24. Introduction to Reinforcement Learning
25. Advanced Policy Estimation Algorithms
