Machine Learning Study 01

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition

Overview

• Use Scikit-learn to track an example ML project end to end
• Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
• Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
• Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
• Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

Chapters

1. The Machine Learning Landscape
2. End-to-End Machine Learning Project
3. Classification
4. Training Models
5. Support Vector Machines
6. Decision Trees
7. Ensemble Learning and Random Forests
8. Dimensionality Reduction
9. Unsupervised Learning Techniques
10. Introduction to Artificial Neural Networks with Keras
11. Training Deep Neural Networks
12. Custom Models and Training with TensorFlow
13. Loading and Preprocessing Data with TensorFlow
14. Deep Computer Vision Using Convolutional Neural Networks
15. Processing Sequences Using RNNs and CNNs
16. Natural Language Processing with RNNs and Attention
17. Autoencoders, GANs, and Diffusion Models
18. Reinforcement Learning
19. Training and Deploying TensorFlow Models at Scale