This resource is packed with clear explanations, visualizations, and examples on topics like training machine learning classifiers, building neural networks, transformers, and boosting algorithms on images, text, and more. It also covers advanced topics such as GANs, reinforcement learning, graphical neural networks, and large-scale transformers used for natural language processing.

This book is an indispensable resource for developers and data scientists who have Python fundamentals and want to get into machine learning and deep learning, or who want to deepen their knowledge in these areas. Readers are expected to have a good understanding of mathematics before starting, particularly a foundation in calculus and linear algebra.

Contents:
1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. Tour of Machine Learning Classifiers Using Scikit-Learn
4. Creating Good Training Datasets – Data Preprocessing
5. Compressing Data via Dimension Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Community Learning
8. Applying Machine Learning to Sentiment Analysis
9. Predicting Continuous Target Variables with Regression Analysis
10. Working with Unlabeled Data - Cluster Analysis

Book Link: Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python: Raschka, Sebastian, Liu, Yuxi (Hayden), Mirjalili, Vahid, Dzhulgakov, Dmytro: 9781801819312: Amazon.com: Books