The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts.

Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.

This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.

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Spis treści

Python Machine Learning By Example. Unlock machine learning best practices with real-world use cases - Fourth Edition eBook -- spis treści

• 1. Getting Started with Machine Learning and Python
• 2. Building a Movie Recommendation Engine
• 3. Predicting Online Ad Click-Through with Tree-Based Algorithms
• 4. Predicting Online Ad Click-Through with Logistic Regression
• 5. Predicting Stock Prices with Regression Algorithms
• 6. Predicting Stock Prices with Artificial Neural Networks
• 7. Mining the 20 Newsgroups Dataset with Text Analysis Techniques
• 8. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
• 9. Recognizing Faces with Support Vector Machine
• 10. Machine Learning Best Practices
• 11. Categorizing Images of Clothing with Convolutional Neural Networks
• 12. Making Predictions with Sequences Using Recurrent Neural Networks
• 13. Advancing Language Understanding and Generation with Transformer Models
• 14. Building An Image Search Engine Using Multimodal Models
• 15. Making Decisions in Complex Environments with Reinforcement Learning