Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases

Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas.

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Key Features

• Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling
• Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions
• Implement ML models, such as neural networks and linear and logistic regression, from scratch

Book Description

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.

What you will learn

• Follow machine learning best practices throughout data preparation and model development
• Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning
• Develop and fine-tune neural networks using TensorFlow and PyTorch
• Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP
• Build classifiers using support vector machines (SVMs) and boost performance with PCA
• Avoid overfitting using regularization, feature selection, and more

Who this book is for

This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.

Table of Contents

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

Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.