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 DescriptionThe 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.

*Email sign-up and proof of purchase requiredWhat 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 forThis 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.