Python Machine Learning By Example : Unlock machine learning best practices with real-world use cases (4 Revised edition)

Learn machine learning (ML) with this hands-on guide from best-selling author and ex-Google ML engineer Yuxi (Hayden) Liu.

He teaches the basics of ML algorithms to NLP transformers and multimodal models with best practice tips and real-world examplesKey FeaturesNew and updated content on NLP transformers, PyTorch, and computer vision modelingBest practices have expanded beyond one chapter with tips to improve your ML solutions showcased throughout the bookImplement ML algorithms, such as neural networks and decision trees from scratchBook DescriptionThe fourth edition of Python Machine Learning by Example is a comprehensive guide for beginners and experienced ML practitioners who want to learn more advanced techniques like multimodal modeling.

Written by best-selling author and ex-Google ML engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for ML engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on NLP transformers with BERT and GPT-4 and multimodal computer vision models with PyTorch and Hugging Face.

You’ll learn advanced modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This book navigates through complex challenges, bridging the gap between theoretical understanding and practical application.

Elevate your ML expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.What you will learnFollow machine learning best practices for data preparation, model training, and evaluationBuild and improve image classifiers using CNNs, transfer learning, and data augmentationBuild and fine-tune neural networks using TensorFlow and PyTorch for stock price prediction and image searchAnalyze sequence data and make predictions using RNNs and transformersBuild classifiers using SVMs and boost performance with principal component analysisLearn to avoid overfitting using cross-validation, regularization, feature selection, and dimensionality reductionWho this book is forThis expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python knowledge.

The real-world lessons and code prepare anyone undertaking their first serious ML project.