Python Machine Learning By Example, Fourth Edition

eBook Details:

• Paperback: 518 pages
• Publisher: WOW! eBook; 4th edition (July 31, 2024)
• Language: English
• ISBN-10: 1835085628
• ISBN-13: 978-1835085622

eBook Description:

Python Machine Learning By Example, Fourth Edition: 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.

The Python Machine Learning By Example, Fourth Edition 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.

• 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

This hands-on Python Machine Learning By Example, 4th Edition 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.