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Python Machine Learning By Example Unlock machine learning

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Description

English | 2024 | ISBN: 9781835085622 | 757 pages | True EPUB | 23.11 MB

Python Machine Learning By Example:
Unlock machine learning best practices with real-world use cases 4th Edition

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

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

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