Python Machine Learning By Example
Unlock machine learning best practices with real-world use cases
4th Edition
Published on 31. July 2024
Book
Paperback/Softback
526 pages
978-1-83508-562-2 (ISBN)
Description
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.
Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free
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.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 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.
Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free
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.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 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.
More details
Edition
4th Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
College/higher education
Professional and scholarly
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 29 mm
Weight
971 gr
ISBN-13
978-1-83508-562-2 (9781835085622)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Yuxi (Hayden) Liu Liu, Yuxi (Hayden)
Python Machine Learning By Example
Unlock machine learning best practices with real-world use cases
E-Book
07/2024
4th Edition
Packt Publishing Limited
from
€33.59
Available for download
Person
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.
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.
Content
Table of Contents
Getting Started with Machine Learning and Python
Building a Movie Recommendation Engine
Predicting Online Ad Click-Through with Tree-Based Algorithms
Predicting Online Ad Click-Through with Logistic Regression
Predicting Stock Prices with Regression Algorithms
Predicting Stock Prices with Artificial Neural Networks
Mining the 20 Newsgroups Dataset with Text Analysis Techniques
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
Recognizing Faces with Support Vector Machine
Machine Learning Best Practices
Categorizing Images of Clothing with Convolutional Neural Networks
Making Predictions with Sequences Using Recurrent Neural Networks
Advancing Language Understanding and Generation with Transformer Models
Building An Image Search Engine Using Multimodal Models
Making Decisions in Complex Environments with Reinforcement Learning
Getting Started with Machine Learning and Python
Building a Movie Recommendation Engine
Predicting Online Ad Click-Through with Tree-Based Algorithms
Predicting Online Ad Click-Through with Logistic Regression
Predicting Stock Prices with Regression Algorithms
Predicting Stock Prices with Artificial Neural Networks
Mining the 20 Newsgroups Dataset with Text Analysis Techniques
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
Recognizing Faces with Support Vector Machine
Machine Learning Best Practices
Categorizing Images of Clothing with Convolutional Neural Networks
Making Predictions with Sequences Using Recurrent Neural Networks
Advancing Language Understanding and Generation with Transformer Models
Building An Image Search Engine Using Multimodal Models
Making Decisions in Complex Environments with Reinforcement Learning