What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Key Benefits

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

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

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. *Email sign-up and proof of purchase required

Frequently bought together

₱2347.99

₱2653.99

₱2704.99

Total ₱ 7,706.97

Jacob Smith Sep 21, 2024

This book is an absolute gem for anyone looking to dive deep into the world of machine learning using Python! From the moment I opened it, I was impressed by the clear, concise explanations and the practical examples that make even the most complex topics easy to understand.The author does a fantastic job of breaking down key machine learning algorithms, explaining not just the "how" but the "why" behind each method. The inclusion of real-world datasets and hands-on exercises makes it easy to follow along and apply what you've learned immediately.

Amazon Verified review

Placeholder Oct 22, 2024

It will covers from the most basic concepts

Amazon Verified review

saandeep sreerambatla Jul 31, 2024

"Python Machine Learning by Example, Fourth Edition" by Yuxi (Hayden) Liu is a fantastic resource for anyone interested in machine learning, whether you're just starting out or already have some experience. This book strikes a great balance between explaining the theory behind machine learning and showing you how to apply it in real-world scenarios, making it an essential addition to any data scientist’s collection.The book is well-organized, kicking off with the basics of machine learning and Python programming. Liu does an excellent job of explaining why machine learning is so important today and then helps you set up your Python environment. This ensures that even those with minimal programming experience can keep up.What really stands out about this book is its hands-on approach. Each chapter is packed with real-world examples that help bring complex machine learning concepts to life. For instance, the chapters on building a movie recommendation engine with Naïve Bayes and predicting stock prices with regression algorithms are particularly insightful, showing you exactly how these models work and how to apply them to real problems.The book also covers advanced topics like deep learning, natural language processing (NLP), and reinforcement learning. The sections on convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) for sequence prediction are especially useful. They provide a deep dive into these advanced models, complete with code examples using TensorFlow and PyTorch, which are incredibly helpful for anyone looking to implement these techniques in their own projects.Another great feature of this book is the focus on best practices. Liu includes 21 best practices that cover the entire machine learning workflow, from data preparation to model deployment and monitoring. This is invaluable for anyone looking to build robust and scalable machine learning solutions.It's worth noting that the book assumes you have a basic understanding of Python and some familiarity with statistical concepts. This might be a bit challenging for complete beginners, but it doesn't take away from the overall value of the book. Instead, it sets a realistic expectation for the level of expertise needed to fully benefit from the content.In conclusion, "Python Machine Learning by Example, Fourth Edition" is an excellent resource that bridges the gap between theory and practice. Yuxi (Hayden) Liu's clear explanations, practical examples, and focus on best practices make this book a must-read for anyone serious about mastering machine learning with Python. Whether you're a data analyst, a machine learning engineer, or a data scientist, this book will provide you with the tools and knowledge you need to succeed.

Amazon Verified review

Thomas M. Aug 21, 2024

I highly recommend Liu's Python ML by Example! As a long term practitioner of all things analytics and data science, it was refreshing to come back to the foundations with this book. I wish I had this resource available when I was originally getting started in the field, as Liu has a knack for covering a broad range of salient topics in ML, while still offering plenty of depth for those looking to go into the weeds of how algorithms work. Super practical, this book focuses on real-life examples, spanning marketing & ads, content recommendations, text sentiment, image classification and beyond. The book also navigates tabular ML and deep learning concepts flawlessly. Liu doesn't stop at the fundamentals; the book also covers advanced topics like deep learning, natural language processing (NLP), and reinforcement learning. The sections on convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) for sequence prediction offer valuable insights into these cutting-edge techniques. These topics area all presented in ways that even new-to-ML readers would be able to grasp. These days, no ML book is complete without including GenAI as a topic, which the author integrates seamlessly. All around a super well rounded and practical read!

Amazon Verified review

Ayon Roy Sep 05, 2024

Starting my journey in machine learning was both exciting and overwhelming. I struggled to bridge the gap between theory and practical application in real-world projects. That’s why Yuxi Hayden Liu’s "Python Machine Learning by Example" has been a game-changer for me. This book offers a structured approach, making it easier to transition from learning to execution.Liu covers essential topics like overfitting, underfitting, and cross-validation right from the start, ensuring that you grasp the fundamentals. What truly sets this book apart is the hands-on projects that accompany each concept. From building a movie recommendation engine using Naive Bayes to predicting stock prices and exploring deep learning through artificial neural networks, Liu walks you through each step—from data preparation to model evaluation.The book is rich with best practices, such as feature engineering, algorithm selection, and monitoring model performance. By the end, you'll not only have a solid understanding of basic and advanced topics, including CNNs, transformer models, and reinforcement learning, but you’ll also feel confident applying them in real-world scenarios.Yuxi Hayden Liu’s industry experience shines through, making this book an invaluable guide for anyone feeling lost in their machine learning journey. Highly recommended for both students and professionals looking to elevate their skills. Happy reading!

Amazon Verified review

Python Machine Learning By Example , Fourth Edition

Unlock machine learning best practices with real-world use cases

What do you get with a Packt Subscription?

Key Benefits

What You Will Learn

Who Is This Book For?

Book Description

Frequently bought together

Recommendations for you

Customer reviews

Filter reviews by

People who bought this also bought

About the author

FAQs

Python Machine Learning By Example , Fourth Edition

Unlock machine learning best practices with real-world use cases

Packt Subscriptions

What do you get with a Packt Subscription?

Key Benefits

What You Will Learn

Who Is This Book For?

Book Description

Frequently bought together

Recommendations for you

Customer reviews

Filter reviews by

People who bought this also bought

About the author

FAQs

Create a Free Account To Continue Reading

Sign in to activate your 7-day free access