This book is quite good and full of wonderful examples that can be used to help you create a machine learning app of your own. I have some experience and understanding of the fundamentals, so it’s a little tough to say if this adequately covers the basics. For writing the apps in python, yes. It has the underlying explanation of how the things work, and this comes before the software, so it helps develop a good understanding of what is actually happening. It’s a good book and I definitely recommend it.

Excellent resource and walk-through. We'll done.

Excellent book

I am starting my Python journey with Machine Learning. I am new to Python. What I like so far from the book it explains the task at hand before you begin to code. Learning concepts and background and not coding alone helps reinforce the learning.

Online you might find code snippets but no explanation to why or use. That is helpful if you're a seasoned coder looking for help.

If you really want to learn purpose of ML and apply to real world application and code along this book is right.

Good book, well written, great examples, great concepts.

As someone who has spent the last decade driving data-centric transformation across healthcare, life sciences, and enterprise ecosystems, Python Machine Learning by Example is more than just a technical manual- it’s a deeply practical, thoughtfully structured, and rigorously engineered guide for anyone looking to master applied machine learning using Python.

What makes this book stand out- especially in its fourth edition- is its relentless focus on real-world applicability. This isn’t just about tuning hyperparameters or explaining algorithms in isolation. Yuxi Liu walks the reader through full-scale business scenarios- ranging from movie recommendation engines to stock price forecasting and reinforcement learning simulations. For practitioners like me who live in the intersection of data, business impact, and stakeholder alignment, the value here is enormous.

A Pragmatic Dive into Machine Learning

Each chapter builds not just on complexity, but on context. For instance, the use of Naïve Bayes for movie recommendations isn’t just pedagogical- it reflects how streaming services have historically leveraged collaborative filtering with Bayesian thinking. Similarly, the chapters on marketing mix modeling and classification-based use cases mirror the very solutions I’ve led for global biopharma clients, optimizing promotional ROI for blockbuster drugs.

The chapters on neural networks and CNNs elegantly deconstruct the intricacies of image recognition and text classification. I particularly appreciated the inclusion of advanced chapters on Transformer models, BERT, GPT, and CLIP- a nod to the explosive progress we've seen in natural language understanding and multimodal AI. These additions keep the book firmly rooted in the now while offering a clear path from foundational to frontier.

Code as a Learning Companion

The codebase accompanying the book is clean, modular, and incredibly usable. Having built predictive engines and patient-finding algorithms across therapeutic areas like SCD and ATTR-CM, I found the practical implementations in the book aligned with best practices used in production-grade systems. The TensorFlow and PyTorch chapters are especially valuable- striking a rare balance between clarity and depth.

In fact, the section on RNNs for sequential prediction felt like an echo of the early warning systems I helped design- flagging patients at risk of discontinuing therapy. These parallels made the book feel less like a tutorial and more like a mirror to real-world analytical problem-solving.

A Blueprint for ML Maturity

Beyond technical accuracy, what I truly value is the emphasis on end-to-end ML maturity- data preprocessing, feature engineering, model tuning, cross-validation, interpretability, deployment, and monitoring. These are the gears often overlooked by theoretical texts, but indispensable in enterprise-scale environments. The final chapters on best practices read like a checklist I would give to my consulting teams while onboarding new client engagements.

For data scientists aspiring to scale their impact from code to boardroom insights, this book provides both the technical fluency and architectural perspective to do so.

If you're a data practitioner seeking practical fluency, a consultant designing ML-driven strategy, or a product owner aiming to translate data into decisions, this book is must-read. It’s not just Python by example - it’s machine learning by experience. And in a world where AI is reshaping everything from marketing to medicine, Liu’s work gives you the blueprint to not just keep up - but to lead.

In the fast-paced world of machine learning, staying ahead requires continuous learning and hands-on experience. Python Machine Learning by Example (Fourth Edition) delivers a practical approach to learning and applying machine learning techniques through engaging real-world projects. The book is an invaluable resource for developers, data scientists, and anyone interested in mastering the intricacies of Python for machine learning.

As a technology professional with a focus on cloud-based data platforms, I found the book to be particularly insightful in bridging the gap between theoretical concepts and practical applications. With experience in building scalable data systems using tools like Snowflake and Python, I appreciated how this book emphasizes not only algorithmic understanding but also real-world applications, such as recommendation engines and predictive modeling.

1. Hands-on Learning with Real-World Projects: The book guides readers through a variety of practical projects, including building a movie recommendation system and predicting stock prices. These examples resonate with my own experience in designing systems where real-world use cases drive learning and optimization.

2. Comprehensive Coverage of Machine Learning Algorithms: From traditional algorithms like Naïve Bayes and Logistic Regression to modern techniques like Transformers and Reinforcement Learning, the book provides a broad spectrum of machine learning algorithms. It aligns well with my work in AI-driven automation, helping bridge gaps in understanding complex AI models for real-time decision-making.

3. Advanced Techniques with Python: The book leverages Python and essential libraries (e.g., TensorFlow, sci-kit-learn, PyTorch) to demonstrate machine learning models. As someone who has worked on automating data workflows and analytics, I found the use of Python frameworks particularly beneficial for quick prototyping and experimentation.

4. Practical Insights into Model Optimization: The book doesn’t just teach algorithms but also delves deep into the nuances of optimizing machine learning models. From hyperparameter tuning to cross-validation, it offers a comprehensive toolkit for ensuring high-quality predictions, which resonates with my experience in performance optimization and data pipeline management.

For professionals in data science, machine learning, and AI-driven automation, Python Machine Learning by Example offers a perfect blend of theory and practice. The practical examples and clear explanations make it an excellent resource for beginners and experienced practitioners looking to refine their skills. As someone specializing in optimizing cloud-based data systems, I can attest to the importance of understanding machine learning's role in modern data architecture. This book serves as a strategic guide for staying ahead in the field.

If you are looking to dive deeper into machine learning with Python, I highly recommend this book. Its blend of clear explanations and hands-on projects will equip you with the knowledge needed to tackle complex challenges in machine learning.

This guide stands out by bridging theory with hands-on implementation, featuring best practices from an ex-Google ML engineer that may benefit practitioners at all levels.

Here are the key takeaways:

🔮 Stock Price Prediction Implementation: Chapters 5 and 6 offer complementary approaches to the same problem - first using traditional regression algorithms, then implementing ANNs. Early career professionals will appreciate how the author contrasts these methods on the same dataset, demonstrating when simpler models might suffice v/s when Deep Learning provides meaningful improvements.

🔍 Image Search Engine with Multimodal Models: Chapter 14 provides a thorough walkthrough of building an image search engine using CLIP (Contrastive Language-Image Pre-training), which folks can incorporate into projects requiring cross-modal retrieval systems.

🤖 Transformer Models for NLP: Chapter 13 demystifies transformer architecture by implementing and fine-tuning BERT and GPT models for practical language tasks. College students will find the step-by-step explanations particularly valuable as the chapter progresses from fundamentals to advanced applications, including how the attention mechanism works and best practices for fine-tuning.

👕 CNNs for Image Classification: Chapter 11 on "Categorizing Images of Clothing" offers a practical demonstration of CNNs with PyTorch. The chapter builds from basic CNN concepts to transfer learning techniques, making it an excellent resource for practitioners looking to efficiently implement CV tasks with modern frameworks.

📊 Machine Learning Best Practices: Chapter 10 is dedicated entirely to best practices throughout the ML lifecycle - from data prep to model deployment. This standalone chapter, complemented by tips throughout the book, addresses common pitfalls like overfitting through practical techniques including regularization and feature selection.

I like that this book doesn't beat around the bush and immediately jumps into examples starting from Chapter 2. The combination of well-documented code examples, real-world use cases, and best practices makes this a pretty practical encyclopedia of learning about different ML approaches in action.

interesting book, delivered in time and in good conditions

This book does a great job of teaching machine learning by breaking down the math behind the algorithms and pairing it with clear, practical examples. I appreciate how it goes beyond just showing how things work—it really helps you understand why they work. You can tell the author has a lot of experience, and it shows throughout the book. Well done!

It will covers from the most basic concepts

Very good, very practical

Very useful book