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Key benefits

Dive into machine learning algorithms to solve the complex challenges faced by data scientists today

Explore cutting edge content reflecting deep learning and reinforcement learning developments

Use updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-end

Description

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.

Who is this book for?

If you’re a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.

What you will learn

Understand the important concepts in ML and data science

Use Python to explore the world of data mining and analytics

Scale up model training using varied data complexities with Apache Spark

Delve deep into text analysis and NLP using Python libraries such NLTK and Gensim

Select and build an ML model and evaluate and optimize its performance

Implement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learn

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Annie Jun 19, 2021

The concepts are explained clearly and step by step. Machine learning is not an easy topic, I find that this book is really helpful

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Brandon Dec 01, 2020

The book is a great practical resource for those interested in applying ML techniques quickly. As other reviewers have mentioned, it is a bit light on theory and the more technical aspects of ML until you get deeper into the book. Having said that, the examples and code provided are very practical and to the point. I would recommend it if you are either already familiar with basic ML theory and the math behind it or if you have a software engineering background and are simply looking to quickly implement an ML solution. The author walks you through code segments and explains each step and by the end, you will have covered most of the more popular ML models and know which ones to use given your data and your goals.The chapters on SVMs and building and predicting ad-clicks are very practical. I like how the author walks through each model type and compares them so you have a baseline and can see the differences in implementation and result. That was a helpful exercise spread across a few chapters. Also, the Best Practices chapter near the end was a good, "I need an answer quickly" kind of reference.Overall, I liked the book and think it would be helpful from a more practical perspective. I think this book paired with another more theoretical resource would really round out those seeking to learn ML.

crystalattice Nov 23, 2020

Python ML By Example (BE) is a good complement to Python ML Third Edition (3E). The 3E book focuses on the theory and general application of ML programming, while the BE book focuses an specific application examples.While they both tackle ML programming, their approach is different. The BE book assumes you have a reasonable, foundational background in ML and uses that basis to create specific ML-based applications.For example, whereas 3E has a simple note about Naïve Bayes classification, the BE book has a whole chapter dedicated to the algorithm, discussing the different types of classification methods, how Naïve Bayes works, and then actually implementing a Naïve Bayes application. On the flip side, the 3E book has a whole chapter dedicated just to the different classifiers and different implementations of them using scikit-learn.It's almost like the 3E book is a textbook and the BE book is its complementary workbook for practice. While you may be able to be successful with either one, combining them really maximizes your ML learning.To speak about the BE book in more detail, the topics covered include:*Introduction to Python ML, including software installation*Using Naïve Bayes algorithm to create movie recommendation application*Using SVM for facial recognition*Using tree-based algorithms to predict ad click-through*Using Apache Spark to work with large data sets*Using regression algorithms and neural networks to predict the stock market*Using text analysis and NLP to data mine newsgroups*Using unsupervised learning models to identify newsgroups topics*Using different types of neural networks for different types of analysis approaches*Using reinforcement learning for decision making*ML best practicesIt is a long book (nearly 500 pages), but the material is invaluable for anyone in the ML field, especially if you don't have a lot of experience with the different algorithms. And in conjunction with 3E, you almost have a complete ML curriculum.

Edward C. Charbonnet Dec 07, 2020

I am one of those who cannot study theory without also actually working on practical examples. This book gives an excellent intro into history and theory and the pros and cons - mostly pros. Then it goes into real practical examples including access to code and libraries. I've only read parts of the book and found it very imformative and loved the clear examples. ML is not my current area of focus so I'll likely postpone going in deeper for now but very glad I read all of chapter 1 and the first parts of 2 & 3. So be aware of the limitations of my review but for someone ready to go deeper (especially beginners), I highly recommend looking into the very interesting topics of the other chapters. They focus deeply on very specific applications.

Raidenv Mar 01, 2024

I'm looking forward to the latest and updated edition which covers the rise GPT 4.0 and Foundation models.This book is a nonsense , straightforward approach.Yes in some parts its a little outdated however , if you debug and correct it you can get it working.This is a book with code in it , the game changes quickly.

Python Machine Learning by Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn , Third Edition

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Python Machine Learning by Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn , Third Edition

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