If you're comfortable with Python and want a practical path into machine learning, this book is a friendly, example-driven guide. It focuses on scikit-learn while introducing the supporting ecosystem (NumPy, pandas, SpaCy, imbalanced-learn, and more).
"The book is the perfect read for anyone who wants to transition into machine learning. It broadly covers all the key algorithms with an insightful practitioner's perspective."
Get the book here: books2read.com/u/m2ZW8d
Here are a few example reviews.
Ali Faizan rated it: 5 out of 5 stars.
"For a machine learning noob like me, it was pleasing to see that the book did not dive straight into the nitty-gritty of machine learning algorithms: it first established the raison d’être for machine learning and cohesively captured the whole gamut of developing a machine learning model. This helped me quite a bit to understand the bigger picture later on in the book where it demonstrated the practical use of various machine learning algorithms. I'll happily recommend this book to anyone interested in scikit-learn, and machine learning in general too."
Paul Schmidt rated it: 5 out of 5 stars.
"This book is information rich with practical examples. I whom never read or touched this area was surprised to learn the weight that data analysis had on machine learning. Yes, this book also teaches you about data analysis. Throughout the chapters you learn what not to do when building machine learning and deep learning models. The author teaches you what not to do by analysing the data at hand and improving the models upon that knowledge. The book is very information rich and can easily be reread from chapter to chapter. There are some things to keep in mind, this book is not for Python beginners and I urge you to know some of the basics from the pandas and matplotlib modules. In other words this book is strongly recommended."
Przemyslaw Chojecki rated it: 5 out of 5 stars.
"If you've already done a couple of data science projects, had a basic understanding of Python, did some visualisation and want to go deeper into some details of what it means to analyse data, then this book is for you. This is a practical guide to both supervised and unsupervised learning with plenty of examples in code. The main focus is on imperfect data and how to make sense of these imperfections through various machine learning algorithms. The author discusses standard data science algorithms using scikit-learn library which gives a coherent overview of the subject. You will learn decision trees, KNN classification, Naive Bayes and much more; applied to classical datasets like Iris dataset, Boston housing prices or Fashion-MNIST. Recommended for beginning data scientists!"
Adam Powell rated it: 5 out of 5 stars.
"The perfect read for an analyst that wants to transition into machine learning. It broadly covers all the key algorithms with an insightful practitioner's perspective. Highly recommended!"
DigitalSreeni: Book Review - Machine Learning with scikit-learn and scientific python toolkits
Dimitri Bianco: Hands-On Machine Learning with scikit-learn and Scientific Python Toolkit
The book has 13 chapters and is written for ML practitioners who want a clear, practical path. You will build intuition, learn the workflow end to end, and leave with code patterns you can reuse in real projects.
You will cover:
"Hands-On Machine Learning with Scikit-Learn" is generally well-regarded in the machine learning community. It is known for its practical approach, providing readers with hands-on examples and exercises using the Scikit-Learn library.
The book covers fundamental concepts and techniques in machine learning, making it suitable for beginners and intermediate learners. It is often praised for its clear explanations and code examples that help readers understand and apply machine learning algorithms effectively.
Start your machine learning journey by visiting this link*
Links to Amazon are affiliate links.
Tarek Amr