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Machine Learning with Python: A Practical Beginners’ Guide
As the second title in the Machine Learning From Scratch series, this book teaches you how to code machine learning models in Python.
By working on different projects with repeatable steps, you will have the blueprints and the effective strategies to code and design prediction models using your own data.
Who is this book for?
The book is designed for beginners with basic background knowledge of machine learning, including common algorithms such as logistic regression and decision trees. For a gentle explanation of machine learning theory minus the code, we suggest reading the first book in this series Machine Learning for Absolute Beginners (Third Edition) , which is written for a more general audience. • How to code a machine learning prediction model using a range of algorithms including logistic regression, gradient boosting, and decision trees.
• How to install a development environment and use the programming language Python to code 10 different models.
• How to write your model in the least amount of code possible with the help of Pandas, Scikit-learn, Matplotlib, and Seaborn.
• How to visualize relationships in your dataset including Heatmaps and Pairplots with just a few lines of code.
By working on different projects with repeatable steps, you will have the blueprints and the effective strategies to code and design prediction models using your own data.
Who is this book for?
The book is designed for beginners with basic background knowledge of machine learning, including common algorithms such as logistic regression and decision trees. For a gentle explanation of machine learning theory minus the code, we suggest reading the first book in this series Machine Learning for Absolute Beginners (Third Edition) , which is written for a more general audience. • How to code a machine learning prediction model using a range of algorithms including logistic regression, gradient boosting, and decision trees.
• How to install a development environment and use the programming language Python to code 10 different models.
• How to write your model in the least amount of code possible with the help of Pandas, Scikit-learn, Matplotlib, and Seaborn.
• How to visualize relationships in your dataset including Heatmaps and Pairplots with just a few lines of code.
186 pages, Hardcover
Published October 8, 2021
139 people are currently reading
52 people want to read
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Displaying 1 - 4 of 4 reviews
December 16, 2021
Good but same old
The book contained good information and covered a range of methods. Unfortunately, the content isn’t any different from what can easily be found and easier learned from a YouTube video. Overall, good content but nothing special.
The book contained good information and covered a range of methods. Unfortunately, the content isn’t any different from what can easily be found and easier learned from a YouTube video. Overall, good content but nothing special.
October 30, 2025
This book is a solid, hands-on guide for anyone looking to get practical with ML. I appreciated how it balances explanations of the concepts with real Python examples, making it easier to connect theory with practice. Working through the exercises helped me understand how algorithms behave in the real world, and the book reinforced the importance of experimenting and iterating when building models. It’s approachable, practical, and a resource I’ll keep coming back to as I continue learning.
January 24, 2021
Good Start for beginners
I was interested in a book which approached the topic using python. this book does it very well with examples. Although I am from ML major many moons ago, it use to be C or C++ during my college years. thus I found that the author did a great job introducing ML and how it was executed in Python.
I was interested in a book which approached the topic using python. this book does it very well with examples. Although I am from ML major many moons ago, it use to be C or C++ during my college years. thus I found that the author did a great job introducing ML and how it was executed in Python.
August 20, 2020
Pretty simple coding, yet very effective. By the end of it you will know how to build most of the basic machine learning codes.
Read while implementing in just 3 days! Coupled with the first book I now feel that I have a much more solid base to build upon for my future Machine Learning knowledge.
Read while implementing in just 3 days! Coupled with the first book I now feel that I have a much more solid base to build upon for my future Machine Learning knowledge.
Displaying 1 - 4 of 4 reviews