Review of Introduction to Machine Learning with Python
The following is a review of the book Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Muller and Sarah Guido.
Introduction to Machine Learning with Python is a very practical book, oriented for readers who are comfortable programming in Python, and that want to learn Machine learning in a practical way, sliding away from heavy maths and complex theory.
It’s main focus is to teach programmers how to build Machine Learning applications using Scikit-learn, Pandas, Numpy and Matplotlib, in a way that is easy to follow and very hands-on, while briefly discussing the main concepts and terminology behind the Machine Learning algorithms it discusses.
If you are a developer that wants to learn to implement Machine learning algorithms, and create awesome applications, without diving deep into the complexity of the field, then this is the perfect book for you. Introduction to Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.
Apart from that, you will not only learn how to code and implement Machine Learning algorithms but also when a certain business problem demands or can be improved with ML, the whole workflow of a ML project: pre-processing, training, evaluating and implementing into production.
The book comes with a lot of code and many very useful examples of actual applications that have Machine Learning at its core. The book is really nicely written and patiently teaches you all you need to know to code ML applications, with no requirements of previous Artificial Intelligence/Machine Learning knowledge.
You can find the contents of the book, divided by sections in this link.
Like we mentioned earlier, this book assumes you are already familiar with programming in Python, and that you have experience with Pandas and Numpy at least. If you don’t check out one of the books in our Data Analysis section to learn to program in Python while mastering these libraries.
These two books are really good precursors of Introduction to Machine Learning with Python:
Who is this book for?
We would recommend this book to developers and semi-advanced Python programmers who would like to learn to do cool stuff with data, like building predictive models, or clustering segmentation. If you want to learn TO DO Machine Learning this is the book for you, specially if you have no or very little mathematical and statistical background.
If you are looking for a book to build to the hype that you have about Machine Learning and Artificial Neural networks and make you an expert in the field, then Introduction to Machine Learning with Python is probably not the best suit for your.
Introduction to Machine Learning with Python is a very nice resource for learning to code Machine Learning applications, in an efficient, clean, and structured manner. It will not make you an expert in any of the covered Machine Learning concepts, but it will give you a good understanding of the basics.
In our opinion this book is a very good follower of a theoretical Machine Learning book like The 100 page Machine Learning book or an online course like Andrew’s Ng Machine Learning course on Coursera. You can find a review of this course on our Machine Learning Courses Category.
Many times these kind of books and courses teach you the theory of Machine Learning but are quite shallow on the practice, and if there is any it consists on Jupyter Notebooks with pre-made exercises: not the proper way to learn to implement (a Jupyter Notebook can’t be used to build an application).
This book is a very good complement for the people with Machine Learning knowledge that wanna consolidate their practice and start being able to build awesome applications, or for those who are not so interested in understanding everything but want to know how to build cool projects.
You can buy Introduction to Machine Learning with Python on Amazon:
- Müller, Andreas C. (Author)
- English (Publication Language)
- 400 Pages - 11/01/2016 (Publication Date) - O'Reilly Media (Publisher)
About the authors
Andreas Muller received his PhD in machine learning from the University of Bonn. After working as a machine learning researcher on computer vision applications at Amazon for a year, he recently joined the Center for Data Science at the New York University.
In the last four years, he has been maintainer and one of the core contributor of Sci-kit, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.
Sarah is a data scientist who has spent a lot of time working in start-ups. She loves Python, machine learning, large quantities of data, and the tech world. She is an accomplished conference speaker, currently resides in New York City, and attended the University of Michigan for grad school.
Thank you very much for reading How to Learn machine Learning, and we hope you enjoyed our review of this great book, that serves as an Introduction to Machine Learning with Python.
Have a great day!