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Introduction to Machine Learning with Python: A Guide for Data Scientists

4.33  ·  Rating details ·  220 ratings  ·  24 reviews
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are ...more
ebook, 400 pages
Published September 26th 2016 by O'Reilly Media (first published June 25th 2015)
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Gabri
May 29, 2018 rated it really liked it  ·  review of another edition
I have not read much of this book, to be honest; my teacher covered all literature during his classes so I didn't feel a need to read what I had heard and seen already. But this book is really nicely written: it patiently teaches all you need to know when you start with little to no foreknowledge, providing codes and its outputs and explaining what this output means.

I would recommend this to anyone who wants to learn how to do machine learning; familiarity with Python is a big pre.
Arash Amani
finally, I could read this great book. I have found that some topic that I didn't know about them like chaining and pipeline. the working with text data has more profit for me
Philipp
Dec 08, 2016 rated it it was amazing  ·  review of another edition
What a useful book - it focuses mostly on scikit-learn with some numpy, pandas and matplotlib thrown in, you could say it's an in-depth tour of some of the more useful methods in scikit-learn - classifying, regression, a bit of clustering, PCA, all the different ways to measure the outcome of your model, how to use the incredibly useful scikit-learn Pipeline to test parameters and models, etc.

The examples are useful and interesting, especially the face picture clustering and classifi
...more
Terran M
May 19, 2018 rated it really liked it  ·  review of another edition
I do not like this book as much as as An Introduction to Statistical Learning: With Applications in R, but if you are constrained or committed to using Python instead of R, it is the best available alternative as of 2018, and I do think it's pretty good overall. You will need to already be fluent in numpy, pandas, and matplotlib to read this book; if you area not, read Python Data Science Handbook: Tools and Techniques for Developers first.
Stefan Kanev
Oct 19, 2018 rated it really liked it  ·  review of another edition
This is a great book.

It is a nice introduction to Machine Learning (scikit-learn specifically) without much maths needed. It will by no means make you an expert, but it will give you a good sense of the basics, a walkthrough of scikit-learn and hopefully some intuition about the popular algorithms. A nice supplement to it is the Andrew Ng Coursera course about Machine Learning. The two make a very good starting point into the ML journey for programmers who are curious.
Akshay Sapre
Oct 29, 2017 rated it really liked it
It is a nice book to start Machine Learning. Book explains about all the algorithms.
Mark
Nov 03, 2019 rated it it was amazing  ·  review of another edition
Shelves: data-science
If you want to understand Machine Learning, start here. The facial recognition sections alone are worth the price of admission. Deep enough to be interesting and informative without overwhelming the reader. Take it slow and read it with a pencil and a computer nearby (the examples are on GitHub).
Wonhee
Feb 16, 2019 rated it it was amazing  ·  review of another edition
This book should be the first book for anyone who has a bit of programming background and want to overview how machine learning would look like without deep diving into the linear algebra and/or any relevant math.

The book uses Python, scikit-learn, bumpy, etc that are well defined and have been widely used, and take examples one by one, but not with serious math or from the scratch but using existing scikit-learn.

Probably some people would like to learn all the stuff from the scratc
...more
Philip
Nov 27, 2018 rated it it was amazing  ·  review of another edition
Great way to get started with Python and ML:
- Gives overview of tools/libraries you'll likely need
- Broad overview of algorithms, with a good explanation on how they work and insight into how the main parameters influence behavior (with examples in the book and code to demonstrate how to use. Code is also available on Github.)
- Many guidances as when to use what (depending on which kind of data/problem you've got; which techniques work better + it gives an idea on which techniq
...more
Xingda Wang
Dec 11, 2018 rated it it was amazing  ·  review of another edition
I would recommend all pure beginner to start machine learning from this book, Author Andreas clarifies several key concepts (for example grid search and cross validation) in details, which confuses me for long, now I am so clear. Another good point is that the book is so well structured, and could read a second time, I recognise that I couldn't find anther more efficient path!

Meanwhile, I found a code error through all the code in this book, in Chapter 5, Evaluation Metrics and Scori
...more
Ulises Jimenez
This book is very basic introduction to Machine Learning and there are better books for example hands on machine learning with scikit-learn and tensorflow. The examples in the book uses a library that the author did which makes difficult to really learn how to do the analysis in python
Mohammad Jafar
It might be my bad choice, but this book doesn't give you a nice insight into how (classical) machine learning algorithms and techniques work. It is more or less a restructuring of scikit-learn's documentation with nearly zero mention of mathematical backgrounds of the techniques used. It is full of code examples and the results of running them and an explanation of "how" those results are and often not "why". The most useful part of the book is the last chapter which refers the reader to papers ...more
نجود
Aug 26, 2018 rated it really liked it  ·  review of another edition
Overall the book is good, It packed with lots of practical examples and exercises to build machine learning models with Python, but I personally feel that it lacked details on deeper math and deeper ideas on main concepts of the models. so it may be good for those with little mathematical or statistical background.
Konstantin
Dec 26, 2018 rated it it was amazing  ·  review of another edition
fine book for starter
Max
Jun 20, 2019 rated it it was amazing  ·  review of another edition
ML basis book.
Abhishek Singh
Aug 28, 2018 rated it it was amazing  ·  review of another edition
Nice intro to machine learning in python. The book is easy and intuitive for beginner machine learning practitioners.
Melissa Mock
Oct 27, 2019 rated it it was amazing  ·  review of another edition
Clear explanations that provide a strong foundation in machine learning. After reading this book I have enough of an understanding to dive deeper into any of the ML models covered.
Mark
Aug 02, 2017 rated it liked it  ·  review of another edition
Definitely introductory, but also quite thorough. Well organized as well.
Spyros Galanis
Aug 19, 2018 rated it really liked it  ·  review of another edition
Interesting book, it provides a good introduction to the applications of machine learning, without spending too much time on the mathematics.
Ivan Atanasov
Nov 21, 2017 rated it really liked it  ·  review of another edition
Very good summary about scikit-learn library practical usage.
Sweemeng Ng
Sep 06, 2018 rated it really liked it  ·  review of another edition
Focused on statistical learning, but cover very good fundamental, like model search and all that.
Arnold Taremwa
Feb 05, 2017 rated it it was amazing  ·  review of another edition
I really loved this book! Its an introductory text, perfect to get your hands dirty with most popular supervised and unsupervised learning algorithms to make pretty good predictions.
I like the higher conceptual explanations of the methods and not bogging you down with too much math, which you can find in other texts if you wanted.
I got a broad introduction to all the main techniques used in machine learning and I am in a good place now to dig deeper in the topics.
Nishank Magoo
Basic Book. Easy to Read .
Bill
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