40 Algorithms Every Programmer Should Know: Hone your problem-solving skills by learning different algorithms and their implementation in Python
S**S
Mixed quality but overall a good practical primer to algorithms
Some of the chapters are good introductions to their respective topic. Something I would really recommend to someone wanting a starter.Other topics are, unfortunately rather short for the complexity of the topic presented. That expresses itself by not giving the same thorough intro in respect to the basics or being quite condensed like giving only a real abstract presentation. I reduce one star for those shortcomings. Why? Because for the topics I feel this being very obvious, there are complete books as introduction.Maybe it would have been a good idea to spare those topics and to provide a better curated list of other introductory books for further reading, but I would assume writing a book and finding a good balance is hard work as well as an art in itself
K**R
One of the best python books to have!
This book is true to its name and has so many algorithms and presents them well. It beats any data science text in presenting them as well (even though the theory of data science is not presented in whole in this book). I would call this book both practical and meaningful for anyone wishing to use python for any purpose. Excellent book, well-written, well-presented, and easy to learn from.
T**M
Good book of algorithms
The book takes you through the popular algorithms in a clear and consise way. Very good for developing programming skills that are required for job interviews.
H**F
Poor content and quality
First, this is badly produced: misspellings, grammatical errors and typos. Second, it really isn't a discussion of algorithms (except for simple sorting): it is a high level introduction to some of the python mathematical libraries. It does not discuss the actual algorithms: just how to invoke them from the libraries. So, it does not help with knowing the algorithms, just with using them. Very disappointing.
R**K
Easy read - must have
It goes beyond just listing the algorithms and the implementation - it provides complexity analysis. How to keep ML safe? ... good gamut of relevant topics - Loving it!!