40 Algorithms Every Programmer Should Know: Hone your problem-solving skills by learning different algorithms and their implementation in Python Illustrated Edition

There are a few books that should be required reading for all programmers. Fowler's Refactoring book, Robert C. Martin's book on Agile Software development, the GoF Design Patterns book, and of course Dale Carnegie's "How to Win Friends and Influence People".This book should be added to the list.The book starts with some basics of Python programming and foundational algorithms like searching and sorting, and quickly moves on to more advanced algorithms for machine learning and big data.Imran writes in a style that is easy to understand, to the point and enjoyable. The text also includes both diagrams and Python code to help the reader understand.Definitely five-stars! An excellent read for programmers young and old.

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.

Covers a lot of different algorithm types and their uses but many are glossed over or oversimplified. I can't really fault the author though as the book would need to be 10x as long to cover each in depth.The expected categories are covered though (sorting, graph traversal, etc). When you get to areas like hashing though, they cover 2 common ones (MD5 and SHA-512) and explain how, in general - as you would give to a non-programmer, hashing works and how to run in Python using a library.

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!!

For analytical professionals, this is a good reference book to use which aligns the use cases in different business areas and proper algorithms.

Although the topic choices were made correctly, unfortunately the depth was very limited. Also, there are lots of typos in very critical points. Since the algorithm codes are generally based on Python embedded library functions, it is not possible to see the coding clearly.

Good refresher for programmers who do not remember everything from their computer science class (like myself).

There are quite a few egregious typos. They are the sort of typos that completely change the meaning. It's basically lying to the reader!One example:"Note that among the four types of Big O notation types presented, O(n2) has the worst performance and O(logn) has the best performance."O(logn) is not the best! It's actually the worst! This is an absolutely ridiculous typo for an algorithms book to make!There are also examples of example code output being completely wrong, giving the reader false sense about set operators.There is a github with some errata, but it's not complete and the page numbers don't match this copy of book.There are also instances of ambiguous language that can definitely lead the reader to incorrectly understand the data structures and algorithms described in the book.I WOULD NOT recommend anyone buy this book.