Python Machine Learning

by Wei-Meng Lee

Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart―it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today.  Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand. •          Python data science―manipulating data and data visualization •          Data cleansing •          Understanding Machine learning algorithms
•          Supervised learning algorithms •          Unsupervised learning algorithms •          Deploying machine learning models  Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level. 

320 pages, Paperback

Published April 30, 2019

Reviews

[Aaron · Rating 1 out of 5]

1. First half of the book is about how to install python, how to use numpy, pandas and matplotlib.
2. Eventually, it starts getting into 'machine learning' by doing linear regression with scikit-learn. There's nothing new other than the existing sample projects from scikit-learn. I'd rather just read the documentation online.
3. Most of the scatter dot charts just didn't make any sense to me, until I realize the data points are supposed to be colored. (see attached picture), while this book is a black-white print. Do they even do proofreading on the book?
4. I got the book from the library and went through it in over an hour, so I'm grateful I didn't lost any $ or too much time on it.

[Ben · Rating 3 out of 5]

Inside The Machine

While this book may not be the most comprehensive book on the topic, it does a great job of introducing the reader to a variety of machine learning concepts.

The book is well-structured and provides plenty of examples, making it easy for readers to follow along and apply what they learn.

One of the best things about this book is the author's use of code snippets and visual graphs to explain complex concepts. This makes the material much more accessible to readers who may be new to the subject.

That being said, there is certainly room for improvement in terms of depth and detail. I found that the book doesn't delve deeply enough into certain topics or that it leaves out important information.

Overall, Python Machine Learning is a decent resource for those looking to gain a basic understanding of machine learning concepts.
It's well-organized and easy to follow, but may not provide enough depth for more advanced readers.

3.7/5

[Mauro Manucci · Rating 2 out of 5]

Over a third of the book is dedicated to explaining pandas, numpy, matplotlib... stuff that you can find online for free.
The stats is poorly explained.
And some graphs don't even make any sense.

At least it is quite accessible (easy to read) I guess.

[Jey The Moose · Rating 3 out of 5]

A bit outdated, but I learned a lot.