---
product_id: 46737777
title: "Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow"
brand: "sebastian raschkavahid mirjalili"
price: "VT17165"
currency: VUV
in_stock: true
reviews_count: 5
url: https://www.desertcart.vu/products/46737777-python-machine-learning-second-edition-machine-learning-and-deep-learning
store_origin: VU
region: Vanuatu
---

# Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

**Brand:** sebastian raschkavahid mirjalili
**Price:** VT17165
**Availability:** ✅ In Stock

## Quick Answers

- **What is this?** Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow by sebastian raschkavahid mirjalili
- **How much does it cost?** VT17165 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.vu](https://www.desertcart.vu/products/46737777-python-machine-learning-second-edition-machine-learning-and-deep-learning)

## Best For

- sebastian raschkavahid mirjalili enthusiasts

## Why This Product

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## Description

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## Customer Reviews

### ⭐⭐⭐⭐⭐ Good balance of theory and code. Excellent for people who already have intermediate stats/ML knowledge.
*by S***W on March 12, 2018*

This book is excellent for the following demographic:People who already have a decent level of skill and experience in statistics who want to:- 1) Elevate their understanding of ML techniques without absolutely breaking their skull on dense theory- 2) Learn how to implement the algorithms in Python and gain moderate proficiency in sci-kit learnI would say it's not a beginner's book, but more for intermediates. I am half-way through and find it a little challenging, but definitely attainable. This balance I consider to be putting me right in the sweet spot for learning. To judge whether you're a good candidate for this book, you can compare your experience and skill to me :I started this book after earning a PhD in the social sciences, which basically gave me good coverage in inferential and applied statistics (T, F distributions, p-values, confidence intervals, linear regression, one-way and factorial ANOVA, PCA, etc.). I also took a machine learning graduate course at my university and a few online courses in introductory ML for R. All of this background gave me solid grounding in statistics. With all this I still find this book somewhat challenging, but definitely not too hard. I'd say without my background I would find this book hard to get through. There is linear algebra, concepts like minimizing cost functions, bias/variance tradeoff, learning from errors, etc. So, if you are just starting out or reading the previous sentence and don't know what I'm talking about, I would recommend learning more stats fundamentals before starting this.After you gain some proficiency in stats, come learn this book and elevate your understanding of the algorithms, add nuance to them, integrate them into your mental conceptual structures more fully (e.g. you'll know more nuances of ML, e.g. which subsets of algorithms are preferred for controlling more of the bias, variance, how random forest is basically bagging with a twist, how adaboost's treatment of classification errors has kind of an element of perceptron implementation, and many more).

### ⭐⭐⭐⭐⭐ Excellent explanations, excellent visualizations, excellent mathematical proofs; incredible book!
*by S***Y on August 14, 2018*

This book will stay on your reference shelf for years to come!The authors clearly have taught these materials many times before, and their significant mathematical and technical prowess is delivered using a very approachable style. This book seems best suited for someone who wants to sit down and begin to apply Python Machine Learning to a problem that they already know they have. It's not particularly an "intro course to M.L.", but it contains enough details that you could easily follow along and learn how to use the various tools and techniques of the field if you've never seen or heard of them before.The copious notes scattered throughout this book are pure gold, mined from the obvious experiences of the authors while working in the field. If there ever is a Machine Learning equivalent to the venerable "Forrest M. Mims Engineering Notebook" for electronics, I feel these two authors could write it!Once you use this book to work on your current M.L. problem in Python, you will find yourself returning to it as a reference for other problems in the M.L. space. Its lucid explanations will help reinforce the topics presented, and cement your understanding of the materials.This book will get you writing Python Machine Learning code to work your current M.L. problem in no time flat!

### ⭐⭐⭐⭐ This book is quite good
*by P***R on February 2, 2018*

This book is quite good. Normally, I only buy used books (to save money). Since this book is so new, I could not find it used. Hence I bought it new at full price. I was wary of the expenditure and the content. After a minute or two reading the book, I changed my mind. This book is clearly worth the price. The level of technical content (AI and Python) is quite high. Anyone with a background in programming (hopefully Python programming) will learn a ton from this book. Of course, you need the right tools to use this book (a PC with a graphics card). However, it you have the tools you will get more than what you paid for.

## Frequently Bought Together

- Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow
- Computational Physics

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*Product available on Desertcart Vanuatu*
*Store origin: VU*
*Last updated: 2026-03-08*