--- product_id: 46737777 title: "Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow" price: "₹ 5379" currency: INR in_stock: true reviews_count: 10 url: https://www.desertcart.in/products/46737777-python-machine-learning-second-edition-machine-learning-and-deep-learning store_origin: IN region: India --- # Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow **Price:** ₹ 5379 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - **How much does it cost?** ₹ 5379 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.in](https://www.desertcart.in/products/46737777-python-machine-learning-second-edition-machine-learning-and-deep-learning) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description desertcart.com: Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow: 9781787125933: Raschka, Sebastian, Mirjalili, Vahid: Books Review: Good balance of theory and code. Excellent for people who already have intermediate stats/ML knowledge. - 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 learn I 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). Review: Excellent explanations, excellent visualizations, excellent mathematical proofs; incredible book! - 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! ## Technical Specifications | Specification | Value | |---------------|-------| | ASIN | 1787125939 | | Best Sellers Rank | #759,954 in Books ( See Top 100 in Books ) #144 in Business Intelligence Tools #325 in Data Processing #602 in Python Programming | | Customer Reviews | 4.5 4.5 out of 5 stars (301) | | Dimensions | 7.5 x 1.41 x 9.25 inches | | Edition | 2nd ed. | | ISBN-10 | 9781787125933 | | ISBN-13 | 978-1787125933 | | Item Weight | 2.51 pounds | | Language | English | | Print length | 622 pages | | Publication date | September 20, 2017 | | Publisher | Packt Publishing | ## Images ![Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Image 1](https://m.media-amazon.com/images/I/71PCVqFXvgL.jpg) ## 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 learn I 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! ### ⭐⭐⭐⭐ Good book for starters in Neural Networks *by E***N on November 10, 2018* Book gives a good overview of how to tackle a learning problem. Preparing learning data and evaluation of learning model. Witch python libraries to use and a lot of examples. Was very useful l for me Thanks guys ## Frequently Bought Together - Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Computational Physics --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.in/products/46737777-python-machine-learning-second-edition-machine-learning-and-deep-learning](https://www.desertcart.in/products/46737777-python-machine-learning-second-edition-machine-learning-and-deep-learning) --- *Product available on Desertcart India* *Store origin: IN* *Last updated: 2026-04-22*