--- product_id: 72169186 title: "Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" price: "MX$5496" currency: MXN in_stock: true reviews_count: 13 url: https://www.desertcart.mx/products/72169186-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow store_origin: MX region: Mexico --- # Code-driven practical exercises Comprehensive ML techniques Hands-on deep learning with TensorFlow & Keras Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems **Price:** MX$5496 **Availability:** βœ… In Stock ## Summary > πŸš€ Unlock your AI potential with the ultimate hands-on ML guide! ## Quick Answers - **What is this?** Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - **How much does it cost?** MX$5496 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.mx](https://www.desertcart.mx/products/72169186-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Key Features - β€’ **Code-First Learning:** Dive into real Python notebooks with step-by-step exercises and solutions. - β€’ **Deep Learning Demystified:** Explore CNNs, RNNs, LSTMs, and reinforcement learning with intuitive explanations. - β€’ **Master the Full ML Spectrum:** From linear regression to GANs, build a robust machine learning toolkit. - β€’ **Production-Ready Frameworks:** Leverage Scikit-Learn, TensorFlow, and Keras to build scalable intelligent systems. - β€’ **Stay Ahead with Practical Insights:** Balance theory and practice with clear writing, humor, and GitHub support resources. ## Overview Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow is a top-rated, practical guide that empowers programmers to build intelligent systems using state-of-the-art Python frameworks. Covering everything from classical machine learning models to advanced deep learning architectures, this book combines minimal theory with extensive coding exercises and real-world projects, making it the go-to resource for mastering machine learning and AI in 2024. ## Description Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow--author AurΓ©lien GΓ©ron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Review: Fabulous book - jam-packed - This book should be regarded as a "gold-standard" for technical books. It balances theory and practice, has exercises (actually with answers!) and covers a tremendous breadth and depth. The book starts out in a refreshingly unconventional way of giving you a crash course in ML concepts before diving in to an end-to-end project. I note that one reviewer didn't like that but I liked it a lot. While a lot of it will go over your head if you lack experience (and the author assumes you don't have much), it gives you appreciation of what an overall real-life project might look like. The rest of the book is spent unpacking each of those stages. The first part of the book looks at more "classical" or traditional machine learning concepts like linear regression, logistic regression, SVMs, decision trees, ensemble learning and unsupervised models. Along the way you learn a lot of data science best-practises and how to train and test things properly. The second part dives into deep learning, progressing from general neural networks to CNNs, RNNs, LSTMs, autoencoders and GANs. You get a flavour of how GPT models work. Other topics covered in this section are Tensorflow and Keras (including a part on deploying models) and a chapter on another paradigm: reinforcement learning. Geron doesn't shy away from the math but gives you enough theory to appreciate the detail if you like that, and explains it in intuitive ways and with code. Some of the formulas can look intimidating but they are unpacked and explained well. There are review questions and/or exercises at the end of each chapter. One of my biggest frustrations with technical books in general is when they give you questions but no answers. Here, you get answers and also worked code in the provided notebooks, which is amazing. Other technical authors: take note. The exercises are often quite challenging to implement or at least open-ended, but I believe that to be a good thing. I learnt a lot from doing them (I'll admit I didn't do all of them!). The writing is clear, engaging and often humourous. To sum up, if you want to learn more about ML, I highly recommend this book. This review is for the 2nd edition but I'll be buying the 3rd edition and will definitely be re-reading. There is so much great information to take in. Thanks to the author for this masterpiece. Review: Ultra readable, extremely practical and great support resources on github - Loved this book, I recommend whenever I'm asked by people who want to get practical with ML. The chapters follow a logical order and are well worth working though carefully, following all the code with the result being that you'll get a very solid foundation for ML, covering both the data science driven statistical methods (first half of the book) and xNN/RL (2nd half). It fills the gap between books that are too hello world/simplistic and the other end which is greek alphabet soup. Loved the fact you can just spin up a colab notebook and point it at the github for the book and just get on with playing with all the examples...no messing around with lots of local machine setup. Oh and if you need a refresher on python or linear algebra, then he has that covered too, just look at the github only chapters. If I could give 6 stars, I would...just buy it! Am now waiting for the 3rd edition, avail in US but not in UK yet... ## Features - Hands on Machine Learning with Scikit Learn Keras and TensorFlow: Concepts Tools and Techniques to Build Intelligent Systems - Books Subjects Computing Internet Computer Science AI Machine Learning Books Subjects Computing Internet Programming Languages Books Subjects Computing Internet Computer Science Information Systems - Product Type: ABIS BOOK - Brand: OReilly ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | 406,078 in Books ( See Top 100 in Books ) | | Customer Reviews | 4.8 out of 5 stars 3,360 Reviews | ## Images ![Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Image 1](https://m.media-amazon.com/images/I/81R5BmGtv-L.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Fabulous book - jam-packed *by H***. on 18 September 2023* This book should be regarded as a "gold-standard" for technical books. It balances theory and practice, has exercises (actually with answers!) and covers a tremendous breadth and depth. The book starts out in a refreshingly unconventional way of giving you a crash course in ML concepts before diving in to an end-to-end project. I note that one reviewer didn't like that but I liked it a lot. While a lot of it will go over your head if you lack experience (and the author assumes you don't have much), it gives you appreciation of what an overall real-life project might look like. The rest of the book is spent unpacking each of those stages. The first part of the book looks at more "classical" or traditional machine learning concepts like linear regression, logistic regression, SVMs, decision trees, ensemble learning and unsupervised models. Along the way you learn a lot of data science best-practises and how to train and test things properly. The second part dives into deep learning, progressing from general neural networks to CNNs, RNNs, LSTMs, autoencoders and GANs. You get a flavour of how GPT models work. Other topics covered in this section are Tensorflow and Keras (including a part on deploying models) and a chapter on another paradigm: reinforcement learning. Geron doesn't shy away from the math but gives you enough theory to appreciate the detail if you like that, and explains it in intuitive ways and with code. Some of the formulas can look intimidating but they are unpacked and explained well. There are review questions and/or exercises at the end of each chapter. One of my biggest frustrations with technical books in general is when they give you questions but no answers. Here, you get answers and also worked code in the provided notebooks, which is amazing. Other technical authors: take note. The exercises are often quite challenging to implement or at least open-ended, but I believe that to be a good thing. I learnt a lot from doing them (I'll admit I didn't do all of them!). The writing is clear, engaging and often humourous. To sum up, if you want to learn more about ML, I highly recommend this book. This review is for the 2nd edition but I'll be buying the 3rd edition and will definitely be re-reading. There is so much great information to take in. Thanks to the author for this masterpiece. ### ⭐⭐⭐⭐⭐ Ultra readable, extremely practical and great support resources on github *by R***N on 23 November 2022* Loved this book, I recommend whenever I'm asked by people who want to get practical with ML. The chapters follow a logical order and are well worth working though carefully, following all the code with the result being that you'll get a very solid foundation for ML, covering both the data science driven statistical methods (first half of the book) and xNN/RL (2nd half). It fills the gap between books that are too hello world/simplistic and the other end which is greek alphabet soup. Loved the fact you can just spin up a colab notebook and point it at the github for the book and just get on with playing with all the examples...no messing around with lots of local machine setup. Oh and if you need a refresher on python or linear algebra, then he has that covered too, just look at the github only chapters. If I could give 6 stars, I would...just buy it! Am now waiting for the 3rd edition, avail in US but not in UK yet... ### ⭐⭐⭐⭐⭐ Amazing book on ML *by C***G on 14 August 2021* Have been advised by many people this is possibly the best book on ML but held off on owning a hard copy as I found it a bit expensive so I grabbed this one roughly 50% off. The level of detail is amazing and everything ML related is nicely explained. It's nice to see the book was printed in colour which makes the code easier to follow and reproduce. I also liked the layout very much and found it helped to make the book flow - will happily read this cover to cover. The quality of the paper is on thin side but to be fair the content is worth more - I own other similar size ML books printed in black and white that cost more with half the content because it was printed on thick paper. Highly recommended for anyone with an interest in ML. ## Frequently Bought Together - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Deep Learning (Adaptive Computation and Machine Learning series) - Deep Learning with Python, Second Edition --- ## Why Shop on Desertcart? - πŸ›’ **Trusted by 1.3+ Million Shoppers** β€” Serving international shoppers since 2016 - 🌍 **Shop Globally** β€” Access 737+ million products across 21 categories - πŸ’° **No Hidden Fees** β€” All customs, duties, and taxes included in the price - πŸ”„ **15-Day Free Returns** β€” Hassle-free returns (30 days for PRO members) - πŸ”’ **Secure Payments** β€” Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** β€” Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.mx/products/72169186-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow](https://www.desertcart.mx/products/72169186-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow) --- *Product available on Desertcart Mexico* *Store origin: MX* *Last updated: 2026-07-07*