--- product_id: 422067384 title: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" price: "SAR 351" currency: SAR in_stock: true reviews_count: 13 url: https://www.desertcart.com.sa/products/422067384-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow store_origin: SA region: Saudi Arabia --- # End-to-end ML project tracking Advanced neural net architectures TensorFlow & Keras integration Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems **Price:** SAR 351 **Availability:** ✅ In Stock ## Summary > 🚀 Elevate your AI game with the ultimate hands-on ML toolkit! ## 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?** SAR 351 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.com.sa](https://www.desertcart.com.sa/products/422067384-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 - • **Master ML from Start to Finish:** Track a complete machine learning project using Scikit-Learn with hands-on examples. - • **Unleash Unsupervised Learning Power:** Harness clustering, dimensionality reduction, and anomaly detection techniques. - • **Explore Cutting-Edge Neural Networks:** Dive deep into CNNs, RNNs, GANs, transformers, and more to build intelligent systems. - • **Learn by Doing with Practical Exercises:** Challenge yourself with production-ready code and exercises designed for immediate application. - • **Build Real-World AI with TensorFlow & Keras:** Train models for computer vision, NLP, generative AI, and reinforcement learning. ## Overview This bestselling, third-edition guide by Aurélien Géron empowers professionals to build intelligent systems using practical Python frameworks—Scikit-Learn, Keras, and TensorFlow. Covering everything from foundational models to advanced deep learning architectures, it offers clear explanations, real-world examples, and challenging exercises, making it the go-to resource for mastering machine learning and deep learning in a professional context. ## Description Through a recent series of 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 bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning Review: Great book to learn practical Machine Learning - I have just finished Hands-On ML book and I cannot recommend it enough. I have been working as a Mobile Software Developer for 12 years and now I am thinking about trying something new. I remember some Math and Statistics from school but definitely not enough to get deep into the subject. From my experience, you can read the book and finish all the exercises without understanding any of the Math (although as author points out, it is beneficial if you understand the Math behind it - e.g. to understand why it works, read and implement papers). Book goes into the detail and explains the history of how we got there so it was fairly easy for me to follow and understand majority of the book. I missed this kind of detail from ML courses that I tried. You will also see significant papers explained - something that would be difficult for me to do alone at this point. However, one thing I appreciated the most were the exercises. In ML courses I tried, the exercises were simple and too easy to give you anything. Here it was a real challenge and I have a good feeling about what I learned by doing those exercises. There are also a lot of references for books or papers in case you want to focus on a specific area. One blind spot I am seeing though is focus on Keras/TensorFlow and GCP pipeline whereas the most examples on internet seem to be from PyTorch and AWS as a most popular cloud solution. However, as author points out, if you know one it will be easy for you to switch (I also reimplemented some of the PyTorch projects as part of exercises without too much difficulty). Still, I need to think about it and get some more PyTorch and AWS experience. Review: Better explanation, better visuals, nice print - I bought three AI books this year and I ended up reading this one so far by Aurelien instead of the other (which was unfortunately in black & white, had misaligned paper cut, etc.). The book by Aurelien Geron (3rd edition) has better explanation, better visual aids, nicer print, etc. One thing I probably would suggest though, is to maybe do a similar code comments style/explanation like what was done in the third book that I got (Deep Learning With Python by Francis Chollet), which I just got but haven't read yet. Some of the code explanation is on the same page/area/line. Convenient. No flipping of pages... ## Features - Use scikit-learn to track an example ML project end to end - Explore several models, including support vector machines, decision trees, random forests, and ensemble methods - Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection - Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers - Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #32,099 in Books ( See Top 100 in Books ) #3 in Computer Vision & Pattern Recognition #5 in Computer Neural Networks #6 in Python Programming | | Customer Reviews | 4.7 out of 5 stars 886 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/81qHV3ACapL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Great book to learn practical Machine Learning *by A***R on April 4, 2025* I have just finished Hands-On ML book and I cannot recommend it enough. I have been working as a Mobile Software Developer for 12 years and now I am thinking about trying something new. I remember some Math and Statistics from school but definitely not enough to get deep into the subject. From my experience, you can read the book and finish all the exercises without understanding any of the Math (although as author points out, it is beneficial if you understand the Math behind it - e.g. to understand why it works, read and implement papers). Book goes into the detail and explains the history of how we got there so it was fairly easy for me to follow and understand majority of the book. I missed this kind of detail from ML courses that I tried. You will also see significant papers explained - something that would be difficult for me to do alone at this point. However, one thing I appreciated the most were the exercises. In ML courses I tried, the exercises were simple and too easy to give you anything. Here it was a real challenge and I have a good feeling about what I learned by doing those exercises. There are also a lot of references for books or papers in case you want to focus on a specific area. One blind spot I am seeing though is focus on Keras/TensorFlow and GCP pipeline whereas the most examples on internet seem to be from PyTorch and AWS as a most popular cloud solution. However, as author points out, if you know one it will be easy for you to switch (I also reimplemented some of the PyTorch projects as part of exercises without too much difficulty). Still, I need to think about it and get some more PyTorch and AWS experience. ### ⭐⭐⭐⭐⭐ Better explanation, better visuals, nice print *by -***L on January 5, 2026* I bought three AI books this year and I ended up reading this one so far by Aurelien instead of the other (which was unfortunately in black & white, had misaligned paper cut, etc.). The book by Aurelien Geron (3rd edition) has better explanation, better visual aids, nicer print, etc. One thing I probably would suggest though, is to maybe do a similar code comments style/explanation like what was done in the third book that I got (Deep Learning With Python by Francis Chollet), which I just got but haven't read yet. Some of the code explanation is on the same page/area/line. Convenient. No flipping of pages... ### ⭐⭐⭐⭐⭐ This is a dynamite book for practical understanding. *by L***E on October 4, 2023* Wow! What a thorough and well written book. It starts out with examples if you are purely interested in how to apply ML methods. The rest of the book takes a deeper dive into what the different algorithms are doing and gives examples of how to apply each method. I think the level of the writing is a great balance between thoroughness and approachability. My background is in mechanical engineering and I find the detail of the book to be satisfying, without getting so bogged down in theory and proofs as to make it overwhelming. It is also very comprehensive - you could use this book as a reference for looking up more detail about specific algorithms as they come up in your work/learning, but I am actually enjoying reading it cover-to-cover to broaden my understanding of the subject. Finally, you can't beat the price for a book of this quality. This well exceeded my expectations. ## Frequently Bought Together - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications - AI Engineering: Building Applications with Foundation Models --- ## 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.com.sa/products/422067384-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow](https://www.desertcart.com.sa/products/422067384-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow) --- *Product available on Desertcart Saudi Arabia* *Store origin: SA* *Last updated: 2026-08-28*