--- product_id: 422067384 title: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" price: "427 zΕ‚" currency: PLN in_stock: true reviews_count: 13 url: https://www.desertcart.pl/products/422067384-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow store_origin: PL region: Poland --- # 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:** 427 zΕ‚ **Availability:** βœ… In Stock ## Summary > πŸš€ Unlock the future of AI mastery with every page turned! ## 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?** 427 zΕ‚ with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.pl](https://www.desertcart.pl/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 - β€’ **Comprehensive Yet Accessible:** Perfect for professionals transitioning into ML, with clear explanations and minimal math barriers. - β€’ **Master Real-World ML Projects:** Follow a complete machine learning pipeline using scikit-learn to build confidence and practical skills. - β€’ **Hands-On with TensorFlow & Keras:** Build, train, and deploy models for computer vision, NLP, and reinforcement learning with industry-leading tools. - β€’ **Explore Cutting-Edge Neural Networks:** Dive deep into CNNs, RNNs, GANs, transformers, and more to stay ahead in AI innovation. - β€’ **Challenge Yourself with Rigorous Exercises:** Tackle meaningful, real-world problems that push your skills beyond theory into impactful practice. ## Overview Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a top-ranked, practical guide that empowers professionals to build intelligent systems by mastering end-to-end ML workflows, advanced neural network architectures, and hands-on TensorFlow/Keras implementations. With a 4.7-star rating from over 850 reviews, it combines clear explanations, challenging exercises, and up-to-date techniques to accelerate your AI career. ## 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 AurΓ©lien GΓ©ron 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: 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... 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. ## 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 | #15,778 in Books ( See Top 100 in Books ) #5 in Python Programming #6 in Computer Neural Networks #48 in Artificial Intelligence & Semantics | | Customer Reviews | 4.7 out of 5 stars 863 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 ### ⭐⭐⭐⭐⭐ 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... ### ⭐⭐⭐⭐⭐ 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. ### ⭐⭐⭐⭐⭐ Very insightful *by J***E on January 4, 2026* Insightful and easy to follow. ## 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? - 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.pl/products/422067384-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow](https://www.desertcart.pl/products/422067384-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow) --- *Product available on Desertcart Poland* *Store origin: PL* *Last updated: 2026-05-16*