Hands-On Machine Learning with Scikit-Learn, Keras, & TensorFlow by Aurélien Géron is an essential guide for anyone looking to learn about machine learning and how to apply it using popular Python libraries. This book covers everything from the basics of machine learning to advanced topics in deep learning, all while providing hands-on examples and practical advice.
The book is divided into three parts, with each part focusing on a different aspect of machine learning. Part one covers the fundamentals of machine learning, including the different types of machine learning algorithms, data preprocessing, model evaluation, and hyperparameter tuning. This section is a great starting point for beginners, as it provides a solid foundation for understanding how machine learning works and how to apply it to real-world problems.
Part two delves into deep learning with TensorFlow and Keras, two of the most popular libraries for building deep learning models. This section covers topics such as neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. The author provides detailed explanations of each topic, along with code examples that demonstrate how to implement them in Python.
Part three focuses on advanced topics in machine learning, such as reinforcement learning, unsupervised learning, and deploying machine learning models. This section is ideal for those who want to take their machine learning skills to the next level and learn about more complex algorithms and techniques.
One of the standout features of this book is the hands-on approach taken by the author. Each chapter includes practical examples and exercises that allow readers to apply the concepts they have learned in a real-world setting. The code examples are clear and easy to follow, making it easy for readers to experiment with different algorithms and techniques.
Overall, Hands-On Machine Learning with Scikit-Learn, Keras, & TensorFlow is a comprehensive and well-written guide to machine learning that is suitable for beginners and experienced practitioners alike. The author does a great job of breaking down complex concepts into easily digestible chunks, making it easier for readers to understand and apply them in their own projects.
In conclusion, I highly recommend this book to anyone looking to learn more about machine learning and how to implement it using Python. Whether you are just starting out or have some experience with machine learning already, this book is sure to help you improve your skills and become a better machine learning practitioner.
Really liked this video, now I know what to expect from the book.
do you still recommend this book for 2024??
advise me PLZ
i read grokking machine learning book , do i need to read hands on Machine Learning with Scikit-Learn and TensorFlow also or not?
Hands on Machine Learning with Scikit-Learn and TensorFlow will help me more and get me lots of additional knowledge or not
What is up with so many Asians and LatAms being interested in “quant” finance?
Thanks brother. Good info for me
Hi can you make a video about the deep learning with python book ? And is it suitable for a beginner in neural networks?
Im currently going through this book, currently on chp. 3 …
one thing for sure if u really want to enjoy or better understand this book, learn complete python before purchasing this book …
I would like to start in ML's field. This book looks great but what other book should I use to supplement the theory part?
I have both of hands on machine learning with python and pyhton for deep learning how can I use them
is still this book recommended for 2022 ?
Bianco, what about: Machine Learning for Asset Managers By Lopes de Prado?
For my program in stats, I read the elements of Statistical learning, still my favorite book. I read Applied Predictive Modeling by Max Khun. I would recommend this book to anyone, it's a good read.
Which books do you recommend as companions (for Math, and other lacking areas) of this Hands-on ML book?
Do I need to learn Python before following this book? I know only the basics of Python.
I don't know a thing about ML. I am a Python programmer, have done a some matrix stuff before, is this a good book for a complete beginner for me?
Why buy this book if there is good online documentation, udemy courses, YouTube, medium, Twitter?
Well I would comment on that book as a general view of machine learning The most important part is in chapter 1 and chapter 2
and the rest of it I recommend everyone should go through the details yourself for example RPCA U should able to build up your own script from scratch
and keep doing new project to keep your mind strong
Reading this book now. It's pretty friendly for beginners.
Does the book by Christopher Bishop provide any additional value for someone who has already gone through the Deep learning book( by Goodfellow and friends) and intro to statistical learning?
Solutions to these exercises are available in appendix A.
Where can i find this Appendix A for hands on machine learning with scikit-learn and tensorflow 2