Value: $69.99
(as of Aug 17, 2025 21:55:56 UTC – Particulars)
Utilized machine studying with a strong basis in concept. Revised and expanded for TensorFlow 2, GANs, and reinforcement studying.
Key Options
Third version of the bestselling, broadly acclaimed Python machine studying e book Clear and intuitive explanations take you deep into the speculation and apply of Python machine studying Absolutely up to date and expanded to cowl TensorFlow 2, Generative Adversarial Community fashions, reinforcement studying, and finest practices
E book Description
Python Machine Studying, Third Version is a complete information to machine studying and deep studying with Python. It acts as each a step-by-step tutorial, and a reference you may preserve coming again to as you construct your machine studying methods.
Full of clear explanations, visualizations, and dealing examples, the e book covers all of the important machine studying strategies in depth. Whereas some books educate you solely to observe directions, with this machine studying e book, Raschka and Mirjalili educate the ideas behind machine studying, permitting you to construct fashions and purposes for your self.
Up to date for TensorFlow 2.0, this new third version introduces readers to its new Keras API options, in addition to the most recent additions to scikit-learn. It is also expanded to cowl cutting-edge reinforcement studying strategies based mostly on deep studying, in addition to an introduction to GANs. Lastly, this e book additionally explores a subfield of pure language processing (NLP) referred to as sentiment evaluation, serving to you discover ways to use machine studying algorithms to categorise paperwork.
This e book is your companion to machine studying with Python, whether or not you are a Python developer new to machine studying or wish to deepen your information of the most recent developments.
What you’ll study
Grasp the frameworks, fashions, and strategies that allow machines to ‘study’ from information Use scikit-learn for machine studying and TensorFlow for deep studying Apply machine studying to picture classification, sentiment evaluation, clever net purposes, and extra Construct and practice neural networks, GANs, and different fashions Uncover finest practices for evaluating and tuning fashions Predict steady goal outcomes utilizing regression evaluation Dig deeper into textual and social media information utilizing sentiment evaluation
Who this e book is for
If you understand some Python and also you wish to use machine studying and deep studying, choose up this e book. Whether or not you wish to begin from scratch or prolong your machine studying information, that is an important useful resource. Written for builders and information scientists who wish to create sensible machine studying and deep studying code, this e book is good for anybody who needs to show computer systems the right way to study from information.
From the Writer
What’s new on this third version?
Many readers have informed us how a lot they love the primary 12 chapters of the e book as a complete introduction to machine studying and Python’s scientific computing stack. To maintain these chapters related and to enhance the reasons based mostly on reader suggestions, we up to date them to help the most recent variations of NumPy, SciPy, and scikit-learn.
Probably the most thrilling occasions within the deep studying world was the discharge of TensorFlow 2. Consequently, all of the TensorFlow-related deep studying chapters have acquired an enormous overhaul. Since TensorFlow 2 launched many new options and elementary modifications, we rewrote these chapters from scratch. Moreover, we added a brand new chapter on Generative Adversarial Networks, that are one of many hottest matters in deep studying analysis, in addition to a complete introduction to reinforcement studying based mostly on quite a few requests from readers.
What are the important thing takeaways out of your e book?
Machine studying will be helpful in nearly each downside area. We cowl a whole lot of completely different subfields of machine studying within the e book. My hope is that individuals can discover inspiration for making use of these elementary strategies to drive their analysis or industrial purposes. Additionally, utilizing well-developed and maintained open supply software program makes machine studying very accessible to a large viewers of skilled programmers, in addition to those that are new to programming.
Python Machine Studying Third Version can be completely different from a basic educational machine studying textbook on account of its emphasis on sensible code examples. Nonetheless, I believe this strategy is extremely invaluable for each college students and younger researchers who’re getting began in machine studying and deep studying. We heard from readers of earlier editions that the e book strikes a superb stability between explaining the broader ideas supported with nice hands-on examples, giving a lightweight introduction to the mathematical underpinnings.
Why is it vital to find out about GANs and reinforcement studying?
The primary GANs paper had simply come out two years earlier than we began engaged on the second version, however we weren’t certain of its relevance. Nonetheless, GANs have developed into one of many hottest and most generally used deep studying strategies. Folks use them for creating art work, colorizing and bettering the standard of images, and to recreate previous online game textures in greater resolutions. It goes with out saying that an introduction to GANs was lengthy overdue.
One other vital machine studying subject not included in earlier editions is reinforcement studying, which has acquired an enormous enhance in consideration lately. Due to spectacular initiatives reminiscent of DeepMind’s AlphaGo and AlphaGo Zero, reinforcement studying has acquired intensive information protection. And only recently, it’s been used to compete with the world’s prime e-sports gamers within the real-time technique online game StarCraft II. We hope that our new chapters can present an accessible and sensible introduction to this thrilling discipline.
Python Machine Studying by Instance 4E
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Know-how Used
TensorFlow, scikit-learn PyTorch, scikit-learn PyTorch PyTorch, TensorFlow, pandas, NumPy, scikit-learn
Reader Information Stage
Newbie to Intermediate Newbie to intermediate Intermediate to Superior Newbie to Intermediate
New Matters
Revised and expanded to incorporate GANs and reinforcement studying New content material on transformers, gradient boosting, and GNNs New content material on diffusion fashions, recommender methods, cell deployment, Hugging Face, and GNNs Revised with PyTorch builds, expanded finest practices, and new content material on LLMs and multimodal fashions
Writer : Packt Publishing
Publication date : Dec 12 2019
Version : third ed.
Language : English
Print size : 772 pages
ISBN-10 : 1789955750
ISBN-13 : 978-1789955750
Merchandise weight : 1.43 kg
Dimensions : 19.05 x 4.42 x 23.5 cm
Finest Sellers Rank: #322,677 in Books (See Prime 100 in Books) #130 in AI Human Imaginative and prescient & Language Techniques #134 in A.I. Neural Networks #138 in Pure Language Processing Software program
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