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Python Machine Learning Cookbook
Python Machine Learning Cookbook

Python Machine Learning Cookbook: Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets , Second Edition

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Profile Icon Giuseppe Ciaburro Profile Icon Prateek Joshi
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₹800 per month
Paperback Mar 2019 642 pages 2nd Edition
eBook
₹999.99 ₹2442.99
Paperback
₹3053.99
Subscription
Free Trial
Renews at ₹800p/m
Arrow left icon
Profile Icon Giuseppe Ciaburro Profile Icon Prateek Joshi
Arrow right icon
₹800 per month
Paperback Mar 2019 642 pages 2nd Edition
eBook
₹999.99 ₹2442.99
Paperback
₹3053.99
Subscription
Free Trial
Renews at ₹800p/m
eBook
₹999.99 ₹2442.99
Paperback
₹3053.99
Subscription
Free Trial
Renews at ₹800p/m

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Python Machine Learning Cookbook

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Key benefits

  • Learn and implement machine learning algorithms in a variety of real-life scenarios
  • Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques
  • Find easy-to-follow code solutions for tackling common and not-so-common challenges

Description

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.

Who is this book for?

This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.

What you will learn

  • Use predictive modeling and apply it to real-world problems
  • Explore data visualization techniques to interact with your data
  • Learn how to build a recommendation engine
  • Understand how to interact with text data and build models to analyze it
  • Work with speech data and recognize spoken words using Hidden Markov Models
  • Get well versed with reinforcement learning, automated ML, and transfer learning
  • Work with image data and build systems for image recognition and biometric face recognition
  • Use deep neural networks to build an optical character recognition system

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 30, 2019
Length: 642 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789808452
Vendor :
Google
Category :
Languages :
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What do you get with a Packt Subscription?

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Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
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Product Details

Publication date : Mar 30, 2019
Length: 642 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789808452
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Table of Contents

17 Chapters
The Realm of Supervised Learning Chevron down icon Chevron up icon
Constructing a Classifier Chevron down icon Chevron up icon
Predictive Modeling Chevron down icon Chevron up icon
Clustering with Unsupervised Learning Chevron down icon Chevron up icon
Visualizing Data Chevron down icon Chevron up icon
Building Recommendation Engines Chevron down icon Chevron up icon
Analyzing Text Data Chevron down icon Chevron up icon
Speech Recognition Chevron down icon Chevron up icon
Dissecting Time Series and Sequential Data Chevron down icon Chevron up icon
Analyzing Image Content Chevron down icon Chevron up icon
Biometric Face Recognition Chevron down icon Chevron up icon
Reinforcement Learning Techniques Chevron down icon Chevron up icon
Deep Neural Networks Chevron down icon Chevron up icon
Unsupervised Representation Learning Chevron down icon Chevron up icon
Automated Machine Learning and Transfer Learning Chevron down icon Chevron up icon
Unlocking Production Issues Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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