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Book details
Format: epub
File Size: 15 MB
Print Length: 642 pages
Publisher: Packt Publishing; 2 edition (30 March 2019)
Sold by: Amazon Asia-Pacific Holdings Private Limited
Language: English
ASIN: B07Q4TBR8G

Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch

Key Features

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
Book 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.

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
Who this book is 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.

Table of Contents

The Realm of Supervised Learning
Constructing a Classifier
Predictive Modeling
Clustering with Unsupervised Learning
Visualizing Data
Building Recommendation Engines
Analyzing Text Data
Speech Recognition
Dissecting Time Series and Sequential Data
Image Content Analysis
Biometric Face Recognition
Reinforcement Learning Techniques
Deep Neural Networks
Unsupervised Representation Learning
Automated machine learning and Transfer learning
Unlocking Production issues