Python Machine Learning Cookbook: Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets,2nd Edition
Authors: Giuseppe Ciaburro – Prateek Joshi
ISBN-10: 1789808456
ISBN-13: 9781789808452
Released: 2019-03-30
Print Length 页数: 642 pages
Book Description
More Information
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
About
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.
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
contents
1 The Realm of Supervised Learning
2 Constructing a Classifier
3 Predictive Modeling
4 Clustering with Unsupervised Learning
5 Visualizing Data
6 Building Recommendation Engines
7 Analyzing Text Data
8 Speech Recognition
9 Dissecting Time Series and Sequential Data
10 Analyzing Image Content
11 Biometric Face Recognition
12 Reinforcement Learning Techniques
13 Deep Neural Networks
14 Unsupervised Representation Learning
15 Automated Machine Learning and Transfer Learning
16 Unlocking Production Issues
