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Book Overview & Buying
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Table Of Contents
Practical Machine Learning Cookbook
By :
Practical Machine Learning Cookbook
By:
Overview of this book
Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations.
The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more.
The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
Table of Contents (15 chapters)
Preface
1. Introduction to Machine Learning
2. Classification
3. Clustering
4. Model Selection and Regularization
5. Nonlinearity
6. Supervised Learning
7. Unsupervised Learning
8. Reinforcement Learning
9. Structured Prediction
10. Neural Networks
11. Deep Learning
12. Case Study - Exploring World Bank Data
13. Case Study - Pricing Reinsurance Contracts
14. Case Study - Forecast of Electricity Consumption