Practical Machine Learning Cookbook

Key Features

• Implement a wide range of algorithms and techniques for tackling complex data
• Improve predictions and recommendations to have better levels of accuracy
• Optimize performance of your machine-learning systems

Book Description

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.

What You Will Learn

• Get equipped with a deeper understanding of how to apply machine-learning techniques
• Implement each of the advanced machine-learning techniques
• Solve real-life problems that are encountered in order to make your applications produce improved results
• Gain hands-on experience in problem solving for your machine-learning systems
• Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model’s performance, and improving the model’s performance

About the Author

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

Table of Contents

Chapter 1: Introduction to Machine Learning
Chapter 2: Classification
Chapter 3: Clustering
Chapter 4: Model Selection and Regularization
Chapter 5: Nonlinearity
Chapter 6: Supervised Learning
Chapter 7: Unsupervised Learning
Chapter 8: Reinforcement Learning
Chapter 9: Structured Prediction
Chapter 10: Neural Networks
Chapter 11: Deep Learning
Chapter 12: Case Study – Exploring World Bank Data
Chapter 13: Case Study – Pricing Reinsurance Contracts
Chapter 14: Case Study – Forecast of Electricity Consumption