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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Length 570 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Atul Tripathi

View More author details

Table of Contents (15) Chapters

Preface

1. Introduction to Machine Learning FREE CHAPTER

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

Exploring World Bank data

13. Case Study - Pricing Reinsurance Contracts

Pricing reinsurance contracts

14. Case Study - Forecast of Electricity Consumption

Introduction

World Bank Indicators (WDI) is the World Bank's compilation of internationally comparable and measurable statistics about the growth of global development and its impact on the quality of people's lives measuring the state of development in countries, regions and income groups by looking into data collected from over 200 economies and more than 50 partner organizations to present more than 1400 indicators. On September 25, 2015, the United Nations General Assembly formally adopted the 2030 agenda for sustainable development, to guide global action over the next 15 years. The five main focus themes of SDG's are people, planet, prosperity, peace, and partnership. Countries have resolved to end poverty and hunger and ensure that all people can fulfill their potential in dignity and equality and in a healthy environment; to protect the planet from degradation and take urgent action on climate change; to ensure that all people can enjoy prosperous and fulfilling...

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Authors (1)

Atul Tripathi

Atul Tripathi

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.

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