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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

Hidden Markov models - regime detection

Standard & Poor's 500 Index (S&P 500) is an American Stock market index of 500 stocks. It is a leading indicator of US equities and reflects the performance of large-cap companies selected by economists. Experts, when determining the 500 stocks, consider factors that are included in the index, including market size, liquidity, and industry grouping. It is a market value-weighted index and one of the common benchmarks for the US stock market. Investment products based on the S&P 500 include index funds and exchange-traded funds are available to investors. Investors have a challenge replicating the S&P 500 since the portfolio would need stocks of 500 companies in ratio to the entire portfolio to replicate the index's market cap methodology. For investors it would be easier to purchase one of the S&P 500 investment products such as the Vanguard S&P 500 ETF, the SPDR S&P 500 ETF...

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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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