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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Pages 570 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Atul Tripathi

Table of Contents (21) Chapters

Practical Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

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

Introduction

Smoothing splines - understanding cars and speed

In order to determine the parameters required in statistics for fitting a model, multiple methods can be used. In each of the cases, fitting involves the estimating of a small number of parameters from the data. Apart from estimating parameters, two important stages are the identification of a suitable model and the verification of the model. These smoothing methods can be used in a variety of ways: to aid understanding and produce smoothed plots, to identify a suitable parametric model from the shape of the smoothed data, or to focus on the effects of interest in order to eliminate complex effects which are of no use.

How to do it...

Let's get into the details.

Step 1 - exploring the data

The first step is to load the following packages:

> install.packages("graphics")
> install.packages("splines")
> library(graphics)
> library(splines)

Creating a matrix. The cbind() function takes the sequence of numbers and creates a matrix. The...

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Authors (1)

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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