You're reading from Data Science Algorithms in a Week Top 7 algorithms for scientific computing, data analysis, and machine learning

Product type Paperback

Published in Oct 2018

Publisher Packt

ISBN-13 9781789806076

Length 214 pages

Edition 2nd Edition

Languages

Python

Tools

Devise

Concepts

Data Analysis

Authors (2):

David Toth

David Natingga

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Table of Contents (12) Chapters

Preface

Who this book is for
What this book covers
To get the most out of this book
Get in touch

1. Classification Using K-Nearest Neighbors

Mary and her temperature preferences
Implementation of the k-nearest neighbors algorithm
Map of Italy example – choosing the value of k
House ownership – data rescaling
Text classification – using non-Euclidean distances
Text classification – k-NN in higher dimensions
Summary
Problems

2. Naive Bayes FREE CHAPTER

Medical tests – basic application of Bayes' theorem
Bayes' theorem and its extension
Playing chess – independent events
Implementation of a Naive Bayes classifier
Playing chess – dependent events
Gender classification – Bayes for continuous random variables
Summary
Problems

3. Decision Trees

Swim preference – representing data using a decision tree
Information theory
ID3 algorithm – decision tree construction
Classifying with a decision tree
Playing chess – analysis with a decision tree
Going shopping – dealing with data inconsistencies
Summary
Problems

4. Random Forests

Introduction to the random forest algorithm
Swim preference – analysis involving a random forest
Implementation of the random forest algorithm
Playing chess example
Going shopping – overcoming data inconsistencies with randomness and measuring the level of confidence
Summary
Problems

5. Clustering into K Clusters

Household incomes – clustering into k clusters
Gender classification – clustering to classify
Implementation of the k-means clustering algorithm
House ownership – choosing the number of clusters
Document clustering – understanding the number of k clusters in a semantic context
Summary
Problems

6. Regression

Fahrenheit and Celsius conversion – linear regression on perfect data
Weight prediction from height – linear regression on real-world data
Gradient descent algorithm and its implementation
Flight time duration prediction based on distance
Ballistic flight analysis – non-linear model
Summary
Problems

7. Time Series Analysis

Business profits – analyzing trends
Electronics shop's sales – analyzing seasonality
Summary
Problems

8. Python Reference

Introduction
Data types
Flow control
Input and output

9. Statistics

Basic concepts
Bayesian inference
Distributions
Cross-validation
A/B testing

10. Glossary of Algorithms and Methods in Data Science

11. Other Books You May Enjoy

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Table of Contents (12) Chapters

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Table of Contents (12) Chapters

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