Book description
Build a strong foundation of machine learning algorithms in 7 days
Key Features
 Use Python and its wide array of machine learning libraries to build predictive models
 Learn the basics of the 7 most widely used machine learning algorithms within a week
 Know when and where to apply data science algorithms using this guide
Book Description
Machine learning applications are highly automated and selfmodifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various realworld data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well.
Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to precluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as knearest neighbors, Naive Bayes, decision trees, random forest, kmeans, regression, and timeseries analysis.
By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
What you will learn
 Understand how to identify a data science problem correctly
 Implement wellknown machine learning algorithms efficiently using Python
 Classify your datasets using Naive Bayes, decision trees, and random forest with accuracy
 Devise an appropriate prediction solution using regression
 Work with time series data to identify relevant data events and trends
 Cluster your data using the kmeans algorithm
Who this book is for
This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You'll also find this book useful if you're currently working with data science algorithms in some capacity and want to expand your skill set
Table of contents
 Title Page
 Copyright and Credits
 Packt Upsell
 Contributors
 Preface

Classification Using KNearest Neighbors
 Mary and her temperature preferences
 Implementation of the knearest neighbors algorithm
 Map of Italy example – choosing the value of k
 House ownership – data rescaling
 Text classification – using nonEuclidean distances
 Text classification – kNN in higher dimensions
 Summary
 Problems

Naive Bayes
 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
 Decision Trees
 Random Forests

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

Regression
 Fahrenheit and Celsius conversion – linear regression on perfect data
 Weight prediction from height – linear regression on realworld data
 Gradient descent algorithm and its implementation
 Flight time duration prediction based on distance
 Ballistic flight analysis – nonlinear model
 Summary
 Problems
 Time Series Analysis
 Python Reference
 Statistics
 Glossary of Algorithms and Methods in Data Science
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Product information
 Title: Data Science Algorithms in a Week  Second Edition
 Author(s):
 Release date: October 2018
 Publisher(s): Packt Publishing
 ISBN: 9781789806076
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