Data Science Algorithms in a Week

Build strong foundation of machine learning algorithms In 7 days.
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Data Science Algorithms in a Week

Dávid Natingga

5 customer reviews
Build strong foundation of machine learning algorithms In 7 days.
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Book Details

ISBN 139781787284586
Paperback210 pages

Book Description

Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis.

This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets.

This book covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem.

Table of Contents

Chapter 1: Classification Using K Nearest Neighbors
Mary and her temperature preferences
Implementation of 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
Chapter 2: Naive Bayes
Medical test - basic application of Bayes' theorem
Proof of Bayes' theorem and its extension
Playing chess - independent events
Implementation of naive Bayes classifier
Playing chess - dependent events
Gender classification - Bayes for continuous random variables
Summary
Problems
Chapter 3: Decision Trees
Swim preference - representing data with decision tree
Information theory
ID3 algorithm - decision tree construction
Classifying with a decision tree
Playing chess - analysis with decision tree
Going shopping - dealing with data inconsistency
Summary
Problems
Chapter 4: Random Forest
Overview of random forest algorithm
Swim preference - analysis with random forest
Implementation of random forest algorithm
Playing chess example
Going shopping - overcoming data inconsistency with randomness and measuring the level of confidence
Summary
Problems
Chapter 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 clusters k in a semantic context
Summary
Problems
Chapter 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 from distance
Ballistic flight analysis – non-linear model
Summary
Problems
Chapter 7: Time Series Analysis
Business profit - analysis of the trend
Electronics shop's sales - analysis of seasonality
Summary
Problems
Chapter 8: Statistics
Basic concepts
Bayesian Inference
Distributions
Cross-validation
A/B Testing
Chapter 9: R Reference
Introduction
Data types
Linear regression
Chapter 10: Python Reference
Introduction
Data types
Flow control
Chapter 11: Glossary of Algorithms and Methods in Data Science

What You Will Learn

  • Find out how to classify using Naive Bayes, Decision Trees, and Random Forest to achieve accuracy to solve complex problems
  • Identify a data science problem correctly and devise an appropriate prediction solution using Regression and Time-series
  • See how to cluster data using the k-Means algorithm
  • Get to know how to implement the algorithms efficiently in the Python and R languages

Authors

Table of Contents

Chapter 1: Classification Using K Nearest Neighbors
Mary and her temperature preferences
Implementation of 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
Chapter 2: Naive Bayes
Medical test - basic application of Bayes' theorem
Proof of Bayes' theorem and its extension
Playing chess - independent events
Implementation of naive Bayes classifier
Playing chess - dependent events
Gender classification - Bayes for continuous random variables
Summary
Problems
Chapter 3: Decision Trees
Swim preference - representing data with decision tree
Information theory
ID3 algorithm - decision tree construction
Classifying with a decision tree
Playing chess - analysis with decision tree
Going shopping - dealing with data inconsistency
Summary
Problems
Chapter 4: Random Forest
Overview of random forest algorithm
Swim preference - analysis with random forest
Implementation of random forest algorithm
Playing chess example
Going shopping - overcoming data inconsistency with randomness and measuring the level of confidence
Summary
Problems
Chapter 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 clusters k in a semantic context
Summary
Problems
Chapter 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 from distance
Ballistic flight analysis – non-linear model
Summary
Problems
Chapter 7: Time Series Analysis
Business profit - analysis of the trend
Electronics shop's sales - analysis of seasonality
Summary
Problems
Chapter 8: Statistics
Basic concepts
Bayesian Inference
Distributions
Cross-validation
A/B Testing
Chapter 9: R Reference
Introduction
Data types
Linear regression
Chapter 10: Python Reference
Introduction
Data types
Flow control
Chapter 11: Glossary of Algorithms and Methods in Data Science

Book Details

ISBN 139781787284586
Paperback210 pages
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