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Data Science Algorithms in a Week

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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
About

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.

Features
  • Get to know seven algorithms for your data science needs in this concise, insightful guide
  • Ensure you’re confident in the basics by learning when and where to use various data science algorithms
  • Learn to use machine learning algorithms in a period of just 7 days
Page Count 210
Course Length 6 hours 18 minutes
ISBN 9781787284586
Date Of Publication 15 Aug 2017
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

Authors

Dávid Natingga

Dávid Natingga graduated with a master's in engineering in 2014 from Imperial College London, specializing in artificial intelligence. In 2011, he worked at Infosys Labs in Bangalore, India, undertaking research into the optimization of machine learning algorithms. In 2012 and 2013, while at Palantir Technologies in USA, he developed algorithms for big data. In 2014, while working as a data scientist at Pact Coffee, London, he created an algorithm suggesting products based on the taste references of customers and the structures of the coffees. In order to use pure mathematics to advance the field of AI, he is a PhD candidate in Computability Theory at the University of Leeds, UK. In 2016, he spent 8 months at Japan's Advanced Institute of Science and Technology as a research visitor.