Data Science Algorithms in a Week

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Key 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

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.

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

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eBook Details:
Category: Algorithms
Author: David Natingga
Language: English
ISBN10: 1787284581
ISBN13: 9781787284586
Pages: 215
PubDate: 2017-09-11 00:00:00
UploadDate: 8/21/2017 10:42:46 am

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