Processing ......
FreeComputerBooks.com
Free Computer, Mathematics, Technical Books and Lecture Notes, etc.
 
Data Science Algorithms in a Week: Top 7 Algorithms for Computing, Data Analysis, and Machine Learning
How many Airports in your home state/province? Click here to find out.
  • Title Data Science Algorithms in a Week: Top 7 Algorithms for Computing, Data Analysis, and Machine Learning
  • Author(s) David Natingga
  • Publisher: Packt Publishing (August 16, 2017)
  • Paperback 210 pages
  • eBook PDF
  • Language: English
  • ISBN-10/ASIN: 1787284581
  • ISBN-13: 978-1787284586
  • Share This:  

Book Description

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.

It 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.

  • 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 the Authors
  • David Natingga is a mathematician and theoretical computer scientist of a Slovak descent working on the mathematics of a mind.
Reviews, Rating, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
Book Categories
Other Categories
Resources and Links