Data Science Algorithms in a Week - Second Edition

Build a strong foundation of machine learning algorithms in 7 days

Data Science Algorithms in a Week - Second Edition

Dávid Natingga
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Build a strong foundation of machine learning algorithms in 7 days
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Book Details

ISBN 139781789806076
Paperback214 pages

Book Description

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world 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 pre-cluster 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 k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series 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

Table of Contents

What You Will Learn

  • Understand how to identify a data science problem correctly
  • Implement well-known 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 k-means algorithm

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Table of Contents

Book Details

ISBN 139781789806076
Paperback214 pages
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