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R Data Analysis Cookbook, Second Edition

You're reading from   R Data Analysis Cookbook, Second Edition Customizable R Recipes for data mining, data visualization and time series analysis

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787124479
Length 560 pages
Edition 2nd Edition
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Authors (3):
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Kuntal Ganguly Kuntal Ganguly
Author Profile Icon Kuntal Ganguly
Kuntal Ganguly
Shanthi Viswanathan Shanthi Viswanathan
Author Profile Icon Shanthi Viswanathan
Shanthi Viswanathan
Viswa Viswanathan Viswa Viswanathan
Author Profile Icon Viswa Viswanathan
Viswa Viswanathan
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Table of Contents (14) Chapters Close

Preface 1. Acquire and Prepare the Ingredients - Your Data FREE CHAPTER 2. What's in There - Exploratory Data Analysis 3. Where Does It Belong? Classification 4. Give Me a Number - Regression 5. Can you Simplify That? Data Reduction Techniques 6. Lessons from History - Time Series Analysis 7. How does it look? - Advanced data visualization 8. This may also interest you - Building Recommendations 9. It's All About Your Connections - Social Network Analysis 10. Put Your Best Foot Forward - Document and Present Your Analysis 11. Work Smarter, Not Harder - Efficient and Elegant R Code 12. Where in the World? Geospatial Analysis 13. Playing Nice - Connecting to Other Systems

Application of ML algorithms - image recognition system

Machine learning or ML algorithms are widely used in various domains and are generally a way of fine-tuning systems with tunable parameters, thereby making the system better with examples in a supervised or unsupervised way. Nowadays, researchers are pushing machine learning with the help of deep neural networks to perform outstanding tasks, which only humans were capable of doing before.

Though it is impossible to list every use case or example of machine learning, we will list some of the most prominent applications:

  • Identification of unwanted spam messages in emails
  • Segmentation of customer behavior for targeted advertising
  • Forecast of weather behavior and long-term climate changes
  • Detection of fraudulent credit card transactions
  • Actuarial estimates of financial damage of storms and natural disasters
  • Recommendation...
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