Machine Learning using Advanced Algorithms and Visualization in R [Video]

More Information
Learn
  • Work with advanced algorithms and techniques to enable efficient machine learning using the R programming language
  • Explore concepts such as the random forest algorithm
  • Work with support vector machine and examine and plot the results
  • Find out how to use the K-Nearest Neighbor for data projection
  • Work with a variety of real-world algorithms that suit your problem
About

Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. The R language is widely used among statisticians and data miners to develop statistical software and data analysis.

In this course, you will work through various examples on advanced algorithms, and focus a bit more on some visualization options. We’ll start by showing you how to use random forest to predict what type of insurance a patient has based on their treatment and you will get an overview of how to use random forest/decision tree and examine the model. Then, we’ll walk you through the next example on letter recognition, where you will train a program to recognize letters using a support Vector machine, examine the results, and plot a confusion matrix.
After that, you will look into the next example on soil classification from satellite data using K-Nearest Neighbor where you will predict what neighborhood a house is in based on other data about it. Finally, you’ll dive into the last example of predicting a movie genre based on its title, where you will use the tm package and learn some techniques for working with text data.

Style and Approach

These videos cover more advanced algorithms in a step-by-step manner and focus a bit more on some visualization options. The video not only makes you aware of the available ML packages in R, but also shows you examples of how to use them, such as building an automated intelligent system. A variety of real-world problem types are used to illustrate these concepts.

Features
  • Dive into advanced algorithms such as decision trees and support vector machines
  • The practical, real-world examples will help you get acquainted with the various stages of machine learning using the R language
  • Look into important machine learning concepts such as random forest, vector machine, K-Nearest Neighbor, and tm package
Course Length 1 hour 15 minutes
ISBN 9781788294980
Date Of Publication 30 May 2017

Authors

Tim Hoolihan

Tim Hoolihan currently works at DialogTech, a marketing analytics company focused on conversations. He is the Senior Director of Data Science there. Prior to that, he was CTO at Level Seven, a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group.

In his job, he uses deep neural networks to help automate of a lot of conversation classification problems. In addition, he works on some side-projects researching other areas of Artificial Intelligence and Machine Learning. Personally, he enjoys working on practice problems on Kaggle.com as well. Outside Data Science, he is interested in mathematical computation in general; he is a lifelong math learner and really enjoys applying it wherever he can. Recently, he has been spending time in financial analysis, and game development. He also knows a variety of languages: R, Python, Ruby, PHP, C/C++, and so on. Previously, he worked in web application and mobile development.