Machine Learning using Advanced Algorithms and Visualization in R

Machine Learning using Advanced Algorithms and Visualization in R
Machine Learning using Advanced Algorithms and Visualization in R

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 15m | 405 MB
eLearning | Skill level: All Levels


Explore advanced algorithm concepts such as random forest vector machine, K- nearest, and more through real-world examples

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.

What You Will 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
+ Table of Contents

Random Forest
The Course Overview
Random Forest Overview
Exploring the Vote92 Data Set
Using a Random Forest Model
Examining the model
New Model and Final Results

Support Vector Machines
SVM Overview and EDA
Building an SVM Model
Examining the Results and Model
Visualizing a Confusion Matrix

K-Nearest Neighbor
Overview of Satellite Data
Overview of K-Nearest Neighbor
Using KNN
Visualizing KNN Results

Movie Reviews: Working With Text
Overview of Movie Review Data
Overview of Document Vectors
Classifying Document Matrices
Clustering Documents
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