Machine Learning R Course

Welcome to our Machine Learning R Course! This comprehensive guide will help you understand the basics of machine learning and how to implement it using the R programming language.

Course Overview

• Introduction to Machine Learning: Learn the fundamentals of machine learning and its applications.
• R Programming Basics: Get started with R programming and its syntax.
• Data Preprocessing: Understand how to clean and prepare data for machine learning models.
• Supervised Learning: Explore various supervised learning algorithms such as linear regression, decision trees, and support vector machines.
• Unsupervised Learning: Discover unsupervised learning techniques like clustering and dimensionality reduction.
• Model Evaluation: Learn how to evaluate the performance of machine learning models.
• Real-World Projects: Work on practical projects to apply your knowledge.

Prerequisites

• Basic understanding of programming (especially R)
• Familiarity with statistics and linear algebra

Course Content

• Introduction to Machine Learning

  • What is machine learning?
  • Types of machine learning algorithms
  • Applications of machine learning

• R Programming Basics

  • Basic syntax and data structures
  • Data manipulation and visualization

• Data Preprocessing

  • Data cleaning
  • Feature engineering
  • Handling missing values

• Supervised Learning

  • Linear regression
  • Decision trees
  • Support vector machines
  • Neural networks

• Unsupervised Learning

  • Clustering
  • Dimensionality reduction
  • Association rules

• Model Evaluation

  • Accuracy, precision, recall, F1 score
  • Cross-validation
  • Model selection

• Real-World Projects

  • Build a recommendation system
  • Predict stock prices
  • Analyze customer churn

Additional Resources

For further reading, check out our Machine Learning with R course.

Machine Learning R