Machine Learning with R: Build, Analyze & Predict

Overview

By the end of this course, learners will be able to identify machine learning foundations, apply statistical concepts, evaluate probability distributions, and implement core algorithms in R. Participants will gain practical skills in data manipulation, regression, classification, decision trees, and ensemble learning, building a comprehensive understanding of both theory and application. This course is designed for students, data enthusiasts, and professionals seeking to master machine learning using R. Learners will benefit from hands-on practice with R programming, exposure to statistical modeling, and guidance on avoiding common mistakes in data analysis. Through real-world examples and structured quizzes, participants will strengthen their ability to clean, analyze, and interpret data with confidence. What makes this course unique is its integration of R programming with machine learning foundations, offering a step-by-step approach from statistical basics to advanced algorithms like random forests and boosting. Unlike generic courses, it emphasizes both conceptual clarity and practical implementation, ensuring learners can directly apply techniques to solve real-world problems effectively.

Syllabus

Getting Started with R and Machine Learning

This module introduces the foundations of Machine Learning and the R programming environment. Learners will explore the key concepts of supervised and unsupervised learning, regression versus classification, and the practical steps to apply machine learning to real-world problems. In addition, the module covers essential R programming skills for data manipulation, vector operations, and dataset preparation, ensuring a strong foundation for statistical and machine learning tasks.

Fundamentals of Statistics in R

This module covers statistical concepts essential for building and interpreting machine learning models. Learners will review core measures such as variance, correlation, R-squared, and standard error while identifying common statistical mistakes. The module also extends to advanced topics including linear regression, statistical assumptions, and interpretation of outputs, equipping learners with the ability to analyze data with confidence.

Probability Distributions and Hypothesis Testing

This module focuses on probability distributions and hypothesis testing, both critical to statistical inference. Learners will examine discrete and continuous probability distributions, variance-covariance structures, and hypothesis rejection criteria. The module also introduces classical distributions such as t, chi-square, and Poisson, along with visualization techniques for testing data assumptions and interpreting results.

Core Machine Learning Algorithms

This module introduces core machine learning algorithms, focusing on regression, classification, decision trees, and ensemble methods. Learners will explore K-Nearest Neighbors (KNN), generalized regression models, decision tree classifiers, and the use of pruning to improve performance. The module concludes with ensemble learning techniques, including random forests and boosting, for building powerful predictive models.