Mastering Machine Learning in R: Basics to Advanced

Forecasting & Clustering Techniques, Classification, Regression, Time Series & Dimensionality Reduction
What you'll learn
- R Programming Fundamentals: Data types, data frames, CSV handling, exploratory analysis, and advanced plotting.
- Classification Techniques: k-NN, logistic regression, decision trees, random forests, SVMs, and performance evaluation.
- Neural Networks in R: From theory to implementation using caret, including training strategies and overfitting prevention.
- Feature & Model Selection: Variable importance, automatic selection, ridge regression, LASSO, and dimensionality reduction.
- Regression Analysis: Linear models, interactions, categorical regressors, and non-parametric methods like GAMs.
- Time Series Forecasting: Simple methods, decomposition, exponential smoothing, and ARIMA/SARIMA models.
- Dynamic Regression Models: Integrating ARIMA with linear regression and transfer function identification.
- Clustering Techniques: k-means, hierarchical, PAM, CLARA, DBSCAN, Gaussian Mixture Models, and cluster validation.
- Dimensionality Reduction: PCA, correspondence analysis, factor analysis, and VARIMAX rotation.

Requirements
- No prior experience with R required
- A computer with internet access and R/RStudio installed.
- Curiosity and willingness to learn through hands-on coding.

Description
Transform Your Data Skills with One of the Most Comprehensive Machine Learning and R Courses OnlineAre you ready to become a data science expert? Unlock the full power of R for data science, machine learning, and forecasting in this all-in-one, hands-on course designed for aspiring analysts, data scientists, and researchers. Whether you're just starting with machine learning and R or looking to master advanced modeling techniques, this course guides you through every essential concept—from data manipulation and visualization to neural networks and time series forecasting.

With over 100 expertly crafted lectures, you'll gain practical experience using R and its most powerful libraries, including caret, ggplot2, forecast, and mclust. Each section builds on the last, ensuring a smooth learning curve and a deep understanding of both theory and application.

Why This Course Stands Out

This isn’t just another R tutorial. It’s a practical, project-based learning experience that blends theory with hands-on coding. You’ll explore real-world problems, build models, validate results, and visualize insights—all using R and its powerful ecosystem of packages like caret, ggplot2, forecast, and mclust.

Each section is carefully crafted to build your skills progressively, with clear explanations, coding walkthroughs, and practical examples that make complex concepts easy to grasp.

Course Features

100+ Lectures across 10 comprehensive sections

Real-world examples and datasets

Step-by-step coding walkthroughs

Quizzes and exercises to reinforce learning

Lifetime access and downloadable resources

Certificate of completion

Who this course is for:
- Professionals preparing for roles in machine learning, forecasting, or statistical modeling.
- Beginners in R who want a structured and practical introduction.
- Researchers and academics working with time series, classification, or clustering.
- Data analysts and scientists looking to expand their modeling toolkit.
More Info