Machine Learning with R: Build Real-World Models

Learn classification, regression, forecasting, clustering, and neural networks using R - real-world projects

What you'll learn
- Develop fluency in R programming for data analysis and machine learning, including working with data frames, packages, and visualization tools.
- Preprocess and clean datasets effectively, handling missing values, outliers, and class imbalance to prepare data for modeling.
- Build and evaluate classification models such as k-NN, logistic regression, decision trees, random forests, and support vector machines.
- Apply regression techniques including linear, polynomial, and non-parametric models, and assess model fit and performance.
- Engineer and select features using variable importance, regularization (LASSO, Ridge), and dimensionality reduction methods like PCA and Factor Analysis.
- Design and train neural networks in R, understanding key concepts like gradient descent, backpropagation, and overfitting.
- Forecast time series data using ARIMA, SARIMA, and ETS models, and compare forecasting approaches for accuracy and reliability.
- Implement dynamic regression models by integrating ARIMA noise into linear regression and identifying transfer function models.
- Perform clustering and unsupervised learning using k-means, hierarchical clustering, Gaussian Mixture Models, DBSCAN, and cluster validation techniques.
- Utilize essential R libraries such as caret, ggplot2, forecast, rpart, and mclust to build, tune, and deploy machine learning models efficiently.

Requirements
- Basic knowledge of statistics
- Familiarity with programming fundamentals
- R and RStudio installed
- Curiosity about data and modeling
- Basic spreadsheet skills (optional)

Description
Machine learning is transforming industries by enabling systems to learn from data, identify patterns, and make decisions with minimal human intervention. At its core, machine learning is about building models that can generalize from examples—whether it's predicting customer behavior, diagnosing medical conditions, or forecasting market trends.

This course series is a comprehensive, hands-on journey into the world of machine learning using R, a powerful language for statistical computing and data analysis. Designed for learners at all levels, the course demystifies machine learning concepts and equips students with the practical skills needed to build intelligent systems.

The series is divided into eight focused modules, each tackling a key area of machine learning:

Introduction to R for Machine Learning: Learn the foundations of R programming, data types, data frames, and essential packages like caret and ggplot2.

Classification: Explore supervised learning techniques to categorize data using algorithms like k-NN, logistic regression, decision trees, random forests, and support vector machines.

Regression: Understand how to model relationships between variables using linear, polynomial, and non-parametric regression methods.

Feature Selection and Dimensionality Reduction: Discover how to improve model performance by selecting relevant features and reducing complexity using PCA, LASSO, and Ridge regression.

Neural Networks: Dive into the basics of deep learning, including how neural networks learn, train, and generalize using gradient descent and backpropagation.

Time Series Forecasting: Learn to model and predict temporal data using ARIMA, SARIMA, and exponential smoothing techniques.

Dynamic Regression Models: Combine regression with time series modeling to capture complex patterns in sequential data.

Clustering and Unsupervised Learning: Apply clustering algorithms like k-means, hierarchical clustering, Gaussian Mixture Models, and DBSCAN to uncover hidden structures in data.

Throughout the course, students will gain hands-on experience by writing R code, analyzing real datasets, and building models from scratch. The emphasis is on practical application, with each concept tied to real-world scenarios and challenges.

By the end of the course, students will not only understand the theory behind machine learning but also be able to implement, evaluate, and optimize models using R. Whether you're a data analyst, researcher, student, or professional looking to upskill, this course provides a solid foundation and advanced techniques to thrive in the data-driven world.

Who this course is for:
- Students and professionals entering the field of machine learning
- Data analysts and scientists looking to expand their R skills
- Researchers working with statistical modeling and forecasting
- Anyone interested in practical, hands-on machine learning with R
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