Deep Learning with R: Build & Predict Neural Networks

Overview

By completing this course, learners will be able to prepare datasets in R, apply statistical and visualization techniques, build regression models, and design, run, and evaluate neural networks. The course begins with data preparation essentials, including working with dataframes, descriptive statistics, and environment setup, ensuring learners can confidently manage their workflow. It then advances to data visualization, where learners generate line graphs, scatter plots, and advanced visualizations to interpret patterns and relationships. Regression modeling concepts are introduced to provide a solid predictive foundation. Finally, the course transitions to deep learning, guiding learners through dataset preparation, neural network coding, multilayer perceptron (MLP) architecture, and predictive testing. What makes this course unique is its balance of theory and hands-on application using R, a widely used tool in both academia and industry. Learners not only gain the technical skills to execute commands and build models but also develop the critical thinking needed to evaluate results in real-world contexts. Whether new to machine learning or seeking to expand into deep learning, this course provides a structured, practical pathway to mastering neural networks with R.

Syllabus

Data Preparation and Environment Setup

This module introduces learners to the fundamentals of working with R for data science and deep learning projects. Learners will explore dataframes, descriptive statistics, directory setup, variable assignment, and essential R syntax. The module ensures that learners can confidently prepare their environment and datasets before advancing to complex modeling.

Data Visualization and Regression Foundations

This module focuses on building strong visualization and regression skills in R. Learners will generate various plots such as line graphs, scatter plots, and multiple plot frames to explore data patterns. The module also introduces regression modeling concepts, including linear and multiple regression, to establish a strong foundation for predictive modeling.

Neural Networks with R

This module transitions learners from regression models to deep learning with neural networks in R. It covers preparing datasets, running neural network code, analyzing hidden layers, and evaluating model predictions. By the end of the module, learners will be able to design, execute, and test neural networks for real-world predictive tasks.