Build a practical foundation in deep learning with R as you progress from data preparation to neural network testing and prediction. Designed for learners new to machine learning and those ready to expand into deep learning, this course shows you how to organize dataframes, configure working directories, assign variables, use essential R syntax, and apply descriptive statistics and Spearman correlation to examine data relationships.

You’ll create line graphs and scatter plots to identify trends and interpret complex datasets, then use linear regression to estimate and explain relationships between variables. With this predictive foundation in place, you’ll prepare financial and multivariable datasets for neural network training, execute neural network code, analyze hidden layers, and apply multilayer perceptron (MLP) syntax in R. By the end of the course, you’ll be able to design, run, test, and evaluate neural networks, generate model outputs, and predict outcomes for unseen data. What sets this course apart is its structured combination of statistical analysis, data visualization, regression modeling, and hands-on neural network development in R. Enroll to develop both the technical workflow and critical thinking needed to interpret model results in real-world predictive tasks.