The Machine Learning Workflow in R: A Hands-On Course

Machine learning is far more than fitting a model: it is the disciplined path from a fuzzy business question to an honest, reproducible prediction you can trust. This four-lesson interactive course teaches that whole workflow in R from the ground up, with one running example carried the whole way through.

Most tutorials drop you straight into lm() or randomForest() and call that machine learning. The hard parts live on either side of that one line: deciding what to predict and why, choosing a model that neither underfits nor overfits, evaluating it without fooling yourself, and stitching it all into a script you can run again tomorrow. Get those right and the modeling really is almost mechanical.

This is the first stop on the Data Scientist path. Each lesson is a guided, interactive experience: you manipulate live diagrams in the browser, answer checkpoints, and write R as you go.

The four lessons

Lesson 1: Framing a problem as machine learning

Turn a plain business question ("who is about to stop buying?") into a well-posed supervised task: pick the target, the unit of analysis, the features known at prediction time, and the metric that matches the decision. The move that decides whether the whole project succeeds.

Start Lesson 1: Framing a problem as machine learning

Lesson 2: The bias-variance tradeoff

Underfitting and overfitting, seen as bias versus variance, and the U-shaped test-error curve that connects them. Slide a model from too simple to too flexible and watch why "more powerful" is not the same as "better."

Start Lesson 2: The bias-variance tradeoff

Lesson 3: Train, validation, test, and data leakage

Why you split data three ways, and the cardinal sin that quietly inflates almost every beginner's accuracy: leakage. Spot fitting on the test set, target leakage, and temporal leakage, and learn to evaluate honestly.

Start Lesson 3: Train, validation, test, and data leakage

Lesson 4: Your first end-to-end model in R

One dataset from start to finish: split, fit, predict, evaluate, and make it all reproducible with a seed and a script. Ends with the path to your Data Scientist certificate.

Start Lesson 4: Your first end-to-end model in R

Who this is for

You can run R and load a package with library(), and you have seen a data frame before. You do not need any prior machine learning experience. By the end you will be able to take a business question and turn it into a trained, honestly evaluated, reproducible model.

What you will be able to do

• Frame a vague question as a supervised learning task with a clear target and metric
• Diagnose underfitting and overfitting through the lens of bias and variance
• Split data correctly and recognise the common forms of data leakage
• Fit, predict, and evaluate a model end to end in R, reproducibly

Ready? Begin with Lesson 1.