AI / ML · Course
Intro to Machine Learning
Build your first real machine learning models, no experience needed. Every lesson has a live example you run and a short exercise you solve, right in your browser with real scikit-learn. You will learn the core loop of machine learning, explore data, train and validate models, avoid overfitting, and use a random forest, all on a relatable dataset of student exam results.
What you’ll learn
- Understand what a machine learning model is and how it learns
- Explore a dataset before modelling it
- Train your first model: define, fit, predict
- Validate honestly with a train/test split and mean absolute error
- Recognise and avoid underfitting and overfitting
- Use a random forest, a strong everyday model
Ends with a hands-on capstone
You’ll finish by building a real project — Predict Student Exam Scores. Complete and pass it to earn your certificate and add it to your portfolio.
All lessons free — start below
How Machine Learning Works · Lesson 1 of 7
How models work
What machine learning really is
Machine learning is teaching a computer to find patterns in data, then use those patterns to make predictions about new data it has never seen.
Imagine you want to predict a student's exam score. You have records of many students: how many hours they studied, their attendance, their score in a previous test, and the exam score they finally earned. A machine learning model studies those records, learns how these things relate to the final score, and can then estimate the score of a student it has never seen.
Some words you will use the whole way through:
- The records you learn from are the training data.
- Each input (study hours, attendance, previous score) is a feature.
- The thing you want to predict (the exam score) is the target.
A simple but powerful model is the decision tree. It splits the data with yes or no questions, like "did the student study more than 5 hours?", until it reaches a sensible prediction at the end of each branch. Ask enough good questions and you get a good estimate.
You do not need heavy maths to begin. You need data, a model, and four steps you will practice in this course: define, fit, predict, evaluate.
Practice
Think of something you would like to predict: exam scores, crop yield, shop sales. Write down the target, and three features that might help predict it. You will reuse this way of thinking throughout the course.