Skip to main content
PProfileMatch
Read Play

Machine Learning Fundamentals Tutorial

Frame problems, prepare data, split it honestly and judge a first model with the right metrics

Beginner4 lessonsabout 20 min of reading

This course is for developers, analysts and students who want to understand machine learning without starting from heavy maths or a framework. If you can read basic Python (loops, lists, functions and dictionaries) you can follow every example. We deliberately use plain Python and the standard library, so you see exactly what a model does instead of calling a black box. The same ideas carry over unchanged when you move to libraries such as scikit-learn.

You will learn to decide whether a problem needs machine learning at all and how to write it down as a prediction task, how to turn messy records into numeric features without leaking information, how to split data into training, validation and test sets and recognise overfitting when it happens, and how to choose metrics that tell the truth about a model. You will build a learned threshold, a nearest-neighbour classifier, a least-squares line and a small metrics report, all from scratch.

The lessons follow the order a real project takes: frame the problem, prepare the data, split and tune, then measure. Every code sample was run and the output shown is exactly what it printed. When you are ready for specific algorithms in depth, continue with Supervised Learning, and if your Python needs a refresher, start with Python Fundamentals.

Start lesson 1: What Is Machine Learning? How to Frame a Problem (and When Not To)Ride it: tricks, quizzes, XP

Machine Learning Fundamentals lessons

  1. 1What Is Machine Learning? How to Frame a Problem (and When Not To)Learn what machine learning is, how a model learns from examples, how to frame a prediction problem with a target and a metric, and when not to use ML.5 min read
  2. 2Machine Learning Data Preparation: Cleaning, Encoding and ScalingPrepare data for machine learning in Python: handle missing values and bad placeholders, encode categories, scale features and avoid leaking test data.5 min read
  3. 3Train, Validation and Test Sets: Understanding OverfittingLearn why machine learning needs train, validation and test splits, see overfitting in a runnable nearest-neighbour example and how to tune without cheating.5 min read
  4. 4Machine Learning Metrics and Your First Model: Accuracy, Precision, RecallLearn why accuracy misleads, compute the confusion matrix, precision, recall and F1 in Python, and fit your first regression model with MAE and RMSE.5 min read

Preparing for an interview? Practise Machine Learning interview questions

Learning Machine Learning Fundamentals to land a job? Browse open roles or check which skills are trending.