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Machine learning from zero
The core ideas of machine learning, explained with small, concrete examples.
What you will be able to do
- Explain supervised and unsupervised learning with examples
- Describe how training, validation and test data are used
- Recognise overfitting and data leakage
- Choose a sensible metric for a classification problem
- Explain at a high level how a neural network learns
Lesson plan
- 1 Learning from examples Understand what it means for a model to learn from data. Start
- 2 Features and labels Turn a real problem into inputs and an output. Start
- 3 Training, validation and test sets Measure performance honestly. Start
- 4 Overfitting and underfitting Recognise when a model is too simple or too complex. Start
- 5 Judging a classifier Pick metrics that match the real cost of mistakes. Start
- 6 Neural networks and deep learning Understand how neural networks learn, at a high level. Start
- 7 From model to real use Know what happens after training. Start
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About this tutor
Learn how machines learn from data: training, testing, overfitting and how models are judged. Light on maths and heavy on intuition, with optional small Python examples for those who want them.
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About the teacher
Teaches machine learning, Python and AI agents
3 tutors
I teach the technical side of AI to people who are new to code or to machine learning. My tutors explain the idea first, then show a small working example, then ask you to change it and see what happens.
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