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Machine learning foundations
Regression, classification and how models generalise.
The foundations of machine learning: supervised learning, linear and logistic regression, gradient descent and the bias-variance tradeoff, starting from Andrew Ng's first Stanford CS229 lecture.
intermediateabout 4 weeks at 60 min a day5 free sources
What you'll cover
Phase 1
Supervised learning
- What learning from data means
- Linear regression
- Gradient descent
Phase 2
Classification
- Logistic regression
- Decision boundaries
- Probabilistic interpretation
Phase 3
Generalisation
- Overfitting and underfitting
- The bias-variance tradeoff
- Regularisation
An outline. Your own plan is made when you start, around your level and deadline.
Sources
Free, and fetched for you
- LectureStanford CS229: Machine Learning, Lecture 1 (Andrew Ng)youtube.com ↗
- Web pageLinear regression (Wikipedia)en.wikipedia.org ↗
- Web pageGradient descent (Wikipedia)en.wikipedia.org ↗
- Web pageLogistic regression (Wikipedia)en.wikipedia.org ↗
- Web pageBias-variance tradeoff (Wikipedia)en.wikipedia.org ↗
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