Here is a plan to learn ML fundamentals in an afternoon by watching some videos on youtube:

## Follow this plan

Machine learning fundamentals:

- What is Bias and Variance? - 6 minutes
- What is a Tensor? - 12 minutes

[Stop and drink coffee, eat a snack]

How to address bias and variance:

- Regularization (e.g. L2/Ridge) - 20 minutes
- Bagging (e.g. Random forrest) - 9 minutes
- Boosting (e.g. AdaBoost) - 20 minutes
- L1 vs L2 regularization - 9 minutes

Extra material:

- Bootstrapping - 9 minutes

## Test your knowledge

- What is bias?
- A: Bla
- B: The inability of a machine learning model (e.g. linear regression) to express the true relationship between X and Y
- C: Bla

- What is variance?
- A: The difference in how well a model fits different datasets (e.g. training and test)
- B: Bla
- C: Bla

- What problem does regularization, bagging and boosting address?
- A: Bla
- B: Bla
- C: Finds the sweet spot between simple and complicated models

- What is regularization?
- A: Bla
- B: Bla
- C: Bla

- What is bagging?
- A: Bla
- B: Bla
- C: Bla

- What is boosting?
- A: Bla
- B: Bla
- C: Bla
- What is bootstrapping?
- A: Repeat an experiment a bunch of times until we feel certain about the result
- B: Repeatly random sample n times (with replacement) from a set of n observations and build up a histogram of any statistic, e.g. the mean.
- C: Augment a small set of observations with synthetic samples to increase sample size