NumPy

Lessons

1. 1.1Why NumPySay what an array holds that a list does not, and why that makes it faster and smaller.
2. 1.2Shape, dtype and ReshapingRead any array's shape and dtype, and reshape without copying.
3. 1.3Indexing, Slicing and MasksSelect exactly the elements you want, including by condition.
4. 1.4Vectorised Operations and BroadcastingCompute on whole arrays at once, and predict when shapes combine.
5. 1.5Aggregations Along AxesSum, average and find extremes over rows or columns.
6. 1.6Linear Algebra in NumPyWrite the maths of the ML videos directly as code.
7. 1.7Random Numbers and ReproducibilityGenerate random data you can reproduce.

Module material

Take these after the last lesson, in this order. Next on each page leads through them.

1. Module quiz15 questions across the whole module. Take it after the last lesson; your first pick on each question is the one that counts.
2. Project: The chain's year, in NumPyStated as a problem, with no template and no step-by-step instructions. Working out the steps is the point.
3. NotebooksOpen them in Google Colab. Each answer is checked as you go: correct, wrong with the expected value, or not answered yet.
4. Common mistakesWhat you see when it goes wrong, why it happens, and the fix, across the whole module.
5. Cheat sheetOne page of the module’s terms, rules and gotchas, with the output each rule produces.
6. Interview questions22 questions interviewers ask on numpy, from first principles to the follow-ups. The first 3 model answers are free to read; Pro opens the rest.

Glossary

Every term the module introduces, with the lesson that introduces it.