Amazon ML Summer School

Nine modules · 29 lectures · self-study notes, worked from the verified slide capture. Everything is saved in this browser only.

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Begin with Supervised Learning — every later module leans on its bias–variance decomposition and its metrics.
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Pacing

The whole course is ≈ — of first-pass reading at 130 wpm, roughly — including second pass and interview prep.

Reading guide. First pass — read a lecture linearly, skip the interactive blocks. Second pass — work every 🧪 example on paper before reading the solution. Before an interview — the Putting it together threads, the whiteboard derivations, the depth-probe tables.