Skip to content
GKgkml.dev

Learn 📚

Practice only measures what you already know. These tracks teach the material first — the model behind the commands, the trade-off behind the service, the reasoning behind the algorithm — and then hand off to the 1,500 practice questions that test it.

5 tracks37 lessons~6 hours of reading

DSA in Python

10 lessons · ~95 min

Most people learn data structures as a catalogue — here is a stack, here is a heap — and then cannot tell which one a problem wants. That is the wrong order. A data structure is a set of trade-offs, and you pick one by knowing which operation has to be fast.

  1. 01Complexity: how to reason about cost
  2. 02Python's containers and what they really cost
  3. 03Arrays, two pointers and sliding windows
  4. 04Hashing: trading memory for time
  5. 05Recursion and backtracking
  6. 06Sorting and binary search
  7. 07Linked lists, stacks and queues
  8. 08Trees, BSTs and heaps
  9. 09Graphs: traversal, ordering and connectivity
  10. 10Dynamic programming without the fear
Start the track →Practice instead

AWS Cloud

7 lessons · ~65 min

AWS has hundreds of services and almost nobody needs most of them. What you actually need is a mental map: which service solves which class of problem, and what the trade-off is against the obvious alternative.

  1. 01How AWS is laid out, and who is responsible for what
  2. 02IAM: identity, policy and the evaluation order
  3. 03Compute: EC2, containers and serverless
  4. 04Storage: object, block and file
  5. 05Networking: VPCs, subnets and reaching things privately
  6. 06Databases and decoupling
  7. 07Cost, observability and the Well-Architected pillars
Start the track →Practice instead

AWS SAA-C03

6 lessons · ~56 min

SAA-C03 does not test whether you can recall what a service does. It tests whether you can pick the right one when four plausible options are on screen and the requirement is buried in a sentence about compliance or cost.

  1. 01What the exam actually asks
  2. 02Domain 1 — Design secure architectures
  3. 03Domain 2 — Design resilient architectures
  4. 04Domain 3 — Design high-performing architectures
  5. 05Domain 4 — Design cost-optimized architectures
  6. 06Reading a scenario question
Start the track →Practice instead

Git & GitHub

7 lessons · ~60 min

Git is usually taught as a list of commands, which is why so many people can commit and push but freeze the moment something goes wrong. The commands are inconsistent and badly named; the underlying model is small and completely regular.

  1. 01The object model: what Git actually stores
  2. 02The three trees: working directory, index, HEAD
  3. 03Branching, merging and resolving conflicts
  4. 04Remotes: fetch, pull, push and tracking
  5. 05Rewriting history: amend, rebase and interactive rebase
  6. 06Recovery and investigation
  7. 07Collaborating: workflows, pull requests and GitHub
Start the track →Practice instead

MLOps

7 lessons · ~66 min

A model that scores well offline is roughly a quarter of the work. The rest is getting it deployed reliably, knowing when it stops working, and being able to explain and reproduce any prediction it made six months ago.

  1. 01Why ML systems fail differently
  2. 02Data pipelines, versioning and feature stores
  3. 03Experiment tracking and reproducibility
  4. 04Evaluating models honestly
  5. 05Deployment and serving patterns
  6. 06Monitoring, drift and retraining
  7. 07Governance, cost and responsible deployment
Start the track →Practice instead