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6-month learning plan

The learning path

12 modules, 130 sessions, block-based within a module, spiralling across. Each module unlocks the ones below. Start at the top and don't skip.

M00

🧰 Setup & Tools

4 sessions · Setup
  1. 001Dev Environment — Linux/WSL, Terminal, VS Code
  2. 002Git & GitHub — Commits, Branches, PRs
  3. 003The Command Line — bash, pipes, grep, jq
  4. 004Reading Docs & Effective Googling — the Meta-Skill
unlocksM01M04M06M07
M01

🐍 Python Foundations

10 sessions · SW
  1. 005Python Variables & Types — Mental Model of Memory
  2. 006Control Flow — if/else, loops, comprehensions
  3. 007Functions — arguments, scope, closures
  4. 008Data Structures — list, tuple, dict, set (when to use what)
  5. 009Classes & Objects — the OOP Mental Model
  6. 010Inheritance, Composition & Polymorphism
  7. 011Errors, Exceptions & Debugging with pdb
  8. 012Modules, Packages, Virtualenvs, pip & uv
  9. 013Testing with pytest — TDD Workflow
  10. 014Type Hints, mypy, dataclasses & pydantic
unlocksM03M02M04
M02

📐 Math Foundations

8 sessions · Math
  1. 015Big-O Notation — Reasoning About Scale
  2. 016Discrete Math — Sets, Logic, Combinatorics, Graphs
  3. 017Linear Algebra I — Vectors, Dot Product, Geometry
  4. 018Linear Algebra II — Matrices, Transforms, Eigenvalues
  5. 019Calculus I — Derivatives & Chain Rule
  6. 020Calculus II — Gradients & Gradient Descent from Scratch
  7. 021Probability — Random Variables, Distributions, Expectation
  8. 022Statistics — CLT, Hypothesis Testing, Confidence Intervals
unlocksM03M08
M03

🧠 Data Structures & Algos

12 sessions · SW
  1. 023Arrays & Strings — Indexing, Slicing, Two-Pointer
  2. 024Hashmaps & Sets — Hash Functions, Collisions
  3. 025Linked Lists — Singly, Doubly, When They Win
  4. 026Stacks & Queues — LIFO/FIFO in Practice
  5. 027Recursion — Call Stack, Base Case, Worked Examples
  6. 028Trees & BSTs — Traversal (BFS/DFS)
  7. 029Heaps & Priority Queues
  8. 030Graphs — Representation, BFS, DFS, Shortest Path
  9. 031Sorting — Merge, Quick, and When to Trust the Built-in
  10. 032Binary Search — the Pattern Behind 100 Problems
  11. 033Dynamic Programming — Memoisation & Tabulation
  12. 034Greedy & Backtracking — When to Use Each
unlocksM04M05M06
M04

🗄️ Databases & SQL

10 sessions · DE
  1. 035The Relational Model — Tables, Keys, Normalisation
  2. 036SQL Basics — SELECT, WHERE, ORDER BY, LIMIT
  3. 037Joins — INNER, LEFT, RIGHT, FULL, Anti-Join
  4. 038Aggregations — GROUP BY, HAVING, Subqueries
  5. 039Window Functions — the Game-Changer
  6. 040CTEs & Recursive Queries
  7. 041Indexes — B-Tree Intuition, When to Add
  8. 042Transactions & ACID — Isolation Levels, MVCC
  9. 043Query Planning — EXPLAIN, Execution Plans, Tuning
  10. 044NoSQL Landscape — KV, Document, Column, Graph
unlocksM05
M05

🔧 Data Engineering

10 sessions · DE
  1. 045Data Modelling — Dimensional, Data Vault, OBT
  2. 046Batch vs Streaming — Mental Model & Use Cases
  3. 047Spark — RDD, DataFrame, Jobs/Stages/Shuffles
  4. 048Kafka — Topics, Partitions, Consumer Groups
  5. 049Stream Processing — Watermarks, Windows, Exactly-Once
  6. 050Orchestration — Airflow, DAGs, Retries, Backfills
  7. 051dbt — Models, Tests, Docs, Warehouse-Native ELT
  8. 052Lakehouse — Delta / Iceberg / Hudi, ACID on Files
  9. 053Data Quality — Freshness, Volume, Schema, Distribution
  10. 054Governance & Cost — Lineage, PII, Attribution
unlocksM11
M06

🌐 Backend & APIs

5 sessions · SYS
  1. 055HTTP Fundamentals — Verbs, Status Codes, Headers, Caching
  2. 056REST API Design — Resources, Versioning, Idempotency
  3. 057GraphQL — Schema, Resolvers, N+1, When to Pick It
  4. 058gRPC & Protobuf — When RPC Wins
  5. 059AuthN & AuthZ — OAuth 2.0, OIDC, JWT
unlocksM07
M07

🏗️ Systems & Infrastructure

10 sessions · SYS
  1. 060OS Basics — Processes, Threads, Memory, FDs
  2. 061Networking I — TCP/IP, DNS, Sockets
  3. 062Networking II — Load Balancers L4 vs L7, Reverse Proxies
  4. 063Caching — Cache-Aside, Write-Through, TTLs, Invalidation
  5. 064CDN — Edge, Cache Hierarchies, Cache-Control
  6. 065Docker — Images, Layers, Dockerfile, Networking
  7. 066Kubernetes I — Pods, Deployments, Services
  8. 067Kubernetes II — ConfigMaps, Secrets, HPA, Network Policies
  9. 068Azure Cloud — Identity, Storage, Networking, App Service
  10. 069Infrastructure as Code — Terraform / Bicep Basics
unlocksM10
M08

🤖 Machine Learning

7 sessions · ML
  1. 070CAP & PACELC — the Actual Trade-Offs
  2. 071Replication — Leader/Follower, Multi-Leader, Leaderless
  3. 072Consistency Models — Linearizable, Sequential, Eventual
  4. 073Consensus — Paxos & Raft Intuition
  5. 074Sharding & Partitioning Strategies
  6. 075Message Queues — SQS, RabbitMQ, Kafka as Queue
  7. 076Multi-Region — Active-Passive, Active-Active, Failover
unlocksM09M10
M09

🧠 LLMs & Modern AI

4 sessions · LLM
  1. 077The 3 Pillars — Metrics, Logs, Traces
  2. 078Prometheus, Grafana, OpenTelemetry — Hands-on
  3. 079SLIs, SLOs & Error Budgets — the SRE Math
  4. 080Incident Response — Runbooks, Postmortems, On-Call
unlocksM11
M10

🚀 MLOps & Production

3 sessions · MLOps
  1. 081AuthN vs AuthZ, Sessions & Password Storage
  2. 082TLS 1.3, PKI & Cert Lifecycle
  3. 083OWASP Top 10, Secrets Mgmt & Threat Modelling
unlocksM11
M11

📐 System Design & Projects

12 sessions · Proj
  1. 084The ML Mental Model — Features, Labels, Train/Val/Test
  2. 085Linear Regression from Scratch (numpy)
  3. 086Logistic Regression — Sigmoid, Cross-Entropy, from Scratch
  4. 087Regularization — L1, L2, Elastic Net
  5. 088Bias–Variance Trade-off & Learning Curves
  6. 089Decision Trees — Gini, Entropy, Splits
  7. 090Random Forest & Bagging
  8. 091Gradient Boosting — XGBoost, LightGBM
  9. 092Evaluation Metrics — P/R/F1/ROC/PR/AUC
  10. 093Feature Engineering — Encoding, Scaling, Missing
  11. 094Imbalanced Data — SMOTE, Class Weights, Thresholds
  12. 095Model Selection — CV, Hyperparameter Tuning, Optuna
M12

🎯 Projects & Capstone

10 sessions · Proj
  1. 096Perceptron & Activation Functions
  2. 097Multi-Layer Perceptron — Forward Pass
  3. 098Backpropagation — Derived by Hand on a 2-Layer Net
  4. 099Optimizers — SGD, Momentum, Adam, RMSprop
  5. 100PyTorch Fundamentals — Tensors, Autograd, nn.Module
  6. 101Regularization in DL — Dropout, BatchNorm, Weight Decay
  7. 102CNNs — Convolution, Pooling, ImageNet Architectures
  8. 103RNNs & LSTMs — Sequences & the Vanishing Gradient
  9. 104Embeddings — word2vec, GloVe, Contrastive Learning
  10. 105Transfer Learning & Fine-Tuning Classical DL

How to use this

  • · Read modules top-to-bottom; inside a module, go session-by-session.
  • · Prereqs are enforced: don't jump ahead until the earlier module is comfortable.
  • · Sessions ~15 minutes of reading each; hands-on work adds 15–45 minutes.
  • · When a session includes a diagram, sit with it before scrolling past it.