learning seriesintermediate 8m2026-07-11

The 36-Topic Learning Series

36 topics · 54 sessions · 18 weeks · a foundations-first, jumbled-track deep-dive on engineering, ML, and LLM systems.

What this is

An 18-week, 54-session learning program covering the 36 topics an experienced engineer needs to move confidently between data engineering, ML, and LLM systems. Foundations first (SOLID, SQL, DSA, Stats, Spark, Python, classical ML) — then advanced stack (Transformers, RAG, agents, fine-tuning, multimodal).

Every 2 new topics are followed by a revision session — recall + drill + hands-on redo.

Cadence

Tue 20:30–22:00 IST (new topic)
Thu 20:30–22:00 IST (new topic)
Sat 09:30–11:00 IST (revision)

Each session: 90 minutes deep-dive · ~30 min pre-read (YouTube + one doc) · ~15 min post-checklist. Weekly load: ~4.5 hrs live + ~2 hrs async.

The 36 topics (jumbled tracks — no 3-in-a-row same track)

#	Topic	Track
01	SOLID Principles & Design Patterns	SW 🐍
02	Advanced SQL — Windows, CTEs, Exec Plans	DE 🗄️
03	DSA & Coding Patterns — Arrays, Trees, Graphs, DP	SW 🧩
04	Statistics & A/B Testing	ML 📊
05	Spark Execution Model — Jobs, Stages, Shuffles	DE 🔥
06	Classical ML Fundamentals — Linear, Trees, Metrics	ML 🌱
07	Python Concurrency — Threads, Asyncio, GIL, GC	SW 🐍
08	Postgres Internals — MVCC, Isolation, Replication	DE 🐘
09	Gradient Boosting — Intuition, XGBoost, LightGBM	ML 🌳
10	API Design — REST, GraphQL, gRPC	SYS 🔌
11	Data Modelling — Dimensional, Vault, OBT	DE 🧮
12	Deep Learning Fundamentals — Backprop, CNNs, Optimizers	ML 🧠
13	Containers & Kubernetes	SYS ⚓
14	Kafka — Brokers, Partitions, Replication, Consumers	DE 📮
15	Embeddings & Vector Spaces — Contrastive, Similarity	ML 📐
16	Distributed Systems Core — CAP/PACELC, Sharding, Multi-Region	SYS 🌐
17	Lakehouse — Delta, Iceberg, Hudi	DE 🏞️
18	Transformers Foundations — Attention, Q/K/V, Multi-Head	LLM 🧠
19	Observability & SRE — Metrics, Traces, SLOs	SYS 📈
20	Streaming — Watermarks, Windows, Exactly-Once	DE 🌊
21	MLOps — Tracking, Registry, CI/CD, Feature Stores	ML 🛠️
22	RAG End-to-End — Chunking, Embeddings, Retrieval, Rerank, Eval	LLM 🔎
23	Caching & CDN — Cache-Aside, TTLs, Invalidation	SYS ⚡
24	Vector DBs at Scale — pgvector, Milvus, HNSW/IVF	DE 🧭
25	Recommender Systems — Two-Tower, Ranking, Online	ML 🎯
26	LLM Evaluation — Judges, RAGAS, Golden Sets	LLM ⚖️
27	URL Shortener System Design	SYS 🏗️
28	Azure Cloud Deep — Identity, Storage, Networking	SYS ☁️
29	LLM Agents — Function Calling, Tool Use, Orchestration	LLM 🤖
30	Security — AuthN/Z, TLS, OWASP, Secrets	SYS 🔐
31	Chat System Design	SYS 💬
32	LLM Serving — KV Cache, Batching, Speculative Decoding	LLM 🚀
33	Data Platform Cost & Governance	DE 💰
34	Fine-tuning LLMs — LoRA, QLoRA, PEFT	LLM 🎛️
35	Multimodal LLMs — CLIP, VLMs, Video, Audio	LLM 🎨
36	Production AI Agent — Capstone	LLM 🏁

The 18 revisions

R	Covers	Slug
R01	S01+S02	Revision 01
R02	S03+S04	Revision 02
R03	S05+S06	Revision 03
R04	S07+S08	Revision 04
R05	S09+S10	Revision 05
R06	S11+S12	Revision 06
R07	S13+S14	Revision 07
R08	S15+S16	Revision 08
R09	S17+S18	Revision 09
R10	S19+S20	Revision 10
R11	S21+S22	Revision 11
R12	S23+S24	Revision 12
R13	S25+S26	Revision 13
R14	S27+S28	Revision 14
R15	S29+S30	Revision 15
R16	S31+S32	Revision 16
R17	S33+S34	Revision 17
R18	S35+S36	Revision 18

Why this order

Foundations before advanced. SOLID, Advanced SQL, DSA, Stats, Classical ML, Deep Learning basics come before Transformers (S18) and the LLM stack.
Tracks jumbled. No 3 consecutive sessions on the same track — keeps your brain fresh and lets ideas cross-pollinate.
Systems woven in. API Design, Distributed Systems, Kubernetes, Observability, Security, and Azure sit between the ML/DE topics — because in real production, they never live apart.
Capstone at the end. The last 6 sessions (LLM Serving through Multimodal → Production AI Agent) bring everything together.

Start

Series begins Tue 14 Jul 2026 · 20:30 IST with Session 01 (SOLID & Design Patterns).

Bring a notebook, a laptop, and a clear 90-min block. See you at S01.

← previous

Production AI Agent — Capstone