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AI Engineering
Course Roadmap · 5m — Full map from zero to AI engineer: what to learn and in what order.What is AI Engineering? · 11m — AI vs ML vs DL vs GenAI, and what an AI engineer actually does daily.How to Learn AI Without Math Fear · 8m — Beginner learning system: concepts, visuals, tiny code, repetition.What is AI, Really? · 10m — Narrow vs general AI, rules vs learning, where AI shines and fails.Machine Learning in Plain English · 12m — Data, features, training, prediction with a spam-filter story.Deep Learning Intuition · 10m — Neurons, layers, why depth helps — no calculus needed.Data, Training and Testing · 9m — Train/test split, overfitting, why models memorize vs generalize.Python Essentials for AI · 15m — Variables, functions, lists, dicts, files, pip, venv in one go.APIs, JSON and Pydantic · 12m — How AI apps talk: requests, JSON, schemas, validation.Async Python for AI Apps · 10m — Why streaming needs async, await in 10 minutes.What are LLMs? · 10m — Next-token prediction, cutoff dates, why they hallucinate.Tokenization · 8m — Why 'hello world' is not 2 things to a model. BPE in action.Transformer Architecture Simplified · 14m — Attention, embeddings, blocks — LEGO analogy.The LLM Training Pipeline · 12m — Pretrain → SFT → RLHF in plain English.Making Your First LLM API Call · 12m — OpenAI-compatible call in 20 lines. API keys safely.Understanding LLM Parameters · 9m — Temperature, top-p, max_tokens — with taste tests.Structured Output from LLMs · 10m — JSON mode, schemas, why apps need structure.What is Prompt Engineering? · 8m — Instruction, context, examples, output contract.Anatomy of an Effective Prompt · 10m — Role, task, constraints, format — template you can reuse.Advanced Prompting: CoT, Few-shot, ReAct · 12m — Chain-of-thought, self-consistency, ReAct loop.Defending Against Prompt Injection · 9m — System vs user, delimiters, least-privilege tools.What are Embeddings? · 10m — Words → vectors, similarity, why search becomes math.Vector Databases in 10 Minutes · 10m — HNSW, top-K, metadata filters without jargon.What is RAG? · 11m — Retrieval + generation. Freshness problem, vs fine-tune vs long-context.Chunking Strategies · 10m — Split docs so retrieval finds them: size, overlap, headings.Evals Basics: Faithfulness & Recall · 11m — 50-Q golden set, recall@k, abstain rate — catch regressions.Building a Production RAG Pipeline · 15m — Chunking, evals, citations, hybrid retrieval.From Text Generation to Action · 10m — Tool schemas, when to call, safety rails.What is MCP? · 8m — Model Context Protocol: USB-C for AI tools.What are AI Agents? · 11m — Plan → act → observe loop, memory, human-in-loop.Agent Memory Systems · 10m — Short chat + long vector/profile memory, checkpoints.Agent Reliability & Evals · 12m — Retries, checkpoints, evals that actually catch regressions.Vision Models & Image Understanding · 10m — CLIP, ViT intuition, captioning vs VQA.Diffusion Basics for Beginners · 11m — Denoising idea, prompts, negative prompts, control.Understanding AI App Costs · 9m — Tokens, $/1M, caching to cut 80% cost.Latency Optimization · 10m — Streaming, KV-cache, speculative decoding intuition.The AI Engineering Lifecycle · 10m — Prototype → eval → ship → monitor → iterate.Limits, Bias & Responsible AI · 10m — Hallucinations, bias, privacy, when NOT to use AI.AI Career Roadmap for Beginners · 12m — Portfolio projects, what recruiters look for, 90-day plan.
DSA (Beginner)
DSA Roadmap (Beginner) · 6m — 28-chapter path: warmup → Big-O → arrays → patterns → mock.How Coding Interviews Work · 8m — What interviewers score: clarify, brute, optimize, test.How to Pick a Pattern · 9m — Keywords that hint Two Pointers vs Sliding Window vs Hash.Big-O for Beginners · 10m — O(1), O(n), O(n log n) with stories, no proofs.Arrays & Strings Warmup · 12m — Reverse, max, palindrome — 5 must-dos.Recursion Intuition · 10m — Base + trust the leap. Factorial → Fibonacci.Two Pointers · 12m — Opposite ends + same direction with 4 classics.Sliding Window · 12m — Fixed vs dynamic window, longest substring without repeat.Hash Tables & Counting · 11m — Two Sum, anagrams, frequency maps.Prefix Sum · 10m — Range sums in O(1), subarray sum = k.Binary Search · 11m — Sorted search + answer space in plain words.Sorting Basics · 10m — When sort helps: intervals, Top-K, two pointers setup.Kadane: Max Subarray · 10m — Best ending here? Extend or restart — O(n).Stack & Queue · 10m — Valid parentheses, monotonic stack teaser.Linked List Basics · 11m — Reverse, cycle detect with fast/slow.Trees: BFS & DFS · 13m — Traversals + max depth + level order.Heaps & Top-K · 11m — K largest, merge K lists intuition.Intervals · 9m — Merge, insert — sort by start.Graphs: BFS/DFS · 12m — Islands, clone graph idea.DP for Beginners · 13m — Climb stairs → house robber, memo vs tab.Matrix BFS: Rotting Oranges · 11m — Multi-source BFS levels = minutes.Mock Interview Strategy · 10m — 15/30/45-min plan + STAR for code + checklist.
System Design (Beginner)
System Design Roadmap (Beginner) · 6m — 26-chapter path: foundations → data → scale → real questions.What is System Design? · 9m — From app to millions: tradeoffs, not perfect answers.Numbers Every Beginner Should Know · 8m — Latency numbers, 1M vs 1B, back-of-envelope.DNS, HTTP, TCP in 10 Minutes · 10m — What happens when you type a URL.Load Balancing Intro · 9m — Round-robin, least-conn, sticky — why we need it.API Design Basics · 10m — REST, idempotency, pagination, rate limits.SQL vs NoSQL · 10m — When rows win, when documents/keys win.Caching Basics · 10m — Cache-aside, TTL, CDN, stampede in one story.Replication & Sharding · 12m — Read replicas vs split writes, hot keys.CDN & Edge Caching · 9m — Hot assets at the edge, TTLs that save your DB.Queues & Pub/Sub · 10m — Decouple, retry, DLQ with food-delivery story.Kafka Intro (Beginner) · 11m — Topics, partitions, consumer groups — replayable log.Realtime: WebSockets & SSE · 9m — Chat, live scores — polling vs push.CAP & Consistency in Plain Words · 11m — Why you can't have all three during partitions.Auth Basics: JWT & OAuth · 10m — Sessions vs tokens, OAuth flow, where to validate.Failures, Retries & Rate Limits · 10m — Timeouts, backoff, circuit breaker story.Design TinyURL · 12m — Hash, DB, cache, 4-step framework.Design a Rate Limiter · 11m — Token bucket + Redis, per-user limits.
Tool Showdowns
pandas vs Polars vs FireDucks · 12m — DataFrames compared: compatibility, speed model, and which to learn first.Chroma vs Qdrant vs Pinecone · 11m — Vector DBs for RAG: local prototyping vs self-hosted vs fully managed.Choosing an LLM API · 10m — OpenAI vs Anthropic vs Gemini: how to pick a first provider and stay portable.LangChain vs LlamaIndex vs Raw SDK · 11m — Frameworks compared: agents and prototypes vs data-first RAG vs full control.pgvector vs Dedicated Vector DBs · 10m — Postgres extension vs Chroma/Qdrant/Pinecone: one stack or best of breed?