Post-Course Growth
Reference only — these topics are NOT part of this course. Explore them AFTER you've built real things and discovered you need them.
INFO
- Time: Self-paced
- Difficulty: Varies
- Prerequisites: Core Modules + Flagship Project + Real project experience
This Page Covers
- Topics to explore after completing this course
- When to learn each topic
- Resources for further learning
Important: Don't Learn These Yet
These topics are here for reference when you need them later. Don't try to learn them until:
- You've completed the core modules
- You've shipped your flagship project
- You've built something real and discovered you need these skills
The best way to learn is to have a real problem to solve.
Python Basics
When to Learn This
- When you want to work with AI/ML tools (most are Python-first)
- When you need to process data, work with spreadsheets, or automate tasks
- When JavaScript isn't the right tool — Python is often simpler for scripting
What You'd Cover
- Why Python: Dominant in AI/ML, data science, and automation. Simple syntax.
- Setup: Installing Python, virtual environments (
venv), package manager (pip) - Basics: Variables, functions, lists, dictionaries, loops
- Common tasks: Making API calls with
requests, working with JSON, reading files
Resources for Later
- Automate the Boring Stuff with Python — Free online book, practical projects
- Python.org Tutorial — Official tutorial
- Real Python — High-quality tutorials and guides
Docker Basics
When to Learn This
- When you need to run applications that require specific environments
- When working with teams and need consistent setups ("works on my machine" problems)
- When deploying backend applications or databases locally
What You'd Cover
- Problem it solves: Packages applications with their dependencies into containers
- Core concepts: Images (blueprints), containers (running instances), Docker Hub (image registry)
- Basic commands:
docker pull,docker run,docker ps,docker stop - Docker Compose: Running multiple containers together (app + database)
Resources for Later
- Docker Getting Started Guide — Official hands-on tutorial
- Docker in 100 Seconds — Quick conceptual overview
Databases Introduction
When to Learn This
- When your app needs to store and retrieve data persistently
- When spreadsheets become unwieldy or you need multiple users accessing data
- When building any app with user accounts, content, or records
What You'd Cover
- Why databases: Persistent storage, structured queries, multi-user access, data integrity
- SQL vs NoSQL: SQL for structured data and relationships, NoSQL for flexible schemas and scaling
- Popular options:
- PostgreSQL: Powerful, open-source SQL database
- SQLite: Simple file-based database, great for small projects
- Supabase: PostgreSQL with a nice UI and built-in auth
- Firebase: Google's NoSQL database, good for real-time apps
- Basic SQL:
SELECT,INSERT,UPDATE,DELETE,JOIN
Resources for Later
- SQLBolt — Interactive SQL tutorial
- Supabase Documentation — Great for getting started with PostgreSQL
- Prisma — Modern database toolkit for TypeScript/JavaScript
GraphQL
When to Learn This
- When working with APIs that use GraphQL (many modern APIs do)
- When you need to fetch complex, nested data efficiently in one request
- When REST feels limiting (over-fetching or under-fetching data)
What You'd Cover
- How it differs from REST: Request exactly the data you need in one query
- Queries: Fetching data with specific fields
- Mutations: Creating, updating, deleting data
- When to use it: Complex data relationships, mobile apps, when bandwidth matters
- When REST is fine: Simple APIs, CRUD operations, when GraphQL adds unnecessary complexity
Resources for Later
- How to GraphQL — Free tutorial site
- Apollo GraphQL Docs — Popular GraphQL client
Advanced AI Topics
When to Learn This
- When you've built with AI and want to customize its knowledge or behavior
- When you need AI to use your specific documents or data
- When you want to run AI locally for privacy or cost reasons
Topics to Explore
RAG (Retrieval Augmented Generation)
Make AI answer questions using your own documents. The AI retrieves relevant content from your data before generating a response.
When useful: Chatbots that answer questions about your docs, knowledge bases, customer support
Key concepts: Embeddings (turning text into numbers), vector databases, retrieval pipelines
Fine-tuning
Training a model on your specific data to change its behavior or knowledge. More expensive and complex than RAG.
When useful: When you need consistent style/tone, specialized domain knowledge baked in, or reduced prompt length
Note: Often overkill — try RAG and good prompting first
Vector Databases
Specialized databases for storing and searching embeddings (numerical representations of text). Essential for RAG and semantic search.
Popular options: Pinecone, Weaviate, Chroma (local), pgvector (PostgreSQL extension)
AI Agents
Systems where AI can take actions, use tools, and make decisions autonomously. More complex than simple chat.
When useful: Complex workflows, tasks requiring multiple steps, automation
Frameworks: LangChain, AutoGen, Claude's tool use
Local Models
Running AI models on your own computer instead of using cloud APIs. Better privacy, no per-token costs, but requires decent hardware.
Tools: Ollama (easiest), LM Studio, llama.cpp
Trade-offs: Smaller models than cloud options, requires GPU for good performance
Key Takeaways
- Don't learn these topics until you have a real need for them
- Focus on building real things first
- These skills become easier once you have project context
- Return to this page when you're ready to level up