AI Engineering Foundations Bootcamp
The first course in the AI Engineer track. You build the three things every AI engineer needs before generative or agentic systems: Python for AI software, relational databases and SQL with PostgreSQL, and a working understanding of tokens, embeddings, transformers and LLM APIs. You finish by shipping a small Python, PostgreSQL and LLM app.
- Duration
- 4 weeks · 40 hours
- Format
- Live online + self-study
- First class
- Free, no obligation
- Level
- Beginner
- Schedule
- Evenings, US time zones
- Lectures
- 12 (notes · lab · quiz)
- Certificate
- Certificate of Completion
- Tuition
- $400 per student, batch of 3 to 5 · payment plans
What you'll master.
Python for AI Engineers
- Functions, data structures and OOP
- Virtual environments, JSON and REST APIs
- Secrets, Git and GitHub
Databases & SQL Fundamentals
- Tables, keys and relationships
- SELECT, GROUP BY, joins and subqueries
- Data types, constraints, DDL and DML
Python + PostgreSQL Integration
- psycopg2 and SQLAlchemy
- Parameterized queries (no SQL injection)
- A CRUD API with FastAPI
Generative AI Fundamentals
- Tokens and tokenization
- Embeddings and transformers, conceptually
- Foundation models and hallucination
Working with LLM APIs
- Messages, roles and parameters
- Prompting essentials and JSON output
- Token limits, cost and streaming
Embeddings & Vector Search (pgvector)
- Generating embeddings
- Cosine similarity and semantic search
- SQL filters plus vector similarity
The complete 12-lecture path.
Every lecture includes a live class, self-study notes, a hands-on coding lab and a 10-question quiz.
01Python for AI Engineers
- Functions, control flow and data structures (Module 1)
- Classes, modules and packages (Module 1)
- Reading and writing files
- Debugging with tracebacks
LAB Write a small Python package that parses a text dataset
02Python Tooling, APIs & Git
- Virtual environments with venv and pip/uv (Module 1)
- JSON and REST APIs with requests (Module 1)
- Environment variables and secrets (Module 1)
- Git and GitHub basics (Module 1)
LAB Call a public REST API and commit the project to Git
03Relational Databases & PostgreSQL
- Tables, keys and relationships (Module 2)
- Installing and configuring PostgreSQL (Module 2)
- SELECT, WHERE and ORDER BY (Module 2)
- Using psql
LAB Install PostgreSQL and query a sample database
04Aggregates, Joins & Subqueries
- GROUP BY and HAVING (Module 2)
- INNER, LEFT, RIGHT and FULL joins (Module 2)
- Subqueries (Module 2)
- Reading a query result critically
LAB Write 15 SQL queries against a sample database
05Schema Design, DDL & DML
- Data types and constraints (Module 2)
- Designing a schema (Module 2)
- CREATE and ALTER TABLE (Module 2)
- INSERT, UPDATE and DELETE (Module 2)
LAB Design and build the schema for a small app
06Python + PostgreSQL & FastAPI
- Connecting with psycopg2 and SQLAlchemy (Module 3)
- Parameterized queries and SQL injection (Module 3)
- A CRUD API with FastAPI (Module 3)
- Users, records and logs tables (Module 3)
LAB Build a CRUD REST API in FastAPI backed by PostgreSQL
07What Makes AI Generative
- Generative vs. predictive AI (Module 4)
- Tokens and tokenization (Module 4)
- Embeddings: the intuition (Module 4)
- Foundation models, open source vs. proprietary (Module 4)
LAB Tokenize sample text and compare token counts
08Transformers & Why LLMs Hallucinate
- The transformer at a conceptual level (Module 4)
- Self-attention, introduced (Module 4)
- How an LLM picks the next token
- Why LLMs hallucinate (Module 4)
LAB Trace how a prompt becomes tokens and a response
09Working with LLM APIs
- Messages, roles and parameters (Module 5)
- System vs. user prompts (Module 5)
- Clarity, context and few-shot examples (Module 5)
- JSON mode, token limits, cost and streaming (Module 5)
LAB Summarize and classify sample text with an LLM API
10Embeddings & pgvector
- Generating embeddings with an API (Module 6)
- Cosine similarity and semantic search (Module 6)
- Installing the pgvector extension (Module 6)
- Storing embeddings in a column (Module 6)
LAB Embed a text collection and store it in pgvector
11Semantic & Basic Hybrid Search
- Running a similarity-search query (Module 6)
- Combining WHERE filters with vector similarity (Module 6)
- Choosing how many results to return
- Planning the capstone
LAB Build a search query that mixes SQL filters and vector similarity
12Capstone: Smart Notes
- Notes schema with an embedding column (Capstone)
- Search by meaning (Capstone)
- LLM summary of the top matches (Capstone)
- Demo and code review
LAB Ship and demo the Smart Notes app
Three courses, one path.
Each course builds on the one before it, so you can take them back to back as one program.
- Course 1AI Engineering FoundationsBeginner · 4 weeks · 40 hours
- Course 2Generative AI EngineeringIntermediate (after AIE-1) · 8 weeks · 56 hours
- Course 3Agentic AI EngineeringAdvanced (after AIE-2) · 12 weeks · 60 hours
Who it is for
Aspiring AI engineers, developers moving into AI, computer science students and career switchers with some programming
Prerequisites
Basic programming logic in any language. No AI or database experience needed.
Tools you will use
What you will ship
Smart Notes: a Python and PostgreSQL (pgvector) backend that stores notes, embeds each one, lets you search them by meaning and returns an LLM summary of the top matches.
Assessment
Graded SQL and Python labs (50%), capstone demo and code review (50%)
Certificate
Certificate of Completion: AI Engineering Foundations, issued by Netwisers when you pass.
By the end you will
- Write Python programs with functions, OOP, virtual environments and REST API calls
- Design relational schemas and write joins, aggregations and subqueries in PostgreSQL
- Call an LLM API from Python and get structured output back
- Store embeddings in PostgreSQL with pgvector and run a semantic search
Roles this prepares you for
Includes resume review, LinkedIn optimization and mock interviews focused on the US job market.
Ready to start AI Engineering Foundations?
Book a free consultation. We will confirm the next start date in your time zone and walk you through tuition and payment plans.