Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | RAG (Retrieval-Augmented Generation) | Machine Learning |
|---|---|---|
| Level | Advanced | Intermediate |
| Duration | 2 Months | 4–6 Months |
| Who can join | Open to any stream; no machine learning background needed. Python basics are taught from scratch | Basic computer skills and school-level maths; no prior programming experience required |
| Key tools | Python, Jupyter, VS Code, Git and GitHub, OpenAI, Anthropic Claude | Python, Jupyter Notebook, Google Colab, NumPy, Pandas, SQL |
| What you’ll learn | Module 1: Foundations of LLMs and RAG · Module 2: Python for AI Applications · Module 3: Document Processing and Chunking · Module 4: Embeddings and Vector Databases | Module 1: Python for Machine Learning · Module 2: Maths and Statistics Essentials · Module 3: Data Handling and Visualisation · Module 4: Supervised Learning |
| Portfolio projects | — | — |
| Career roles | RAG developer, LLM application developer, AI engineer | Machine Learning Engineer (junior), Data Analyst, Junior Data Scientist |
Build AI assistants that answer accurately from your own documents, websites and databases.
Start with Python, statistics and data handling, then move on to supervised and unsupervised learning, model evaluation and deploying simple models into real applications.
A counsellor can map either course to your degree, schedule and career goal — one call is usually enough.
Book a Free Demo