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) | Deep Learning |
|---|---|---|
| Level | Advanced | Advanced |
| Duration | 2 Months | 3–4 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; Python or machine learning knowledge is useful but not compulsory |
| Key tools | Python, Jupyter, VS Code, Git and GitHub, OpenAI, Anthropic Claude | Python, Jupyter Notebook, Google Colab, NumPy, Pandas, Matplotlib |
| 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 and Maths for Deep Learning · Module 2: Machine Learning Foundations · Module 3: Neural Network Fundamentals · Module 4: Building and Tuning Models with TensorFlow, Keras and PyTorch |
| Portfolio projects | — | — |
| Career roles | RAG developer, LLM application developer, AI engineer | Deep Learning Engineer, Machine Learning Engineer, Computer Vision Engineer |
Build AI assistants that answer accurately from your own documents, websites and databases.
Learn how artificial neural networks, CNNs, RNNs, LSTMs and transformers learn from data, working in Python with TensorFlow, Keras and PyTorch on real datasets.
A counsellor can map either course to your degree, schedule and career goal — one call is usually enough.
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