Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Agentic AI | Deep Learning |
|---|---|---|
| Level | Intermediate | Advanced |
| Duration | 4 Months | 3–4 Months |
| Who can join | Open to any stream; no computer science degree 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 AI, LLMs and Agentic AI · Module 2: Python for AI Agents · Module 3: Prompt and Context Engineering · Module 4: Tool Calling and Function Execution | 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 | AI agent developer, AI automation engineer, LLM application developer | Deep Learning Engineer, Machine Learning Engineer, Computer Vision Engineer |
Design and deploy AI agents that plan, use tools and complete multi-step tasks on their own.
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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