Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Agentic AI | Machine Learning |
|---|---|---|
| Level | Intermediate | Intermediate |
| Duration | 4 Months | 4–6 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; 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 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 for Machine Learning · Module 2: Maths and Statistics Essentials · Module 3: Data Handling and Visualisation · Module 4: Supervised Learning |
| Portfolio projects | — | — |
| Career roles | AI agent developer, AI automation engineer, LLM application developer | Machine Learning Engineer (junior), Data Analyst, Junior Data Scientist |
Design and deploy AI agents that plan, use tools and complete multi-step tasks on their own.
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.
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