Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Artificial Intelligence | Machine Learning |
|---|---|---|
| Level | Intermediate | Intermediate |
| Duration | 6–9 Months | 4–6 Months |
| Who can join | No prior coding experience; basic school-level maths (percentages, simple algebra) | Basic computer skills and school-level maths; no prior programming experience required |
| Key tools | Python, Jupyter Notebook, Google Colab, NumPy, pandas, Matplotlib | Python, Jupyter Notebook, Google Colab, NumPy, Pandas, SQL |
| What you’ll learn | Module 1: Python for AI · Module 2: Data Handling and Maths Essentials · Module 3: Data Analysis and Visualisation · Module 4: Machine Learning | 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/ML trainee, Data analyst, Junior data scientist | Machine Learning Engineer (junior), Data Analyst, Junior Data Scientist |
Begin with Python and the essential maths, then move into machine learning, deep learning, natural language processing and generative AI, applying each concept through hands-on projects.
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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