Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Artificial Intelligence | Deep Learning |
|---|---|---|
| Level | Intermediate | Advanced |
| Duration | 6–9 Months | 3–4 Months |
| Who can join | No prior coding experience; basic school-level maths (percentages, simple algebra) | Basic computer skills and school-level maths; Python or machine learning knowledge is useful but not compulsory |
| Key tools | Python, Jupyter Notebook, Google Colab, NumPy, pandas, Matplotlib | Python, Jupyter Notebook, Google Colab, NumPy, Pandas, Matplotlib |
| 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 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/ML trainee, Data analyst, Junior data scientist | Deep Learning Engineer, Machine Learning Engineer, Computer Vision Engineer |
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.
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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