AI / ML Path
From Python to neural networks
Start with the maths and Python you need, then build classical ML models, before stepping into deep learning and modern AI.
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Python for ML
NumPy, pandas, and a quick refresher on the Python you actually need.
Maths Foundations
Linear algebra, calculus, and probability - just enough, no more.
Data Handling
Loading, cleaning, and exploring real datasets.
Supervised Learning
Regression and classification with scikit-learn.
Model Evaluation
Train/test splits, cross-validation, bias-variance, and metrics.
Unsupervised Learning
Clustering, dimensionality reduction, and PCA.
Neural Networks
Perceptrons, backprop, and your first deep model.
Deep Learning Frameworks
PyTorch fundamentals and a real CNN.
NLP & LLMs
Text representations, transformers, and using LLM APIs.
Deploy an ML App
Packaging a model and shipping a small web demo.