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.

1

Python for ML

NumPy, pandas, and a quick refresher on the Python you actually need.

2

Maths Foundations

Linear algebra, calculus, and probability - just enough, no more.

3

Data Handling

Loading, cleaning, and exploring real datasets.

4

Supervised Learning

Regression and classification with scikit-learn.

5

Model Evaluation

Train/test splits, cross-validation, bias-variance, and metrics.

6

Unsupervised Learning

Clustering, dimensionality reduction, and PCA.

7

Neural Networks

Perceptrons, backprop, and your first deep model.

8

Deep Learning Frameworks

PyTorch fundamentals and a real CNN.

9

NLP & LLMs

Text representations, transformers, and using LLM APIs.

10

Deploy an ML App

Packaging a model and shipping a small web demo.