← Back to Dashboard
Module 01 · Days 1–7
Python & Math Foundations

You already write code — now learn the Python libraries and just enough math to make ML models work. Everything is framed as data transformations, not theory. By end of week you will have built a full EDA pipeline.

D1
NumPy & Pandas — the daily tools of ML
Arrays, vectorised ops, broadcasting, dataframes, groupby — tools you will use every single day.
D2
Linear algebra intuition — vectors, matrices, dot products
Matrices as data tables, dot products as similarity scores. Geometry of ML without the intimidation.
D3
Probability & statistics for developers
Distributions, Bayes theorem, confidence intervals. Every model output is a probability statement.
D4
EDA — turning raw data into insight
Real data is always messy. Build a pipeline: load → clean → explore → visualise → report.
D5
Git, environments & professional project structure
Every ML repo uses Git. Set up the toolchain you will use for the rest of the 100 days.
D6
MLflow — experiment tracking from day one
The difference between a notebook and a real project. Log runs, compare experiments, register models.
🏗
🏗️ PROJECT: EDA Dashboard on a Real Dataset
Full exploratory analysis. Clean → analyse → visualise → 5 actionable insights → shareable report.