Week 1 — Python & Data Wrangling
Build your foundation: Python, NumPy, pandas, and data cleaning. By end of this week you can read any messy CSV and turn it into clean, analyzable data.
Lesson 2 — NumPy for Numerical Computing (Animated)
Lesson 2 — NumPy for Numerical Computing (Theory)
Lesson 2 — NumPy for Numerical Computing (Lab)
Lesson 3 — pandas for Data Manipulation (Animated)
Lesson 3 — pandas for Data Manipulation (Theory)
Lesson 3 — pandas for Data Manipulation (Lab)
Lesson 4 — Data Cleaning & Preparation (Animated)
Lesson 4 — Data Cleaning & Preparation (Theory)
Lesson 4 — Data Cleaning & Preparation (Lab)
Week 2 — Statistics & Exploratory Data Analysis
Think like a scientist: descriptive stats, visualisations, hypothesis testing, and the EDA checklist that every senior data scientist runs on every new dataset.
Lesson 1 — Descriptive Statistics (Animated)
Lesson 1 — Descriptive Statistics (Theory)
Lesson 1 — Descriptive Statistics (Lab)
Lesson 2 — Data Visualisation with matplotlib & seaborn (Animated)
Lesson 2 — Data Visualisation with matplotlib & seaborn (Theory)
Lesson 2 — Data Visualisation with matplotlib & seaborn (Lab)
Lesson 3 — Hypothesis Testing & A/B Tests (Animated)
Lesson 3 — Hypothesis Testing & A/B Tests (Theory)
Lesson 3 — Hypothesis Testing & A/B Tests (Lab)
Lesson 4 — EDA Walkthrough on Real Data (Animated)
Lesson 4 — EDA Walkthrough on Real Data (Theory)
Lesson 4 — EDA Walkthrough on Real Data (Lab)
Week 3 — Supervised ML I
Train your first ML models from scratch. The workflow, train/test split, Linear Regression, Logistic Regression, and Decision Trees with real Indian datasets.
Lesson 1 — ML Workflow & Train/Test Split (Animated)
Lesson 1 — ML Workflow & Train/Test Split (Theory)
Lesson 1 — ML Workflow & Train/Test Split (Lab)
Lesson 2 — Linear Regression (Animated)
Lesson 2 — Linear Regression (Theory)
Lesson 2 — Linear Regression (Lab)
Lesson 3 — Logistic Regression (Animated)
Lesson 3 — Logistic Regression (Theory)
Lesson 3 — Logistic Regression (Lab)
Lesson 4 — Decision Trees (Animated)
Lesson 4 — Decision Trees (Theory)
Lesson 4 — Decision Trees (Lab)
Week 4 — Supervised ML II
Make your models production-grade: Random Forests, feature engineering, regularisation, and hyperparameter tuning. From 75% accuracy to 88%.
Lesson 1 — Decision Trees Deep Dive (Animated)
Lesson 1 — Decision Trees Deep Dive (Theory)
Lesson 1 — Decision Trees Deep Dive (Lab)
Lesson 2 — Random Forests (Animated)
Lesson 2 — Random Forests (Theory)
Lesson 2 — Random Forests (Lab)
Lesson 3 — Feature Engineering (Animated)
Lesson 3 — Feature Engineering (Theory)
Lesson 3 — Feature Engineering (Lab)
Lesson 4 — Regularisation & Hyperparameter Tuning (Animated)
Lesson 4 — Regularisation & Hyperparameter Tuning (Theory)
Lesson 4 — Regularisation & Hyperparameter Tuning (Lab)
Week 5 — Unsupervised & Deep Learning
Expand the toolkit: customer segmentation with K-Means, dimensionality reduction with PCA, and your first neural network for end-to-end ML pipelines.
Lesson 1 — K-Means Clustering (Animated)
Lesson 1 — K-Means Clustering (Theory)
Lesson 1 — K-Means Clustering (Lab)
Lesson 2 — PCA — Principal Component Analysis (Animated)
Lesson 2 — PCA — Principal Component Analysis (Theory)
Lesson 2 — PCA — Principal Component Analysis (Lab)
Lesson 3 — Introduction to Neural Networks (Animated)
Lesson 3 — Introduction to Neural Networks (Theory)
Lesson 3 — Introduction to Neural Networks (Lab)
Lesson 4 — End-to-End ML Pipeline (Animated)
Lesson 4 — End-to-End ML Pipeline (Theory)
Lesson 4 — End-to-End ML Pipeline (Lab)
Week 6 — Capstone Project
Build a complete Customer Churn ML system end-to-end: EDA, baseline, model, evaluation, deployment, and explainability. Your portfolio centrepiece.
Lesson 1 — Customer Churn Capstone — End-to-End (Animated)
Lesson 1 — Customer Churn Capstone — End-to-End (Theory)
Lesson 1 — Customer Churn Capstone — End-to-End (Lab)
Bonus — Career Awareness
Turn the internship into job offers: ATS-friendly resume, polished GitHub portfolio, and ML interview prep covering bias/variance, metrics, and STAR answers.
Lesson 1 — Data Science Resume Writing (Animated)
Lesson 1 — Data Science Resume Writing (Theory)
Lesson 1 — Data Science Resume Writing (Lab)
Lesson 2 — GitHub Portfolio Polish (Animated)
Lesson 2 — GitHub Portfolio Polish (Theory)
Lesson 2 — GitHub Portfolio Polish (Lab)
Lesson 3 — ML Interview Preparation (Animated)
Lesson 3 — ML Interview Preparation (Theory)
Lesson 3 — ML Interview Preparation (Lab)
Lesson 1 — Python Basics for Data Science (Animated)
No Attachment Found
