Master the complete Machine Learning pipeline — from exploratory data analysis to
deploying tuned ensemble models. Every session is anchored in real datasets and working code.
By Week 6 you will have a portfolio of end-to-end ML projects covering
classification, clustering, and advanced model selection.
What You'll Build
- EDA dashboards that uncover hidden patterns
- Feature selection pipelines that boost model accuracy
- Classifiers using Linear, SVM & Tree-based models
- High-performance Ensemble models (RF, XGBoost, Stacking)
- Unsupervised clustering & dimensionality reduction
- Automated hyperparameter tuning with cross-validation
Primary Tools & Stack
Course Modules
Module 01
Getting to Know Your Data
Module 02
Machine Learning Landscape
Module 03
Exploratory Data Analysis (EDA)
Module 04
Feature Selection & Engineering
Module 05
Classification Fundamentals
Module 06
Linear Models & Logistic Regression
Module 07
Support Vector Machines (SVM)
Module 08
Decision Trees & Pruning
Module 09
Ensemble Models (RF, Boosting, Stacking)
Module 10
Unsupervised Learning & Clustering
Module 11
Hyperparameter Tuning & Model Selection
🏆
Capstone
End-to-End ML Project — EDA → Model → Evaluation → Report
Prerequisites
Required
- Python 3.9+ (functions, loops, classes)
- NumPy & Pandas basics
- High-school level statistics (mean, variance)
- GitHub account
Helpful (Not Required)
- Linear algebra fundamentals (vectors, matrices)
- Basic probability & distributions
- Prior exposure to Jupyter Notebooks
Ready to Master Machine Learning?
Join the cohort · Hands-on from Day 1 · Portfolio-ready by Week 6
Enroll Now →