Live Milestone Capstones
Every trainee builds production-grade AI systems, Computer Vision classifiers, RAG assistants, and Cloud-native Kubernetes pipelines as part of their 60-class journey.
Mini Project: Student Data Analysis System
Work with a real student CSV dataset using Python, NumPy, and Pandas to clean, filter, and extract academic performance metrics.
Mini Project: Sales / Student Performance Statistical Analysis
Calculate and visualize Mean, Median, Mode, Variance, Standard Deviation, Percentiles, and Correlation on real-world datasets.
Project: Complete Exploratory Data Analysis (EDA) Pipeline
Execute complete end-to-end data cleaning, handling missing values, outlier treatment, statistical correlation, and visual dashboard generation.
Course 1 Capstone: Student Performance & Academic Predictor (Data Analysis Edition)
Python → NumPy/Pandas → Data Cleaning → Statistics → EDA → Visualization → Insights → GitHub Portfolio Repository.
Project: Complete Scikit-Learn ML Workflow Pipeline
Dataset → Preprocessing → Feature Engineering → Train → Validation → Test → Evaluation → Model Export.
Project: House Price Prediction System
Real Estate Dataset → Cleaning → Feature Engineering → Multiple Linear Regression → RMSE / R² Evaluation → Live Price Predictor.
Project: Telecom Customer Churn & Loan Approval Predictor
Build and compare Logistic Regression, Random Forest, and SVM models. Evaluate using Confusion Matrix, ROC-AUC, and export trained model.
Project: Customer Segmentation & Behavioral Clustering
Customer Demographic & Spending Data → Preprocessing → K-Means Clustering → Silhouette Evaluation → Targeted Marketing Segments.
Project: Model Performance Optimization & Pipeline Engineering
Take a baseline 70% accurate model, apply feature engineering, scaling, Stratified K-Fold CV, and GridSearchCV to boost performance above 90%.