Module 0A: Machine Learning Foundations

A required foundation module covering classical ML concepts, model training, feature engineering, evaluation, and deployment thinking.

Syllabus Modules

Module 0A: Machine Learning FoundationsComplete

Understand supervised and unsupervised classical ML pipelines: data preprocessing, regression, classification, model evaluations, clustering, tuning, and prediction APIs.

Lessons & Submodules

What is machine learning?ML workflow Data preprocessing Regression Classification Model evaluation Feature engineering Unsupervised learning Model selection and tuning ML deployment thinking

Total Lessons: 10Explore Module

Track Progress

0 / 6Projects Verified

Learning Outcomes

Implement data preprocessing, cleaning, and feature scaling pipelines
Train and evaluate supervised models for classification and regression
Execute unsupervised clustering and dimensionality reduction
Analyze model bias-variance tradeoffs and deploy prediction APIs

Interview Defense

Explain evaluation metrics (Precision, Recall, F1) and when accuracy is misleading
Defend feature engineering choices and address data leakage