AI & Machine Learning · Level 2

Machine Learning Foundations

By SRTR Academy Faculty

4 modules · 17 lessons · 9 videos · 12 hrs

Supervised/unsupervised learning, regression, classification, decision trees, model evaluation, scikit-learn basics

What you will learn

Module 1: ML Landscape & Setup
- 🎬 What is machine learning? — free preview
- 📄 Types of learning: supervised, unsupervised, reinforcement
- 🎬 Setting up Python + scikit-learn environment
- 📄 The ML workflow end-to-end
Module 2: Supervised Learning — Regression
- 🎬 Linear regression from scratch — free preview
- 📄 Polynomial and regularized regression
- 🎬 Evaluating regression models (MSE, R²)
- 📄 Hands-on: predicting house prices
Module 3: Supervised Learning — Classification
- 🎬 Logistic regression and decision boundaries — free preview
- 📄 Decision trees and random forests
- 🎬 K-Nearest Neighbors (KNN)
- 📄 Model evaluation: accuracy, precision, recall, F1
- 🎬 Hands-on: spam classifier
Module 4: Unsupervised Learning & Next Steps
- 🎬 K-Means clustering — free preview
- 📄 Dimensionality reduction with PCA
- 🎬 Model selection and cross-validation
- 📄 Deploying a simple ML model