🤖
AI

Machine Learning —
Models & Projects

Build, train, and evaluate machine learning models using Scikit-learn. Work with real-world datasets across classification, regression, clustering, and more.

📘 34 Lessons
⏱ ~22 hrs
🎯 Intermediate → Advanced
🌐 English
⭐ 4.9 (1.1k reviews)
👥 6,900 enrolled
Free
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This course includes

✓ 34 structured lessons
✓ Scikit-learn from scratch
✓ 5 real dataset projects
✓ Model evaluation techniques
✓ Hyperparameter tuning
✓ Certificate on completion
Overview
Curriculum
Requirements
Instructor

What you'll learn

✓ Core ML concepts: supervised vs unsupervised
✓ Linear & logistic regression
✓ Decision trees, random forests
✓ SVM, KNN, Naive Bayes
✓ K-Means clustering
✓ Model evaluation: accuracy, F1, ROC-AUC
✓ Feature engineering & selection
✓ Hyperparameter tuning & cross-validation

Course Curriculum

Module 1 — ML Foundations
6 lessons · ~3 hrs
⌄
▶ What is Machine Learning? 14 min
▶ Types of ML: Supervised, Unsupervised, RL 16 min
▶ Scikit-learn Overview 18 min
▶ Train/Test Split & Bias-Variance 20 min
▶ Feature Scaling (Standard, MinMax) 16 min
📝 Module Quiz 10 min
Module 2 — Regression Models
7 lessons · ~3.5 hrs
⌄
▶ Linear Regression 22 min
▶ Multiple Linear Regression 18 min
▶ Polynomial Regression 16 min
▶ Ridge & Lasso 20 min
▶ Evaluation: MSE, RMSE, R² 14 min
▶ Logistic Regression 22 min
🛠 Project: House Price Predictor —
Module 3 — Classification Models
8 lessons · ~4 hrs
⌄
▶ Decision Trees 22 min
▶ Random Forest 24 min
▶ K-Nearest Neighbors (KNN) 18 min
▶ Support Vector Machines 22 min
▶ Naive Bayes 16 min
▶ Confusion Matrix, F1, ROC-AUC 18 min
▶ Cross-Validation & Grid Search 20 min
🛠 Project: Spam Classifier —
Module 4 — Unsupervised & Capstone
7 lessons · ~3.5 hrs
⌄
▶ K-Means Clustering 22 min
▶ Hierarchical Clustering 18 min
▶ PCA — Dimensionality Reduction 22 min
▶ Feature Engineering 20 min
▶ Pipeline API in Scikit-learn 16 min
▶ Deploying a Model (Flask intro) 20 min
🛠 Capstone: Customer Segmentation —

Requirements

Instructor

🤖
Dr. Aditya Kulkarni
ML Engineer · AI Researcher

Aditya holds a PhD in Computer Science with specialization in machine learning. He has published research in NLP and computer vision and now leads ML engineering at a Series B AI startup. His courses emphasize intuition first, math second.