Introduction to Machine Learning Laboratory Notes
Subject Overview
The practical companion to Introduction to Machine Learning, implementing regression, classification, clustering, dimensionality reduction and ensemble learning in Python — from linear/
Subject at a glance
1
Units
12
Topics
1
Credits
1
Infographics
Unit-wise Syllabus
1 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- Linear regression with regression line visualization
- logistic regression with decision boundary
- decision tree (ID3/CART) classifier
- Naive Bayes classifier
- random forest classifier
- SVM for linearly separable classes
- K-Means clustering with visualization
- hierarchical clustering with dendrogram
- DBSCAN clustering
- PCA with classifier performance comparison
- single-layer perceptron for AND/OR/XOR
- AdaBoost boosting demonstration
- 1Load data
- 2Split
Training and test sets
- 3Train
Fit the model
- 4Evaluate
Accuracy, precision, recall
- 5Visualise
Decision boundary, clusters or tree
Study guide
In this Python lab you implement the models from class: linear regression with a regression line plot, logistic regression with a decision boundary, decision trees (ID3 and CART), Naive Bayes, random forest, SVM for linearly separable classes, K-Means, hierarchical clustering with a dendrogram, DBSCAN, PCA with classifier comparison, a single-layer perceptron for AND, OR and XOR, and an AdaBoost demonstration. Notice that a single-layer perceptron cannot learn XOR; understanding why is a classic viva question.
Why Introduction to Machine Learning Laboratory Matters
Training and evaluating real models in Python is the practical foundation for every ML internship or project.
How to Prepare for Introduction to Machine Learning Laboratory
- Split data into training and test sets in every experiment.
- Visualise results with plots; they make the viva easy.
- Write short notes on what each model assumes and where it fails.
Introduction to Machine Learning Laboratory: Frequently Asked Questions
What models are built in the ML lab?
Regression, decision trees, Naive Bayes, random forest, SVM, K-Means, hierarchical clustering, DBSCAN, PCA, a perceptron and AdaBoost.