Machine Learning Laboratory Notes
Subject Overview
The practical companion to Machine Learning, designing and evaluating data models using linear regression, logistic regression, K-Nearest Neighbors, K-Means clustering and Support Vector Machines in Python, R or MATLAB. A 2-credit elective lab paper.
Subject at a glance
1
Units
5
Topics
2
Credits
1
Infographics
Unit-wise Syllabus
1 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- Data model design and evaluation using Linear Regression
- Logistic Regression
- K-Nearest Neighbors
- K-Means Clustering and Support Vector Machines
- using standard or assumed datasets in Python/R/MATLAB
- 1
Load data
pandas.read_csv
- 2
Explore and clean
- 3
Split train and test
train_test_split
- 4
Fit the model
LinearRegression, KNN, SVM
- 5
Evaluate
Accuracy, confusion matrix, RMSE
- 6
Visualise results
Study guide
In this lab you design and evaluate models using linear regression, logistic regression, k-nearest neighbours, K-means clustering and support vector machines on standard datasets in Python, R or MATLAB. Plot results and report metrics for each model.
Why Machine Learning Laboratory Matters
Hands-on modelling experience on real data sets is what ML interviews examine most.
How to Prepare for Machine Learning Laboratory
- Split data properly and report metrics.
- Visualise results.
- Write a short interpretation for each experiment.
Machine Learning Laboratory: Frequently Asked Questions
What models are built in the ML lab?
Linear and logistic regression, k-NN, K-means and SVM.