Machine Learning with Python and scikit-learn
Why colleges run this
Machine learning is the fastest-growing skill demand across sectors; an end-to-end project from data to a deployed model gives students a portfolio piece and genuine competence.
Course outcomes
CO1
Prepare a raw dataset for modelling through cleaning, encoding and scaling
K3CO2
Build supervised regression and classification models with scikit-learn
K3CO3
Evaluate a model using cross-validation and appropriate metrics
K4CO4
Tune hyperparameters to reduce overfitting
K4CO5
Deploy a trained model as a prediction service
K3Modules tap a module for its theory & lab
Hours shown are the recommended 5-day format — module time scales to the duration you pick.
01ML foundations and workflow5 h
Theory
Problem framing, the ML pipeline, train/test discipline
Lab
Frame a problem and split a dataset correctly
Output
Problem-framing note
02Data preparation5 h
Theory
Cleaning, encoding, scaling, feature engineering
Lab
Prepare a raw dataset for modelling
Output
Prepared dataset + notebook
03Regression5 h
Theory
Linear and regularised regression, evaluation metrics
Lab
Build and evaluate a regression model
Output
Regression notebook
04Classification5 h
Theory
Logistic regression, decision trees, k-NN, support vector machines
Lab
Build and compare classifiers
Output
Classification notebook
05Evaluation and tuning5 h
Theory
Cross-validation, hyperparameter search, over- and underfitting
Lab
Tune a model with cross-validation
Output
Tuning report
06Ensemble methods5 h
Theory
Random forests, gradient boosting
Lab
Improve results with ensembles
Output
Ensemble notebook
07Unsupervised learning5 h
Theory
Clustering, dimensionality reduction
Lab
Cluster a dataset and interpret the groups
Output
Clustering notebook
08Deployment capstone5 h
Theory
Packaging a model, serving predictions
Lab
Deploy the best model as a service
Output
Deployed prediction service
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice Optional nasscom NSQF assessment