MODULE 7 OF 8 · 30 Hrs · 3 Weeks

Machine Learning Fundamentals

This is where data science becomes predictive. Students learn to build, train and evaluate core machine learning models — from regression through clustering — using the same algorithms working data scientists rely on daily.

Who This Module Is For

Students who have completed the BI module and are ready to build and evaluate real machine learning models.

Real-World Relevance

Machine Learning Engineer and Data Scientist roles both assume core ML fluency — the ability to choose, train and correctly evaluate a model is one of the most heavily tested skills in data science interviews.

Program Overview View Hands-On Labs

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Curriculum

What You Will Learn

A detailed, industry-aligned breakdown of every topic covered in this module.

Supervised vs. unsupervised learning
Regression: linear & logistic
Classification: KNN, Decision Trees, Random Forest
Clustering: K-Means & hierarchical clustering
Model evaluation: accuracy, precision, recall, F1
Train-test split, cross-validation & overfitting
Feature scaling & feature selection
Introduction to ensemble methods

Technology Stack

Tools You Will Use

Hands-on time with the same tools used by working data analysts and data scientists today.

Scikit-learn

Machine learning library used to build, train and evaluate classical ML models.

Pandas

Data manipulation library used to clean, transform and analyse structured datasets.

NumPy

Numerical computing library used for array operations and mathematical computation in data science.

Jupyter Notebook

Interactive notebook environment used for data exploration, analysis and model prototyping.

Practical Work

Hands-On Labs

Production-style data science lab scenarios, built using real, messy datasets.

Train and evaluate linear and logistic regression models on real datasets.

Build classification models using KNN, Decision Trees and Random Forest.

Apply K-Means and hierarchical clustering to segment unlabeled data.

Evaluate model performance using accuracy, precision, recall and F1 score.

Apply cross-validation and feature selection to reduce overfitting.

Evaluation

Assessment

Knowledge Assessment

Quiz covering supervised/unsupervised learning, model evaluation metrics and overfitting.

Practical Evaluation

Students must train, tune and evaluate a classification or regression model on an assigned dataset, and justify their evaluation metric choice.

Portfolio

Projects

Industry-style deliverables added directly to your project portfolio.

Portfolio Project 01

Machine Learning Model Lab

Train, tune and evaluate classification and regression models on real datasets.

Module Outcome

What This Module Builds

Students learn to build, train and evaluate core machine learning models to solve real prediction and classification problems.

Maps to job roles

Junior Data ScientistML Engineer (Entry-Level)Data Science AssociateData Analyst (ML-focused)