Machine Learning

Course Overview

Machine Learning is an application‑driven course that introduces learners to the core concepts, algorithms, and workflows used to build predictive models. Through hands‑on exercises with real datasets, participants learn how to prepare data, train models, evaluate performance, and apply machine‑learning techniques to solve practical business problems. The course blends theory with implementation using Python and popular ML libraries.

Target Audience

This course is ideal for:

• Aspiring data scientists, ML engineers, and advanced data analysts

• Professionals in tech, HR, finance, marketing, and operations who want to leverage predictive analytics

• Students or career switchers entering AI and data‑science roles

• Anyone with basic Python skills looking to advance into machine learning

Course Outcomes

By the end of this course, learners will be able to:

• Understand key ML concepts: supervised vs. unsupervised learning, model training, overfitting, evaluation

• Build predictive models using algorithms like regression, classification, clustering, and decision trees

• Use Python libraries such as scikit‑learn for model development

• Perform feature engineering and data preprocessing for ML workflows

• Evaluate model performance using metrics like accuracy, precision, recall, RMSE, and AUC

• Apply ML techniques to real‑world business scenarios across domains

• Interpret model outputs and communicate insights effectively