Horizon University

This course is a Template, please add your description and change the course photo.

This course is a Template, please add your description and change the course photo.

This course is a Template, please add your description and change the course photo.

This module introduces the end-to-end machine learning pipeline, from raw data to ML models. It covers:

• Data preparation and preprocessing: Cleaning, transforming, and handling missing or noisy data.

• Feature extraction and selection: Identifying the most relevant features to improve model performance.

• Supervised learning: Regression (predicting continuous values) and classification (predicting discrete categories).

• Unsupervised learning and clustering: Discovering hidden patterns and grouping similar data points.

• Model evaluation and deployment: Assessing performance, avoiding overfitting, and applying models to real-world problems.

Students gain hands-on experience implementing ML pipelines using Python and popular ML libraries, preparing them to solve practical problems.