Course Overview: Welcome to the powerful intersection of Artificial Intelligence and Biology. This course is meticulously designed for those who want to solve biological mysteries in the "Big Data" era. If you want to discover biological patterns or build predictive models for disease diagnosis, this course provides you with that roadmap through Machine Learning.

What You Will Learn: We have divided this curriculum into 3 distinct modules comprising 9 comprehensive classes. This journey will take you from data preprocessing all the way to advanced model building.

• Module 1: Foundations of Data Science in Biology Understand the core concepts of handling biological data. You will learn how to convert raw DNA/RNA sequence data into a machine-readable format and how to clean data effectively to avoid the "Garbage In, Garbage Out" problem.

• Module 2: Supervised Learning Models Dive into predictive modeling. You will receive hands-on training on algorithms used to detect new diseases using historical data (Classification) and predict drug responses (Regression).

• Module 3: Unsupervised Learning & Advanced Applications Move beyond labeled data using Clustering and Dimensionality Reduction. Here, you will learn how to identify patterns within thousands of genes and visualize complex high-dimensional data. Finally, an introduction to Deep Learning concepts will be provided.

Who Should Attend:

• Undergraduate and Postgraduate students in Bioinformatics, Biotech, and CSE.

• Researchers who want to analyze data from their biological experiments.

• Anyone interested in Data Science careers in the Healthcare and Pharma industry.

Outcome: By the end of this course, you will be able to handle biological datasets, create ML models for your own research, and gain a solid foundation for building a career in Computational Biology.