📚 Syllabus & Course Curriculum
Bioinformatics & Computational Biology
Module-by-module breakdown of Machine Learning for Bioinformatics: Basics, from foundations to a certified capstone project.
Outline
What is Machine Learning? • Role of Machine Learning in Bioinformatics • Basic Concepts in Machine Learning (Supervised, Unsupervised Learning) • Applications of Machine Learning in Biological Data Analysis
Outline
Types of Biological Data (DNA, RNA, Protein Sequences) • Data Representation in Machine Learning (Vectors, Matrices) • Data Preprocessing for Bioinformatics • Handling Missing Data and Noise in Biological Datasets
Outline
Introduction to Classification and Regression • Applying ML Algorithms (e.g., Decision Trees, SVM) to Bioinformatics Data • Evaluating Model Performance (Accuracy, Precision, Recall) • Predicting Disease Outcomes and Biomarkers
Outline
Clustering and Dimensionality Reduction (e.g., K-Means, PCA) • Identifying Patterns and Features in Genomic Data • Exploring Gene Expression and Protein Functionality • Data Visualization in Bioinformatics
Outline
Machine Learning in Drug Discovery and Genomics • Deep Learning and AI in Bioinformatics • Career Opportunities in Computational Biology and Bioinformatics • Mini Learning Activity / Concept-Based Practice
Earn government-registered certification in Machine Learning for Bioinformatics: Basics
e-Certificate and e-Marksheet issued on successful completion.
