• Interest in Learning: Willingness to explore and understand machine learning concepts.
  
  
• Basic Knowledge: Some background in biology, statistics, and math is helpful.
  
  
• Tech Curiosity: Interest in applying machine learning to biological data.
  
  
• Software: Free tools like Python and Jupyter Notebook will be used — no need to buy anything.
  
  
• Hardware: Laptop or computer with at least 4GB RAM and 100GB storage.
  
  
• Internet: A good internet connection for using online tools like Google Colab.

This intensive crash course bridges the gap between genomics and artificial intelligence, equipping participants with the essential skills to apply machine learning (ML) and deep learning (DL) techniques in biological research. Starting from core ML concepts, the course covers genomic data preprocessing, classification and clustering approaches, DNA sequence analysis, multi-omics integration, and interpretability methods for biomarker discovery. Learners will also explore the future scope of AI in genomics, including emerging trends and real-world applications. Through hands-on training and case studies, participants will gain the confidence to analyze genomic datasets and apply AI/ML tools in research or industry projects

• Understand the fundamentals of AI/ML and their specific applications in genomics and biomedical sciences.
  
  
• Preprocess and manage genomic datasets for machine learning pipelines, ensuring data quality and readiness.
  
  
• Apply classification and clustering algorithms to identify disease patterns, gene expression profiles, and patient subgroups.
  
  
• Use deep learning methods for DNA sequence analysis and complex genomic data interpretation.
  
  
• Integrate multi-omics datasets to uncover insights in systems biology and personalized medicine.
  
  
• Interpret machine learning models for biomarker discovery and evaluate the future role of AI in advancing genomic research.

•  Attendance and Participation: Maintain a minimum of 75% attendance. Regular assessments and attendance contribute to performance evaluation.
•  Discipline: Maintain punctuality and respect in live classes. Engage actively and interact respectfully with instructors and peers.
• Course Fee Payment: Pay course fees on time to avoid suspension or cancellation of access.
• Assignments and Project: Complete assignments and projects sincerely and submit them on time.
• Feedback and Communication: Maintain open communication with instructors and provide constructive feedback.
• Certification: A certificate will be awarded upon course completion.