Artificial Intelligence (AI) and Machine Learning (ML) in Drug Designing

Course Features

Course Instructor: Vanercia
Who should do it: Bachelors (Pursuing / Completed) and above
Assessment: Exams at end of course, Quiz and Assignments
Certificate: Yes

Course Details

Data analysis is now being applied to various fields such as medicines and vaccine development, agriculture, industrial biotechnology and environmental remediation. The skilled persons are indispensable for research as well as industry. Collection and processing of large volumes of data especially in pharmaceutical research and development helps to mitigate the cost of new drugs to the market. Artificial intelligence (AI) and machine learning (ML), over the past decade, have become indispensable for biological data analysis. Automotive nature and accurate predictive capabilities of AI and ML has greatly increased the efficiency of drug and diagnostic industries.

Career Options

Artificial intelligence (AI) and machine learning (ML) has become indispensable for scientific research. The most recent extension of artificial intelligence (AI) and machine learning (ML) into Covid vaccine development has paved the way for newer applications. The enhanced predictive capabilities have greatly increased in efficiency. Thus, knowledge of artificial intelligence (AI) and machine learning (ML) and applying these algorithms to real biological data-sets is the need of the hour.

Learning Outcomes

• Course introduces the core concepts of artificial intelligence (AI) and machine learning (ML)
• Gives insight into Supervised learning: Data types and partitioning
• The course also describe about Artificial neural networks
• The course includes online-lectures, presentations and project-based training designed on the inputs from academia and industry.
• Quiz and assignments based on modules covered, for practice.

Course Modules

• Introduction to the core concepts of artificial intelligence (AI) and machine learning (ML)
• Supervised learning: Data types and partitioning
• Nearest neighbors and decision trees
• Artificial neural networks
• Exploratory data analysis (unsupervised learning)
• Strengths and limitations of the various machine learning algorithms