A big welcome to “Bioinformatics: Introduction and Methods” from Peking University! In this MOOC you will become familiar with the concepts and computational methods in the exciting interdisciplinary field of bioinformatics and their applications in biology, the knowledge and skills in bioinformatics you acquired will help you in your future study and research.

This course is comprehensive as it include all the basics and concepts of drug discovery. After obtaining this course you will be able to easily design your drug from the scratch.

This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.

This is Supervised machine learning full course. It covers all basic concepts from Python, Pandas, Django, Ajax and Scikit Learn. The course start on Jupyter notebook where different operations will performed on data. Learn Python basic, mostly control structure and Django. The end goal of this course is to teach how to deploy machine learning model on Django Python web framework. Actually, that is the purpose of machine learning. We should learn how to put machine learning model into application and it must solve people's problems in the community.

This course is aimed at scientists with no previous experience of machine learning (ML) and who are interested in the applications of ML in drug discovery and will provide a broad overview of how to develop machine learning models without programming using WEKA.

In this capstone project course, the focus is on using genome sequencing and computational techniques to aid in drug discovery for COVID-19.

This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

This course is about applications of Artificial Intelligence & Machine Learning techniques in Drug Discovery, Designing & Development process

The course features theoretical lectures by eminent faculty from the fields of Computer Science and Natural Sciences. It also includes a programming tutorial component to help develop practical skills. The course is ideally suited for students and researchers who may want to develop interdisciplinary skills in solving computationally intensive problems involving natural sciences.