AI for Scientific Research

In the AI for Scientific Research specialization, participants will delve into the application of artificial intelligence in scientific contexts to uncover trends and patterns within datasets. The course is structured into four modules, with each module building on the knowledge and skills acquired in the previous one.

• Module 1: Introduction to Data Science and scikit-learn in Python - Learn to employ artificial intelligence techniques to test hypotheses and apply a machine learning model combining Numpy, Pandas, and Scikit-Learn.
• Module 2: Machine Learning Models in Science - Implement and evaluate various machine learning models on scientific data in Python, including neural networks and random forests.
• Module 3: Neural Networks and Random Forests - Gain a deeper understanding of advanced AI techniques, exploring neural networks and random forests, and complete projects predicting likelihood of heart disease and similarity between health patients.
• Module 4: Capstone Project: Advanced AI for Drug Discovery - Analyze genome sequences to identify potential areas for drug therapy using predictive models.

Upon completion, learners will be equipped with the knowledge and skills to apply AI in scientific research, from basic data analysis to advanced machine learning and predictive modeling.