Capstone Project: Advanced AI for Drug Discovery
Utilize machine learning techniques to analyze COVID-19 genome sequences, aiding in drug discovery efforts and advancing understanding virus's behavior.
Utilize machine learning techniques to analyze COVID-19 genome sequences, aiding in drug discovery efforts and advancing understanding virus's behavior.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI for Scientific Research Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Analyze genome sequences using machine learning
Apply PCA for dimensionality reduction
Implement K-means clustering for sequence analysis
Develop predictive models for sequence similarity
Identify potential drug target subsequences
Skills you'll gain
This course includes:
0.6 Hours PreRecorded video
4 programming assignments
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FullTime access
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There are 4 modules in this course
This capstone project applies machine learning techniques to analyze COVID-19 genome sequences. Students use PCA, K-means clustering, and neural networks to identify potential drug targets.
Capstone Project Advanced AI Drug Discovery Module 1
Module 1 · 2 Hours to complete
Capstone Project Advanced AI Drug Discovery Module 2
Module 2 · 2 Hours to complete
Capstone Project Advanced AI Drug Discovery Module 3
Module 3 · 3 Hours to complete
Capstone Project Advanced AI Drug Discovery Module 4
Module 4 · 3 Hours to complete
Fee Structure
Instructors
Head of Data Science, Machine Learning Solutions
Rajvir Dua is the Head of Data Science at LearnQuest, where he combines his expertise in data science with a strong interest in supply chain management. With experience as a teaching and research assistant, he has been actively involved in the data science and consulting startup space. His academic interests are particularly focused on economic problems related to game theory and optimization, which inform his approach to data-driven decision-making in supply chain contexts.On Coursera, Rajvir offers a variety of courses designed to enhance learners' understanding of data science and its applications. His courses include Advanced AI Techniques for the Supply Chain, Fundamentals of Machine Learning for Supply Chain, and Demand Forecasting Using Time Series. These courses aim to equip students with practical skills in machine learning and data analysis, preparing them for challenges in modern supply chain management. Rajvir's commitment to education and his extensive knowledge in data science make him a valuable resource for those looking to advance their careers in this dynamic field.
Co-founder and CEO, Machine Learning Solutions
Neelesh Tiruviluamala is a math professor and the co-founder and CEO of Machine Learning Solutions, with over ten years of consulting experience in the machine learning domain. He has a keen interest in supply chain problems, appreciating the rich data sets that allow for the application of various quantitative tools. His expertise bridges the gap between theoretical mathematics and practical machine learning applications, making him a valuable asset in the field.On Coursera, Neelesh offers a range of courses that focus on advanced machine learning techniques and their applications in supply chain management. His courses include Advanced AI Techniques for the Supply Chain, Demand Forecasting Using Time Series, and Introduction to Data Science and scikit-learn in Python. Through these courses, he aims to equip learners with the skills needed to leverage data science effectively in real-world scenarios, particularly within the context of supply chain optimization. Neelesh's commitment to education and his extensive experience make him an influential figure in the intersection of mathematics and machine learning.
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