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Sabrina Moore, Rajvir Dua, and Neelesh Tiruviluamala

In the AI for Scientific Research specialization, we'll learn how to use AI in scientific situations to discover trends and patterns within datasets. Course 1 teaches a little bit about the Python language as it relates to data science. We'll share some existing libraries to help analyze your datasets. By the end of the course, you'll apply a classification model to predict the presence or absence of heart disease from a patient's health data. Course 2 covers the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms.In the final project, we'll apply our skills to compare different machine learning models in Python. In Course 3, we will build on our knowledge of basic models and explore more advanced AI techniques. We’ll describe the differences between the two techniques and explore how they differ. Then, we’ll complete a project predicting similarity between health patients using random forests. In Course 4, a capstone project course, we'll compare genome sequences of COVID-19 mutations to identify potential areas a drug therapy can look to target. By the end, you'll be well on your way to discovering ways to combat disease with genome sequencing.

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What's inside

Four courses

Introduction to Data Science and scikit-learn in Python

(0 hours)
This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypotheses. We'll start from the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypotheses. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn.

Machine Learning Models in Science

(0 hours)
This course introduces machine learning techniques for scientific problems. It covers the complete machine learning pipeline, from data preprocessing to advanced models. Topics include PCA, LDA, SVMs, K-means clustering, random forests, and neural networks. Medical and astronomical datasets are used throughout. The final project involves comparing different machine learning models in Python.

Neural Networks and Random Forests

(0 hours)
In this course, we will explore advanced AI techniques, including neural networks and random forests. We'll start with neural networks, examining their structure and properties. Then we'll code simple neural network models and learn to avoid overfitting. After a project predicting heart disease, we'll move to random forests, exploring their differences from neural networks. Finally, we'll complete a project using random forests to predict similarity between health patients.

Capstone Project: Advanced AI for Drug Discovery

(0 hours)
In this capstone project, we'll compare genome sequences of COVID-19 mutations to identify potential drug targets. We'll use genome sequencing to identify target subsequences, perform PCA to reduce dimensionality, and use K-means clustering to find the optimal number of groups and trace the virus's lineage. Finally, we'll predict similarity between the sequences to pick a target subsequence.

Learning objectives

  • How to use ai in scientific situations to discover trends and patterns within datasets
  • The complete machine learning process
  • U​se artificial intelligence to predict sequences in datasets

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