关于此 专项课程

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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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100% 在线课程
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灵活的计划
设置并保持灵活的截止日期。
初级
完成课程大约需要 4 个月
建议进度:3 小时/周
英语(English)
可分享的证书
完成后获得证书
100% 在线课程
立即开始,按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
初级
完成课程大约需要 4 个月
建议进度:3 小时/周
英语(English)

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此专项课程包含 4 门课程

课程1

课程 1

Introduction to Data Science and scikit-learn in Python

课程2

课程 2

Machine Learning Models in Science

课程3

课程 3

Neural Networks and Random Forests

课程4

课程 4

Capstone Project: Advanced AI for Drug Discovery

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LearnQuest

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