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Neural Networks and Random Forests
In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining the structure and properties. Then we’ll code some simple neural network models and learn to avoid overfitting, regularization, and other hyper-parameter tricks. After a project predicting likelihood of heart disease given health characteristics, we’ll move to random forests. We’ll describe the differences between the two techniques and explore their differing origins in detail. Finally, we’ll complete a project predicting similarity between health patients using random forests.
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Rating | Not enough ratings |
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Length | 4 weeks |
Effort | 3 weeks of study, 3-5 hours/week |
Starts | Jul 3 (45 weeks ago) |
Cost | $49 |
From | LearnQuest via Coursera |
Instructors | Rajvir Dua, Neelesh Tiruviluamala |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Programming |
Tags | Data Science Machine Learning |
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Rating | Not enough ratings |
---|---|
Length | 4 weeks |
Effort | 3 weeks of study, 3-5 hours/week |
Starts | Jul 3 (45 weeks ago) |
Cost | $49 |
From | LearnQuest via Coursera |
Instructors | Rajvir Dua, Neelesh Tiruviluamala |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Programming |
Tags | Data Science Machine Learning |
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