LearnQuest
Neural Networks and Random Forests
LearnQuest

Neural Networks and Random Forests

This course is part of AI for Scientific Research Specialization

Taught in English

Some content may not be translated

Rajvir Dua
Neelesh Tiruviluamala

Instructors: Rajvir Dua

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

10 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 quizzes

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

10 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

This course is part of the AI for Scientific Research Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 4 modules in this course

In this module, we'll go through neural networks and how to use them in Python. We'll start by describing what a neural network is and how to construct one by combining a sequence of linear models. Then, we'll talk about converge of neural networks in the hopes of minimizing a loss function. Finally, we'll learn how to code a neural network in Python.

What's included

5 videos3 readings2 quizzes1 discussion prompt

In this module, we'll take a more detailed look into neural network and the considerations we should be having when using them. We'll start by adding layers to our 2-layer network, exploring the different options and their effects. Then, we'll explore some more advanced Python libraries for neural networks in TensorFlow and Keras. Finally, we'll discuss the implications to science and how to apply the models in the space.

What's included

3 videos5 readings1 quiz1 ungraded lab

In this module, we'll build up our knowledge of random forests and their uses in science. We'll start by exploring decision trees and how they operate as models in isolation. Next, we'll look at the impact of combining decision trees to create random forests. From here, we'll talk about the similarities and differences between regression and classification with random forests before concluding with a final project predicting species from lineage.

What's included

2 videos2 readings1 programming assignment1 discussion prompt

In this final project, we'll be comparing a suite of models to find the one that best predicts sepal width.

What's included

1 programming assignment

Instructors

Rajvir Dua
LearnQuest
8 Courses12,651 learners

Offered by

LearnQuest

Recommended if you're interested in Machine Learning

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