LearnQuest

Machine Learning Models in Science

This course is part of AI for Scientific Research Specialization

Taught in English

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Sabrina Moore
Rajvir Dua
Neelesh Tiruviluamala

Instructors: Sabrina Moore

1,572 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.8

(10 reviews)

Intermediate level

Recommended experience

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

What you'll learn

  • Implement and evaluate machine learning models (neural networks, random forests, etc.) on scientific data in Python

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.8

(10 reviews)

Intermediate level

Recommended experience

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

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This course is part of the AI for Scientific Research Specialization
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There are 4 modules in this course

In this module, we'll tackle the steps taken before we can use AI algorithms. We'll start with an introduction to the most prominent data preprocessing techniques including filling in missing values and removing outliers. Then we'll dive into data transformations including PCA and LDA, two methods featured heavily for dimensionality reduction. Finally, we'll learn how to code the algorithms in Python to set up your data for use in the next module.

What's included

12 videos4 readings2 quizzes1 discussion prompt

In this module, we'll dive into two of the most foundational machine learning algorithms: K-Means and support vector machines. We'll start by comparing the two branches of ML: supervised and unsupervised learning. Then, we'll go into the specific similarities and differences between K-Nearest neighbors for classification and K-Means clustering. Finally, we'll perform deep dives into K-Means and SVMs, learning the basic theory behind them and how to implement each in Python.

What's included

4 videos3 readings2 quizzes1 programming assignment1 discussion prompt2 ungraded labs

In this module, we'll explore some advanced AI techniques. We'll start with tree-based algorithms, made popular because of the use of random forests for both classification and regression. Then, we'll build our way to neural networks, starting from experimentation on the different models. We'll spend some time in the Tensorflow playground getting familiar with the different mechanics behind neural networks. Finally, we'll code our own neural networks to make predictions on unseen data.

What's included

1 video4 readings1 quiz1 programming assignment1 discussion prompt2 ungraded labs

In this module, we'll go through a course project to predict diabetes from health data. We'll compare different regressors by implementing them and checking the error on a test set.

What's included

1 programming assignment1 ungraded lab

Instructors

Sabrina Moore
LearnQuest
3 Courses60,518 learners

Offered by

LearnQuest

Recommended if you're interested in Machine Learning

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3.8

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Reviewed on Jul 7, 2022

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