Mining Quality Prediction Using Machine & Deep Learning

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5

(4)

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Course Features

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Duration

1.5 hour

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Level

Beginner

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Teaching Type

Self Paced

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Video Content

1.5 hour

Course Description

This 1.5-hour-long project-based course will teach you how to: - Understand the theory behind Simple and Multiple Linear regression. - Import key python datasets, libraries and perform data visualization. - Perform exploratory data analysis. - Standardize training and testing data. Sci-kit Learn library allows you to train and evaluate different regression models. To perform regression, you will need to build and train an Artificial Neural Network. Understanding the differences between different regression models KPIs like MSE, RMSE MAE, R2, adjusted R2 and more. Visualize the performance of your best regression model by using different KPIs.

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

What You Will Learn

Train Artificial Neural Network models to perform regression tasks

Understand the theory and intuition behind regression models and train them in Scikit Learn

Understand the difference between various regression models KPIs such as MSE, RMSE, MAE, R2, adjusted R2

Course Instructors

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Ryan Ahmed

Adjunct Professor & AI Enthusiast

Ryan Ahmed is a professor who is passionate about education and technology. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehi...

Course Reviews

Average Rating Based on 4 reviews

4.8

75%

25%

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