Machine Learning: Regression
COURSE DESCRIPTION
Regression is a large subset of Machine Learning problems that involves predicting a numerical value using known variables without having to personally work out relationships between those. While this course’s main focus is on constructing and utilizing an appropriate Regression model on a determined problem, you must also understand specific concepts that are universal to Machine Learning as a whole.
LEARNING OUTCOMES
- Understand regression problems in machine learning
- Understand and practice Simple Linear Regression and Multiple Regression
- Understand metrics, why and how are they used for Assessing Performance
- Understand what is Overfit, why it happens and how it impact model quality
- Understand and practice Ridge Regression and LASSO Regression to resolve Overfit
- Understand and practice K-Nearest Neighbor and Kernel Regression
- Apply all learned techniques to solve real-world problems
Course Content
Time: 43 hours
Module 1 – Regression Overview 0/0
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Module 2 – Regression Algorithms 0/0
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Assignment 1: Predict ‘Visibility (km)’ attribute of the weather 0/0
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Module 3 – Regression Tuning 0/0
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Assignment 2 – Create a Regression model predicting the number of Facebook comments at a specific time and optimize the model 0/0
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Module 4 – Time Series Forecasting 0/0
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Instructor
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