Mining Quality Prediction Using Machine & Deep Learning
Coursera Project Network via Coursera
Overview
Class Central Tips
In this 1.5-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Simple and Multiple Linear Regression.
- Import Key python libraries, datasets and perform data visualization
- Perform exploratory data analysis and standardize the training and testing data.
- Train and Evaluate different regression models using Sci-kit Learn library.
- Build and train an Artificial Neural Network to perform regression.
- Understand the difference between various regression models KPIs such as MSE, RMSE, MAE, R2, and adjusted R2.
- Assess the performance of regression models and visualize the performance of the best model using various KPIs.
- Understand the theory and intuition behind Simple and Multiple Linear Regression.
- Import Key python libraries, datasets and perform data visualization
- Perform exploratory data analysis and standardize the training and testing data.
- Train and Evaluate different regression models using Sci-kit Learn library.
- Build and train an Artificial Neural Network to perform regression.
- Understand the difference between various regression models KPIs such as MSE, RMSE, MAE, R2, and adjusted R2.
- Assess the performance of regression models and visualize the performance of the best model using various KPIs.
Taught by
Ryan Ahmed
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