Mining Quality Prediction Using Machine & Deep Learning
Offered By: Coursera Project Network via Coursera
Course Description
Overview
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.
Syllabus
- Mining Quality Prediction
- In this hands-on project, we will train machine learning and deep learning models to predict the % of Silica Concentrate in the Iron ore concentrate per minute. This project could be practically used in Mining Industry to get the % Silica Concentrate at a much faster rate compared to the traditional methods. In this hands-on project we will go through the following tasks: (1) Understand the Problem Statement, (2) Import libraries and datasets, (3) Perform Exploratory Data Analysis, (4) Perform Data Visualization, (5) Create Training and Testing Datasets, (6) Train and Evaluate a Gradient Boosting Regressor Model, (7) Train and Evaluate a Decision Tree Regressor Model,(8) Train and Evaluate a Random Forest Regressor Model, (9) Train and Evaluate an Artificial Neural Network Model, (10) Calculate and Print Regression model KPIs.
Taught by
Ryan Ahmed
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