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
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 much faster rate compared to the traditional methods.
regression models
Deep Learning
Artificial Neural Network
Machine Learning
Python Programming
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Understand the Problem Statement and Business Case
Practice Opportunity #1 [Optional]
Import Libraries/Datasets and Perform Data Exploration
Practice Opportunity #2 [Optional]
Perform Data Visualization
Practice Opportunity #3 [Optional]
Prepare the Data for Model Training
Train and Evaluate a Linear Regression Model
Train and Evaluate a Decision Tree and Random Forest
Practice Opportunity #4 [Optional]
Understand the Theory and Intuition Behind Artificial Neural Networks
Train an Artificial Neural Network to Perform Regression Tasks
Practice Opportunity #5 [Optional]
Compare Models and Calculate Regression KPIs
Capstone Final Project
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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