Mining Quality Prediction Using Machine & Deep Learning

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Mining Quality Prediction Using Machine & Deep Learning Mining Quality Prediction Using Machine & Deep LearningIn this 1.5-hour long project-based course, you will be able to: – Understand the theory and intuition behind Simple and Multiple Linear Regression.

In this Guided Project, you will:

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 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.

Learn step-by-step
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

Import libraries/datasets and perform data exploration

Perform data visualization

Prepare the data before model training

Train and evaluate a linear regression model

Train and evaluate a decision tree and random forest models

Understand the theory and intuition behind artificial neural networks

Train an artificial neural network to perform regression task

Compare models and calculate regression KPIs

Thus, the present study aims to evaluate the feasibility of using machine learning algorithms to predict the percentage of silica concentrate (SiO2) in the froth flotation processing plant in real-time. The predictive model has been constructed using iron

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