Mining Quality Prediction Using Machine & Deep Learning
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.
Skills you will develop
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
How Guided Projects work
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
Reviews
TOP REVIEWS FROM MINING QUALITY PREDICTION USING MACHINE & DEEP LEARNING
You must try, it will give great basic of practice.
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