Mining Quality Prediction Using Machine & Deep Learning

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Coursera Project Network
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在此指導項目中,您將:

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

Clock1.5 hours
Beginner初級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

  • regression models
  • Deep Learning
  • Artificial Intelligence (AI)
  • Machine Learning
  • Python Programming

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Understand the problem statement and business case

  2. Import libraries/datasets and perform data exploration

  3. Perform data visualization

  4. Prepare the data before model training

  5. Train and evaluate a linear regression model

  6. Train and evaluate a decision tree and random forest models

  7. Understand the theory and intuition behind artificial neural networks

  8. Train an artificial neural network to perform regression task

  9. Compare models and calculate regression KPIs

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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