1141_The Application of Artificial Intelligence on Business Forecasting_352488
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Course Intro

Course Resources

  • 1-9月11-course introduction
  • 2-9月18-chp2. Basic practice of data processing, and three data formats: DataFrame, array, list
  • 3-9月25-Example 5-3, 5-4, 5-5. Using sklearn module, "Simple 3-Step" to build a "Logistic Regression" model to predict "Customer Value in CRM"
  • 4-10月02-Example 5-1. Linear Regression Model,Example 5-13. Nonlinear Polynomial Regression Model,Example 5-7. Support Vector Machine (SVM) model
  • 5-10月09-Example 5-8. build a "K-means Clustering model ,Example 5-10. Using keras module for deep learning
  • 6-10月16-Example 5-11. Using keras module for deep learning, "Simple 4-Step" to build a "Neural Network" with "Multiple Neurons" for a "Nonlinear Regression Model" to predict y given x
  • 7-10月23-Example 6-1. Using sklearn module, "Beginner 5-Step" to build a "Linear Regression Model" to predict the "USD-Gold Relationship"
  • 8-10月30-Example 6-3. Using sklearn module, "Beginner 5-Step" to build a "KNN (K-Nearest Neighbors)" model to predict "Customer Value in CRM"
  • 9-11月13日-Example 6-5. Using sklearn module, "Beginner 6-Step" to build a "Decision Tree" model to predict "Customer Value in CRM"
  • 10-11月20日-Example 6-7. Using sklearn module, "Beginner 6-Step" to build a "Support Vector Machine (SVM)" model to predict "Titanic Passenger Survival"
  • 11-11月27日-Example 6-8. Using sklearn module, "Beginner 7-Step" to build a "K-means Clustering" model to automatically cluster "Zoo Animal Data"
  • 12-12月4日-Example 6-10. Using keras module for deep learning, "Beginner 6-Step" to build a "Neural Network" with "One Neuron" for a "Linear Regression Model" to predict y given x
  • 13-12月11日-Example 6-12. Using keras module for deep learning, "Beginner 6-Step" to build a "Neural Network" with "Multiple Neurons" for a "Nonlinear Classification Model" to predict whether patients have diabetes
  • 14-12月18日-Example 7-1. Using seaborn module to display "Iris Dataset" with advanced visualization of multiple features
Teacher / 陳擎文

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