Python for Machine Learning and Data Mining

วันที่ 1

9.00 - 12.00

• Module 1: Introduction to Data Science
• Module 2: Introduction to Python
• Module 3: CRISP-DM
• Module 4: Python Packages
• Module 5: Python Variables
• Module 6: Data type and Data Importing
• Module 7: Missing value and Outlier Handling
• Module 8: Imbalance Data
• • Up sampling
• • Down sizing
• Module 9: Feature selection and Dimensionality Reduction

13.00 - 16.00

• Module 10: Introduction to Data Visualization
• Module 11: Machine Learning Overview
• Module 12: Association Rule
• Module 13: Basic Classification Technique
• Module 14: Classification Performance Measurement

• Module 15: Machine Learning Overview
• Association Rule (apyori library)
• Module 16: Basic Classification Technique
• Module 17: Classification Performance Measurement

วันที่ 2

9.00 - 12.00 

• Module 18: Additional Classification Techniques
• Module 19: Regression Methods
• หาค่า MAE
• หาค่า MSE
• หาค่า RMSE

13.00 - 16.00

• Module 20: Clustering Methods
• Module 21: Neural Networks

วันที่ 3

9.00 - 12.00

• Module 22: Tuning techniques
• Module 23: Deploy model (streamlit)
• Module 24: Demonstration
• Module 25: Introduction to Text mining

13.00 - 16.00

• Module 26: Text mining ENG
• Module 27: Text mining THAI

วันที่ 4

9.00 - 12.00 

• Module 28: Tokenize
• Module 29: Sentiment Analysis

13.00 - 16.00

• Module 30: Text Clustering
• Module 31: Text Classification