machine-learning-notebooks

As part of Teaching Assistant of NUS-BT5151 we had prepared some practice questions and its corresponding python solutions. This repository contains the the questions and solutions corresponding to it. Solutions are compiled in *.ipynb file.

Tutorial 1

1. Basic Python(By John Ang)
2. Third-Party Python Libraries (By Dipika)

• NumPy (for low-level math operations)
• Pandas (for data loading and manipulation)
• Matplotlib and Seaborn (for data visualization)

Tutorial 2:

Support Vector Machine (By Dipika)

1. An example of classification
2. An example of regression

Tutorial 3:

K-Nearest Neighbours (By John Ang and Yihong Lan) cover grid search through the paramters

Tutorial 4:

K-Mean Clusttering (By John Ang and Xiaofei Sun)

Tutorial 5:

• PCA (By Dipika)
• GMM (By Dipika)

Tutorial 7: Handling Text Data

• QUESTION 1: TWITTER SENTIMENT ANALYSIS (By Dipika)
• QUESTION 2: WHAT IS COOKING? (By Dipika)
• QUESTION 3: IMDb Movie Review (By Xiaofei Sun)

Tutorial 8: Handling Text Data

• QUESTION 1: UPVOTED KAGGLE DATASETS (Topic Modelling) (By Dipika)
• QUESTION 2: SMS-SPAM-COLLECTION-DATASET (By Dipika)
• QUESTION 3: Lebanese Arabic Reviews (OCLAR) Data Set (By Dipika)