Main Textbooks

Syllabus & slides:

• Introduction [slides]

• Supervised models [slides]

• Unsupervised models [slides] 

• EDA [slides]

• Evaluation [slides]

• Association Rules [slides] 

• Feature Engineering [slides]

Animations:

• Logistic Regression Illustration.mp4  [code]

• Steps of the K-Medoids Algorithm Presented in the Slides

• 🎵 PCA theme songs: Eigenvalues & Eigenvectors 

• DBSCAN_Slides_example, DBSCAN_From_Scratch_Step_by_Step_Visualization [code]

• Effect of #of components on Noisy digits reconstruction [code]

• Low-bias, High Variance (overfitting) and effect of averaging [code]

• High-bias, Low Variance (underfitting) and effect of averaging

Additional Numeric Examples:

Trees

Quiz1 (Sat-Mon: Solutions), (Sun-Tue: Solutions)

Quiz2 (Sat_Mon: solutions), (Sun-Tue: solutions)

Final Exam: solutions

Homeworks:

1. HW1 (deadline: 25th October) Incomplete DT from scratch

2. HW2 (deadline: 28 November) 

3. HW3 (deadline: 30 December) 

** Final Project [Sales prediction data: train.csv, store.csv, Sentiment analysis data: train_sentiment.csv] (deadline: 31 January)

Recommended Videos:

• EigenValues & EigenVectors