Machine Learning

Tutorials

1. Logistic Regression, Overfitting & regularization
2. Data Preprocessing (missing/categorical data)
3. Data Preprocessing II - Partitioning a dataset / Feature scaling / Feature Selection / Regularization
4. Data Preprocessing III - Dimensionality reduction vis Sequential feature selection / Assessing feature importance via random forests
5. Data Compression via Dimensionality Reduction I - Principal component analysis (PCA)
6. Data Compression via Dimensionality Reduction II - Linear Discriminant Analysis (LDA)
7. Data Compression via Dimensionality Reduction III - Nonlinear mappings via kernel principal component (KPCA) analysis
8. Natural Language Processing (NLP): Sentiment Analysis I (IMDb & bag-of-words)
9. Natural Language Processing (NLP): Sentiment Analysis II (tokenization, stemming, and stop words)
10. Natural Language Processing (NLP): Sentiment Analysis III (training & cross validation)
11. Natural Language Processing (NLP): Sentiment Analysis IV (out-of-core)