Description
- Introduction and business scenario
- CRISP-DM
- Visualising data
- k-Nearest Neighbour
- Naïve Bayes
- Linear Regression
- Decision Trees
- Unsupervised Learning:
Clustering
– Hierarchical
– K means
Association Mining
– Market Basket Analysis - Bias vs Variance (Underfitting vs Overfitting)
- Split and cross validation
- Evaluation methods & performance criteria
- Scoring models
