Day #30 - Machine Learning Fundamentals

Supervised Learning

• Classification and Regression problems
• Past data + Past outputs leveraged
• Regression - Continuous Values
• Classification - Discrete Labels

Unsupervised

• Clustering - Discrete Labels
• Dimensionality reduction - Continuous Values

Classifiers

• SVM (Linear way of approximations)
• KNN (Lazy learner)
• Decision Tree (Rule based approach, Set of Rules)
• Naive Bayes (Pick class with maximum probability)

Evaluation Methods

• K-Fold Validation
• Cross Validation
• Ranking / Search - Relevance
• Clustering - Intra-cluster and inter-cluster distances
• Regression - Mean Square Error
• ROC Curve 

Bagging

• Build classifier with 30% of data
• Again partition and build another classifier with next 30% of data
• Random Forests - Random combination of Trees
• Randomly decide and split on attributes

Boosting

• Multiple weak classifiers build strong classifier
• Sample with replacement
• Adaboost - Adaptive boosting

Stacking

• Use Output from one classifier as input for another classifier
• Knn -> O/P -> SVM

Happy Learning!!!