Decision Tree Fundamentals

Presenation onĀ finding best split, gini, entropy, misclassification error, gain ratio, numerical examples.

Decision Tree, Hunt's Algorithm and attribute condition with examples

classification, training and testing of decision tree, accuracy, error rate calculations, examples

Discrete, continuous, record, graph order data set

Data quality, Noise, Outliers, Missing values, duplicate, Aggregation, Sampling

Curse of dimensionality, Feature subset selection, feature creation, transformation, Similarity and measurement

Data, data type and operations in data mining

Motivation to Become Data Scientist, Success Industry Story, Start ups, Traits

Simple Introduction to K Nearest Neighbor Classifier

Artificial Neural Net for Kannada Vowel Recognition

Stock Predict