Course Curriculum

It stretches your mind, think better and create even better.

Supervised Learning

What is supervised learning

Algorithms in Supervised learning

R-Square coefficient and RMSE as a strength of model, Prediction and confidence interval determination and application

Sum of least squares, ROC and AUC curves, Homoscedasticity and Heteroscedasticity, Multicollinearity and vif, Confusion matrix

What is KNN and why do we use it?

KNN-algorithm and regression

Curse of dimensionality and brief introduction to dimension reduction

KNN-outlier treatment and anomaly detection

Linear and Non-Linear SVM’s, SVM regression

Principal Component Analysis(PCA)

Singular Value Decomposition(SVD)

K-Means clustering and skew plot Extracting unstructured text from files and websites

Time Series & Time Series forecasting

ETS Model, Auto regressive Model, Moving Average Model, ARIMA Model, ETS Model

Anomaly Detection, Transformations, Growth curve, ARCH & GARCH Models

Metrics of rules-Lift, Support, Confidence, Conviction

Apriority Model, Market Basket Analysis

Algorithm implementation and tuning, Applications

Introduction to NN

Artificial Neural Networks

Back propagation

Boosting techniques

Installation of Trial Version of Tableau Publice

Design Flow, Data Viewing

Forecasting using Exponential Smoothing

Granularity and Trimming, Seasonality, Animations

Assignment