Page 19 - Full Stack Development

 Logistic Regression
Description: Learn to analyse Attribute Data, understand the principles of Logistic Regression,
Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.
•Principles of Logistic Regression
•Types of Logistic Regression
•Assumption and Steps in Logistic Regression
•Analysis of Simple Logistic Regression result
Description: Learn about the Multiple Logistic Regression and understand the Regression
Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its
elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart
and lift chart.

 Multiple Logistic Regression

 Confusion matrix

 False Positive, False Negative

 True Positive, True Negative

 Sensitivity, Recall, Specificity, F1

 Receiver operating characteristics curve (ROC curve)

 Lift charts and Gain charts

 Regularization Techniques

 Lasso and Ridge Regressions
Multinomial Regression
Description: Get introduced to Multinomial regression, or otherwise known as multinomial
logistic regression, learn about multinomial logit models and multinomial logistic regression
examples.

 Logit and Log Likelihood

 Category Baselining

 Modeling Nominal categorical data

 Additional videos are provided on Lasso / Ridge regression for identifying the most
significant variables
 Data Mining Unsupervised - Clustering
Description: As part of Data Mining Unsupervised get introduced to various clustering
algorithms, learn about Hierarchial clustering, K means clustering using clustering examples
and know what clustering machine learning is all about.

 Hierarchial

 Supervised vs Unsupervised learning

 Data Mining Process

 Measure of distance
 Numeric - Euclidean, Manhattan, Mahalanobis