Great Learning

 Hello Everybody, Today we'll learn about network and graphical model. so let's start. Graph is basically study of the relationships. modern graph networks are loosely inspired by human brain. [draw diagram] in the human brain also we have Neurons and one neuron is connected to other neuron through dendrite. similarly in graphs also we have similar network but here we call it as node and multiple nodes connected to each other through edge. [show in existing diagram]  if we are creating the graph for social networks then each node represents person and edge represents friendship or connection. similarly if we are creating for maps then node represents city or place and edge represents connecting road. But why do we study the Graph and networks? whole facebook recommendation system depends on graph. If I want to advertise some product or If I want to find an influencer who can advertise my product facebook can easily find node who is the most influential by using some graph tec...

Principle component Analysis  * Application of PCA: 1. Dimensionality reduction - Explain with one example 2. Data Visualization - we cant visualize beyond 3D so if we want to visualize multidimensional data say 10 Dimensional data then we can use PCA. 3. Data Anonymization - if you don't want to send any confidential information then you can use PCA. give an example of credit card fraud detection. https://www.kaggle.com/mlg-ulb/creditcardfraud 4. Factor Analysis: PCA component is basically a linear combination of multiple features so you can use it for factor analysis. How PCA does dimension reduction. If I take any point then I need two values (x1 and x2) to represent this point. that means this is 2 dimensional. Now how can I represent this same data on only 1 dimension without losing any information? We can do this by rotating the axis. if I rotate my axis like this then I can see our all points are lying on the only one axis so we have preserved our all the information on...