4) An Overview on Logistic Regression in ML

 An Overview on Logistic Regression in MLIntroduction:                               This is a supervised ML algorithm and also known to solve the binary classification problems. Binary means two and classification means natures and this means that we use it, to predict the nature between two natures.  In simple words here we have two classes it can be true(presence)...

Read More

3) Multiple Linear Regression

 Multiple Linear RegressionHere we have multiple features such as (x1,x2,x3...) instead of one feature (x1) and after feed these features to model we can get the output (y). Before proceeding away we should understand some of the terminologies which we use for ML. let's assume a[i][j] here will the help of j we can proceed toward columns and with the help of i we can proceed toward rows. Furthermore, n is the total number features.How Model...

Read More

2) Linear Regression Model

 Linear Regression ModelThis is the type of Supervised Learning Algorithms. Here we will predict a value after getting some inputs. We have some features x (if you have multiple features then x will become x1, x2, x3 and so on)as input. We will take these features and feed them to a trained model and our model will predict a value as output. In this way linear Regression Model works. Now let’s try to go in depth of model.If we break the model...

Read More

1) Machine Learning

 Machine LearningIntroduction:Machine Learning is the subset of Artificial Intelligence. Now a days this is very buzz word. If you ask from anyone what do you want to become than most of the people answer it as Machine Learning Engineer. This is not easy as sound like here you have a huge grip on Math and stats to become a good ML Engineer. Actually, This is explicit program which Help us to get label or predict label, recommend videos to anyone...

Read More