Logistic Regression Machine learning is the study of algorithms that can improve automatically through experiences by using trained data. While training the model we use one of the popular Logistic Regression Algorithm Machine Learning. 

In this video, we will cover the following terminology- 

1) Logistic Regression: It is a supervised classification algorithm. it is one of the most popular ML algorithms, which is used to predict the categorical dependent variable using a given set of independent variables or predict the probability of a target variable (predicts the output of a categorical dependent variable). 

2) Sigmoid function: It is a mathematical function having a characteristic "S-shaped" or sigmoid curve. 

3) Mathematical imputation- We will also see mathematical concepts behind the classification kind of problem. 

4) Cost function: The cost function for logistic regression is proportional to the inverse of the likelihood of parameters. Hence, we can obtain an expression for the cost function. 

5) Threshold: it is choosing to turn a projected probability or scores into a class label. 

Logistic Regression: https://www.geeksforgeeks.org/understanding-logistic-regression/