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+# Introduction to Machine Learning
+Nowadays, humans can easily grab native things or learn new technologies but if we talk about machines then here a question arises that “Can machines also, learn from their past experience?”  
+So the answer is “YES”. Machine learning can adopt skills in the absence of human involvement and acquire knowledge. Machine learning is a technology that accredits computers to learn from past experience. Machine learning techniques can be used in various programs like speech recognitions, email filtering,  image recognition, recommender system, etc.  Machine learning has been categorized as a base class of artificial intelligence. 
+
+# Supervised and Unsupervised Learning
+These two techniques come under the category of machine learning. Both techniques help to deal with different datasets in different situations. 
+### 1. Supervised Learning
+* Supervised learning algorithms are trained using labeled data.
+* In supervised learning, input data is provided to the model along with the output.
+* A Supervised learning model predicts the output.
+* Supervised learning can be categorized into Classification and Regression problems.
+### 2. Unsupervised Learning
+* Unsupervised learning algorithms are trained using unlabeled data.
+* In unsupervised learning, only input data is provided to the model.
+* An Unsupervised learning model finds the hidden patterns in data.
+* Unsupervised Learning can be classified as Clustering and Associations problems.
+
+## Now let's understand What is Linear regression?
+Whenever we are dealing with prediction problems we prefer to use the Linear Regression algorithm. It makes predictions for continuous/real or numeric variables such as prices, sales, age, etc. Linear regression algorithm presents a linear relationship between a dependent and (Y) and one or more independent variables(X), hence called linear regression. 
+
+### There are two types of Linear regression: 
+1. Simple Linear Regression:
+If a single independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Simple Linear Regression.
+2. Multiple Linear regression:
+If more than one independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Multiple Linear Regression.
+