Linear Regression With One Variable

Introduction of ML

Machine Learning :  A computer program is said to learn from experience E with respect to some task T and some performance measure P.  If its performance on T as measured by P improves with experience E.

ML Algorithms :
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
4. Recommender System


Support Vector machine (SVM) : allows computer to deal with infinite number of features
Regression : Mean that our goal is to predict continuous value output
Classification : Where goal is to predict discrete value output.

Supervised Learning : Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. Y=f(X)

Unsupervised learning :Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.