Supervised Learning

Step 1 of learning MACHINE LEARNING

As opposed to unsupervised learning stands supervised learning.

Simple definition is type of learning where you know the objective of exercise and also the required output variable.

As the above photograph explains supervised learning knows reds and blues and then try to identify the difference in them. While in unsupervised all the input data is unlabelled and grey, but we still try to separate the observations based on input data.

In supervised learning, the question is to find a mapping between input and output data.

Types of supervised learning Classification and Regression.

Few Classification Techniques are

• Linear Classifiers ( Logistic regression, Naive Bayes classifier, Fisher’s Linear discriminant)
• Support Vector Machines(Least Square support vector machines)
• Quadratic calsifiers
• Kernel estimation(K nearest neighbour)
• Decision Trees(Random Forest)
• Neural Networks
• Learning Vector quantisation

Few Regression Techniques are

• Linear Regression
• Ridge Regression
• Lasso Regression
• Bayesian Linear Regression
• Polynomial Regression

I am trying to combine simple ideas to understand the above terms and techniques!!

If you are a leaner of ML too, then considering following me on Medium and Instagram too.

See you soon