Data science has become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics will help students, recent graduates and young professionals learn classification analysis and its applications in business scenarios.

In this course you will learn

1. Machine learning and data science overview

2. Supervised, unsupervised and semi-supervised learning

3. The difference between supervised and unsupervised learning

4. Preparing and measuring data

5. Missing data imputation

6. Discriminant Analysis

7. Decision Tree

8. Logistic Regression

8. Naïve Bayes Classifier

9. k-Nearest Neighbor

10. Overview of R

So, what is supervised learning?

Let’s say I have labeled fruits and I kept them in separate baskets. So you have separate baskets for yellow banana, golden pineapple, black grapes and so on. Now if I give you a golden pineapple you know exactly what it is and in which basket you need to keep it. So, I am helping you classify fruits by previously labeled and classified fruits.

What essentially is happening here is helping you learn about fruits which are already labeled. You know the characteristics and labels based on which they are separated into different baskets. The labeled fruits help you train your brain about their respective correct baskets. Now, for each new fruit you can put them into its respective basket. When machines learn in this way this is called supervised learning. Supervised learning is a learning in which we teach or train the machine using data which are properly or rather correctly labeled.