Machine learning and data science have 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 advanced exploratory analysis and its applications in business scenarios.

In this course you will learn:

1. Data science overview

2. Types of machine learning

3. Supervised and unsupervised machine learning and their differences

4. Application of supervised and unsupervised machine learning

5. Semi-supervised machine learning

6. understanding, measuring and preparing data for analysis

7. Cluster analysis

8. Features of cluster analysis

9. k-Means clustering

10. Hierarchical clustering

11. Hierarchical clustering case studies

12. Factor analysis

13. Overview of R Programming language

14. Data visualization in R

What is unsupervised learning?

Unsupervised learning is the learning of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.

We will take an example to understand the unsupervised learning process. Let’s say, you are traveling to Amazon. There are many animals, snakes, birds and insects that you have never ever seen in your life. Now, in there you see a new small bird that you have never seen before. No one tells you that it is a bird not a large size insect. You can still make out that it is a bird because it has feathers, it has beak, it can fly etc. No one has taught you about it by labeling it as a bird but you learn from unlabeled data. This is unsupervised learning. The phases of learning are pretty simple. You have input data, you have your algorithm that categorizes, and then you have the output.