Data science and Python 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 classification analysis and its applications in business scenarios using Python programming language.
In this course you will learn:
1. Supervised and unsupervised learning
2. The difference between supervised and unsupervised learning
3. Decision Tree With Python
4. Regression Tree With Python
5. Discriminant Analysis
6. Naïve Bayes Classifier
7. Support Vector Machines With Python
8. k-Nearest Neighbor
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.