In this blog, we will walk through Supervised and Unsupervised Learning and answer these questions:
Like it or not, most of us have a boss, and thus, we work under supervision. Our boss's job is to make sure we stay focused and complete our work. We have quotas to fulfill and projects to complete. They know what the desired and expected outcomes are, the same way data scientists understand the result they are trying to produce with supervised learning.
Supervised learning using Python
This blog is the third one of the series on learning Machine Learning using Python. In the first one, DataScience & Machine Learning: Where to start with Python, we covered setting up Python and installing the relevant libraries. In the second one Looking further into Machine Learning using Python, we covered different machine learning techniques and became familiar with supervised learning. We also talked about the scikit-learn toolkit and saw the SVM approach used due to its flexibility and usefulness.
Photo by Brooke Lark on Unsplash
Welcome back! Earlier, we had covered the basics of getting started with machine learning and Python. (Here is that blog if you missed that: DataScience & Machine Learning: Where to start with Python) The current blog will take the next step and introduce some ML (Machine Learning) concepts and algorithms.
Since we are going to use Python, we will stick to the sklearn Python library as our choice.