How to Get Started with Machine Learning in Scikit-Learn

Description
This video breaks down the process of getting started with machine learning into 4 key questions: what is the iris dataset? How can the iris dataset be loaded into scikit-learn? How do we describe a dataset using machine learning terminology? What are scikit-learn's four key requirements for working with data?

Kevin begins by discussing the famous iris dataset and its relevance to machine learning today. Samples of 3 different species of iris were taken and their measurements recorded. The relevance to machine learning comes from the fact that a process called linear discriminant analysis could be used to accurately distinguish these different species from one another using only the dataset. This represents a type of supervised learning as we learn the relationship between the data and the species of iris. Kevin suggests that this is an easy supervised learning task for machine learning.

Therefore, in the next step of the video Kevin shows how the iris dataset can be loaded into scikit-learn. This will allow you to use a machine learning model to accurately predict the species of each iris in sci-kit learn. This video will give you the tools to input the iris dataset into scikit-learn and then test out machine learning methods as practice.

This video will then give you some key machine learning terminology to make you more comfortable with scikit-learn and machine learning.

Kevin then sets out a series of requirements for working with data in scikit-learn. This will help you make sure that your data is suitable for scikit-learn.

This video is a great tool for anyone interested in machine learning and programming. This video will give you the skills required to use scikit-learn to practice your machine learning with the simple iris dataset.