This session will cover the basics of Scikit-Learn, a popular package containing a collection of tools for machine learning written in Python. See more at http://scikit-learn.org.
Main Goal: To introduce the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package.
- Definition of machine learning
- Data representation in scikit-learn
- Introduction to the Scikit-learn API
Scikit-Learn is a Python package designed to give access to well-known machine learning algorithms within Python code, through a clean, well-thought-out API. It has been built by hundreds of contributors from around the world, and is used across industry and academia.
Scikit-Learn is built upon Python’s NumPy (Numerical Python) and SciPy (Scientific Python) libraries, which enable efficient in-core numerical and scientific computation within Python.
What is Machine Learning?
In this section we will begin to explore the basic principles of machine learning. Machine Learning is about building programs with tunable parameters (typically an array of floating point values) that are adjusted automatically so as to improve their behavior by adapting to previously seen data.
Machine Learning can be considered a subfield of Artificial Intelligence since those algorithms can be seen as building blocks to make computers learn to behave more intelligently by somehow generalizing rather that just storing and retrieving data items like a database system would do.