Scikit Learn 강좌

Scikit-learn—or skilearn—is a very useful library of algorithms in Python for machine learning. It started out as a Google summer of code project in 2007 then was further developed by a group of data scientists from the French Institute for Research in Computer Science and Automation (FIRCA) and released to the public in 2010. The scikit-learn library lives at a github.com URL and is now a community effort that anyone with qualified skills can contribute to. While the library is primarily written in Python, it's also built on Python-based libraries that include NumPy, Matplotlib, pandas, and SciPy. It gives users tools for statistical modeling and machine learning, such as classification, clustering, regression, model selection, preprocessing, and dimensionality reduction.‎

When you learn scikit-learn, you put yourself in a position to be able to contribute to and help maintain the scikit-learn library. Top contributors include data scientists, software developers, machine learning researchers, research scientists, and open-source developers. Outside of contributing to the library, individuals in these career fields and others related to machine learning are better equipped to perform their job duties when they've learned how to utilize the algorithms in scikit-learn.‎

Before starting to learn scikit-learn, you should have experience in and a sound understanding of Python. It is also often required to have experience in additional Python libraries, including NumPy, Scipy, Joblib, Matplotlib, and pandas.‎

Taking online courses on Coursera can help you learn about Python and machine learning as well as the specifics of using and creating algorithms for scikit-learn. You might learn how to build univariate and multivariate linear regression models using scikit-learn, use pandas to manage data, and perform exploratory data analysis and data visualization with seaborn, a Python library based on Matplotlib. With Coursera's online courses, you may also have the opportunity to make predictions in specific topics like electricity consumption, sentiment analysis, and career longevity for NBA rookies, for example.‎