Abstract: scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This workshop will go beyond the basics and show how to effectively evaluate and tune algorithms. We will also discuss the most important machine learning algorithms that you're likely to see in practice, how and when to use them, and some details about how they work internally. The session will focus on linear models for classification and regression and tree-based models, including random forests.
● Language used will be Python. Please access the link provided for download option of Jupyter Notebooks on Anaconda.
● Training materials can be found at the Github repository link provided.
● Attendees can also follow the provided google drive link for content overview.
● This workshop assumes familiarity with Jupyter notebooks and basics of the following libraries:
● It also assumes familiarity with the basics of supervised learning, like training and test data and basics of model evaluation.
● You should have built a model with scikit-learn (or attend Introduction to Machine learning with scikit-learn) before
taking this workshop.
Bio: Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O'Reilly book """"Introduction to machine learning with Python"""". He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.