Introducing Machine Learning for Scientists & Engineers

Artificial Intelligence (AI) is everywhere these days — in the news, in discussions around the water cooler, in people's hopes and fears, and increasingly in the tools you use day to day.  If you'd like to cut through the hype, de-mystify the "magic" and start to understand and master one of the critical components of AI, the Machine Learning for Scientists & Engineers [dates here] is the course for you.


Machine Learning is a class of problems where a computer is programmed to make predictions and improve its performance with more example data.  In this course, you'll learn the details of how this works using Scikit-Learn (the standard library for Machine Learning in Python), focusing on regression and feature engineering with labeled data. Using plenty of live-coded examples and expanded exercises, we'll explore the details of how regression works and how to score model performance before moving on to feature engineering with univariate, bivariate, and multivariate analyses.  This will lead us to the special cases of Classification and Ranking.  Finally, as time allows, we'll give an introduction to unsupervised learning, where we'll learn to identify structure that exists in a data set without explicitly labeling it first.


By the end of this class, you'll have an understanding of the general landscape of  artificial intelligence and machine learning;  you'll be able to use Scikit-Learn to create and train models of varying complexity and evaluate their performance;  you'll have a solid understanding of how regression works.  Most of all, you'll have a solid foundation and starting point for future self-learning.


"I really enjoyed this class. The material was very practical with an emphasis on application. I look forward to using what I learned in my work and am already looking at what classes I can take next. "

- Engineer at Sandia National Labs


"As a machine learning novice, this course was really helpful in introducing the fundamental concepts of ML, then giving me the opportunity to apply them. I would highly recommend this course to any that want to dip their toe in the ML world!"

- Researcher at Sandia National Labs


"The organization of information and pace of the class was perfect. Plenty of hands on practical examples starting with fundamentals and building up into automated, robust solutions. "

- Mid-career Engineer


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