Overview
Machine learning is emerging as today’s fastest-growing job as the role of automation and AI expands in every industry and function.
Cornell’s Machine Learning certificate program equips you to implement machine learning algorithms using Python. Using a combination of math and intuition, you will practice framing machine learning problems and construct a mental model to understand how data scientists approach these problems programmatically. Through investigation and implementation of k-nearest neighbors, naive Bayes, regression trees, and others, you’ll explore a variety of machine learning algorithms and practice selecting the best model, considering key principles of how to implement those models effectively. You will also have an opportunity to implement algorithms on live data while practicing debugging and improving models through approaches such as ensemble methods and support vector machines. Finally, the coursework will explore the inner workings of neural networks and how to construct and adapt neural networks for various types of data.
This program uses Python and the NumPy library for code exercises and projects. Projects will be completed using Jupyter Notebooks.
Machine learning is complex. While you do not need to have machine learning experience in order to take the program, we strongly recommend having prior experience in math, including familiarity with Python, probability theory, statistics, multivariate calculus and linear algebra.
Check your readiness with this free pretest now.
How It Works
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Key Course Takeaways
- Redefine problems using machine learning concepts and terminology
- Create a face recognition system using a simple algorithm
- Estimate probabilities distribution from data and implement the Naive Bayes algorithm to create a name classifier
- Apply convex optimization and implement a linear classifier to create an email spam filter
- Use effective hyperparameter search to select a well-suited machine learning model and implement a machine learning setup from start to finish
- Improve the prediction accuracy of an algorithm using bias variance trade-off
- Extend the applicability of linear classifiers to learn non-linear decision boundaries from more complex datasets
- Choose and train a neural network that achieves cutting-edge accuracy by incorporating appropriate assumptions about your data
Download a Brochure
Not ready to enroll but want to learn more? Download the certificate brochure to review program details.Watch the Video
“Completing a program from eCornell really has allowed me to think outside the box at work. It gave me the confidence I needed to take a seat at that table and say I am ready.”
“eCornell gave me the confidence I needed to take a seat at the table and say: I’m ready.”
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Machine Learning
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