Regular fees: 3600 USD
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.
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. This program uses Python and the NumPy library for code exercises and projects. Projects will be completed using Jupyter Notebooks.
This certificate program includes two self-paced lessons covering the linear algebra computations used in the Machine Learning curriculum. You may refer to these lessons at any time before or during your Machine Learning program.
Check your readiness with this free pretest now.
- Problem-Solving with Machine Learning
- Estimating Probability Distributions
- Learning with Linear Classifiers
- Decision Trees and Model Selection
- Debugging and Improving Machine Learning Models
- Learning with Kernel Machines
- Deep Learning and Neural Networks
KEY COURSE TAKEAWAYS
- Gain the foundational linear algebra skills needed for Machine Learning
- Redefine tasks as machine learning problems using fitting concepts and terminology
- Match the assumptions algorithms make with the properties of your data
- Create a simple image based face recognition system
- Estimate probability distributions from data and build a name classifier
- Implement an email spam classifier filter with convex optimization
- Perform model selection to find the best algorithmic setting for a given problem
- Implement a machine learning setup from start to finish
- Debug machine learning algorithms in a principled manner utilizing the bias variance trade-off
- Convert linear classifiers into non-linear classifiers to learn from complex datasets
- Construct and train deep neural networks for various data modalities, in particular images and text
WHAT YOU'LL EARN
- Machine Learning Certificate from Cornell Computing and Information Science
- 126 Professional Development Hours (12.6 CEUs)