Learn how to forecast cryptocurrency portfolio performance using advanced quantitative methods. Apply machine learning techniques to model crypto market behavior, optimize portfolios, and predict future returns with Python.

Learn how to detect fraud using Python.

Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.

Machine learning is one of the hottest fields in the world today. This course will teach you how to get started solving business problems with AWS machine learning technologies.

Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.

MATLAB is a widely used programming language for statistical computing. This course will teach you the fundamentals of getting started with machine learning and data science within MATLAB.

Humans communicate with language, but computers communicate with data. Discover how to translate between the two in this course.

This course focuses on introducing the TensorFlow 2.0 framework - exploring the features and functionality that it offers for building and training neural networks. This course discusses how TensorFlow 2.0 differs from TensorFlow 1.x and how the use of the Keras high-level API and eager execution makes TensorFlow 2.0 a very easy to work with even for complex models.

Get productive with TensorFlow.js - Google's Machine Learning library for JavaScript. From pre-made off the shelf models to writing or training your own, learn how to create next gen web apps.

Learn how to cluster data using Python and Scikit-learn. Understand how unsupervised learning algorithms like KMeans, DBSCAN, and hierarchical clustering can be used to find patterns and group similar data points in a variety of datasets.

Learn how and when to tackle missing data with deletion, single imputation, linear interpolation, and multiple imputation techniques.

Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.