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 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.

Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

Machine learning is amazing… and intimidating. How can computers do magical things like understand images or text? This training for programmers will dispel the magic and help you to build your own computer vision program, starting from scratch.

If you don't know the question, you probably won't get the answer right. This course is all about asking the right machine learning questions, modeling real-world situations as one of several well understood machine learning problems.

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

Learn how to tune your model's hyperparameters to get the best predictive results.

Learn what Artificial Intelligence (AI) is by understanding its applications and key concepts including machine learning, deep learning and neural networks.