Polish your programming skills by learning dynamic programming using Python and various dynamic programming patterns asked in interview questions.

Learn advanced concepts and practices in embedded systems design and development.

Learn about machine learning, the area of artificial intelligence (AI) that is concerned with computational artifacts that modify and improve performance through experience.

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

Master GANs across various levels, from foundational principles to advanced applications in computer vision, NLP, and audio processing.

Dive into generative adversarial networks (GANs) using PyTorch, focusing on practical applications and hands-on projects.

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.

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 use machine learning in Python in this introductory course on artificial intelligence.

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.

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.