This course highlights the transformative potential of AI in improving diagnostic accuracy and supporting better patient outcomes, especially in underserved populations.

A non-technical course that helps you understand machine learning and artificial intelligence. Topics discussed: the meaning behind common AI terminology, what AI realistically can and cannot do, how to spot opportunities to apply AI, what it feels like to build machine learning and data science projects, how to navigate ethical and societal issues surrounding AI

Discover the fundamentals of AI, dive into models like ChatGPT, and decode generative AI secrets to navigate the dynamic AI landscape.

This online program — developed and taught exclusively by a team of alumni and graduate students from Stanford, MIT, and other top universities — provides guidance on initiating AI projects, pursuing AI ventures, and preparing for college.

This series will lead you through the most popular undergraduate course at Harvard, where you’ll learn the common programming languages, then carries that foundation through CS50’s Introduction to Artificial Intelligence with Python. Through hands-on projects, you’ll gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence.

Make great data visualizations. A great way to see the power of coding!

This specialisation will help you understand the foundations of deep learning, understand how to build neural networks, and learn how to lead successful machine learning projects. It is a A foundational 5-course program that teaches you how to build, train, and apply deep neural networks using Python and TensorFlow.

Handle missing values, non-numeric values, data leakage, and more. This course builds on the courses 'Introduction to Machine Learning' and 'Pandas' by Kaggle.

In this course you use TensorFlow and Keras to build and train neural networks for structured data. The course builds on the course 'Intro to Machine Learning' by Kaggle.

In this course, you learn the core ideas in machine learning and build your first models.

This course includes 81 lectures that teach you everything you need to know about deep learning. The lectures are given by MIT professors or computer scientists affiliated with MIT

A comprehensive introduction to programming course, with lessons on arithmetic and variable, functions, datatypes, lists, and conditions. Get started with Python, if you have no coding experience.

This beginner-friendly specialization (3 courses) teaches you how to build and train machine learning models using Python, covering both supervised and unsupervised methods. You’ll also learn best practices, decision trees, neural networks, recommender systems, and reinforcement learning. Upon completion, you’ll be able to apply ML techniques in real problems and understand when to use each algorithm.

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

This program will give you comprehensive understanding of what this technology is , what it can and can not do and what the implications are for business and for society. It does not only cover the technology but also all the important ethical, legal and societal aspects of AI.

Solve short hands-on challenges to perfect your data manipulation skills. Introduction to Python is a prerequisite for this course.

A comprehensive beginning course for Python, the most important language for data science. Chapters covered: functions, booleans and conditions, loops and list comprehensions, lists, strings and dictionaries.

Learn how to use ChatGPT. Discover best practices for writing prompts and explore common business use cases for the powerful AI tool.