Beginner Audio Classification

Google’s AI Course for Beginners (in 10 minutes)!

Learn How I'd learn ML in 2025 (if I could start over)

Clear and simple explanations of machine learning algorithms. Understand the math and intuition behind ML with Josh Starmer.

Watch AI learn to play games and solve problems. Fun, visual approach to understanding AI and machine learning concepts.

Google's fast-paced, practical introduction to machine learning. A self-study guide for aspiring machine learning practitioners.

This course provides foundational principles of accessibility and inclusive design. It covers major disability types, assistive technologies, legal aspects, and the principles of universal design and accessible content creation.

Learn about AI applications across industries and the fundamental concepts of Machine Learning and Deep Learning. The course also covers the deployment of AI workloads in various environments, including on-premise, cloud, and hybrid models.

This course, part of the 'Advanced Machine Learning' specialization, delves into the practical aspects of machine learning competitions. It covers advanced feature engineering, ensembling methods, and other techniques used by top Kaggle competitors.

This course empowers legal professionals to use Generative AI for creating high-quality contracts quickly and accurately while ensuring compliance. It covers the fundamentals of legal drafting, practical AI applications, and advanced techniques to streamline workflows and reduce errors.

This course introduces the fundamental concepts of parallel programming using CUDA. Students will learn about thread management, memory types, and performance optimization techniques for solving complex problems on Nvidia hardware.

This course explores the fundamentals of Education Technology, including alternative and digital education, hybrid learning, and the core technologies driving Ed Tech such as AI, Data, and AR/VR.

A beginner-friendly course that introduces you to Git Hub Copilot, showing how to boost productivity, write smarter code, and integrate AI into your development workflow. You will learn to harness its capabilities for faster, error-free coding.

This Stanford University course teaches essential statistical thinking concepts for learning from data. Topics include descriptive statistics, sampling, probability, and regression.

A beginner-friendly project on creating a decision tree classifier using the R programming language.

This non-technical course equips you with the knowledge to ask the right questions about data and choose the right tools to read, interpret, and communicate data. You'll learn how to get from data to insights and how data drives decision-making.

This course provides a comprehensive introduction to Data Version Control (DVC) for managing and versioning machine learning data. Students will learn about the machine learning product lifecycle and the differences between data and code versioning.

The first course in the HarvardX Data Science Professional Certificate, it provides the foundational R programming skills necessary for data wrangling and exploration.

This MIT course develops a deep understanding of the principles of statistical inference, including estimation, hypothesis testing, and prediction, on firm mathematical grounds.

This course from MIT provides an introduction to computational thinking and data science. You will learn how to use computation to solve problems and explore data, which is a great foundation for machine learning.

This course from Harvard University introduces the field of Tiny Machine Learning (TinyML), which involves running machine learning models on low-power microcontrollers. It covers the fundamentals of deep learning, data collection, and model deployment on embedded devices, with a focus on applications like keyword spotting and image classification.