Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence. It involves simulating intelligent behavior in machines and enabling them to learn from data, reason, and make decisions. Machine Learning (ML) is a subfield of AI that focuses on developing algorithms and models that allow computers to analyze and interpret data, learn patterns and relationships from it, and make predictions or take actions based on those learned patterns. For example, an AI-powered virtual assistant like Siri or Google Assistant uses machine learning techniques to understand voice commands, analyze natural language, and provide relevant responses or perform actions based on user queries.
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Microsoft | Artificial Intelligence for Beginners - A Curriculum | ⭐ | URL | ||
| Microsoft | ML for Beginners - A Curriculum | ⭐ | URL | ||
| Stanford University | Stanford CS229 | Machine Learning | Spring 2022 | ⭐⭐ | Youtube |
| Stanford University | Stanford CS229 | Machine Learning Full Course taught by Andrew Ng | Autumn 2018 | ⭐⭐ | Youtube |
| Stanford University | Stanford CS221 | Artificial Intelligence: Principles and Techniques | Autumn 2021 | ⭐⭐ | Youtube |
| Stanford University | Stanford CS229M | Machine Learning Theory | Fall 2021 | ⭐⭐⭐ | Youtube |
| Stanford University | Stanford CS229 | Machine Learning Course | Summer 2019 | ⭐⭐ | Youtube |
| Stanford University | Stanford EE104 | Introduction to Machine Learning Full Course | N/A | ⭐⭐ | Youtube |
| MIT | 6.034 | Artificial Intelligence | Fall 2010 | ⭐⭐⭐ | Youtube |
| UC Berkeley | CS 188 | Introduction to Artificial Intelligence | Fal 2018 | ⭐ | Youtube |
| Carnegie Mellon University | CS/LTI 11-777 | Multimodal Machine Learning | ⭐⭐⭐ | Youtube | |
| Carnegie Mellon University | - | Introduction to Machine Learning | Spring 2019 | ⭐ | Website |
| Machine Learning Crash Course | URL | ||||
| Harvard | CS197 | AI Research Experiences | - | ⭐⭐⭐ | Course Website |
| The State of Competitive Machine Learning | - | ⭐⭐⭐ | Website | ||
| National University of Singapore | Uncertainty Modeling in AI | - | ⭐⭐ | Youtube | |
| Basics of Machine Learning | ⭐ | URL | |||
| Kaggle | Intro to AI Ethics | ⭐ | URL | ||
| - | ML Course by Yury Kashnitsky | ⭐ | URL | ||
| Class Central | Elements of AI | ⭐ | URL | ||
| Udacity | Intro to TensorFlow for Deep Learning | ⭐⭐ | URL | ||
| NYU | CSCI-UA.0473-001 | Introduction to Machine Learning | - | ⭐ | Website |
| - | - | Machine Learning Bookcamp by Alexey Grigorev | - | ⭐ | GitHub |
| University of Tübingen | - | Probabilistic ML by Prof. Dr. Philipp Hennig | 2023 | ⭐⭐ | Youtube |
| University of Tübingen | - | Statistical Machine Learning — Ulrike von Luxburg | 2020 | ⭐⭐ | Youtube |
| University of Tübingen | - | Mathematics for Machine Learning — Ulrike von Luxburg | 2020 | ⭐⭐ | Youtube |
| University of Tübingen | - | Neural Data Science — Philipp Berens | 2021 | ⭐⭐ | Youtube |
| University of Tübingen | - | Introduction to Machine Learning — Dmitry Kobak | 2020/21 | ⭐⭐ | Youtube |
| University of Tübingen | - | Data Compression With Deep Probabilistic Models | ⭐⭐ | Youtube | |
| MVA2021 | Kernel methods in machine learning | ⭐⭐⭐ | Youtube Website |