Resources

• Stanford CS229M: Machine Learning Theory
• Introduction to Machine Learning
• Probabilistic Graphical Models
• Deep Multi-Task & Meta Learning - of-the-art multi-task learning and meta-learning algorithms in this graduate-level Stanford course, with a focus on coding problems and a course project. |
• Deep Multi-Task & Meta Learning I - of-the-art multi-task learning and meta-learning algorithms in this graduate-level course, preparing you for research in deep learning. |
• Convex Optimization I
• CS224W: Machine Learning with Graphs - of-the-art graph machine learning techniques, including graph neural networks, graph embedding, and graph algorithms, with hands-on experience on real-world datasets. |
• Reinforcement Learning
• Introduction to Machine Learning - on projects and experienced faculty. Ideal for students interested in data science and AI. |
• Scalable Machine Learning - scale data analysis and internet applications, covering systems, statistics, algorithms, and more. |
• Machine Learning - world applications. |
• Data Science Foundations - world relevance. |
• Data Science
• Data Computing Concepts
• Analyzing Big Data with Twitter - on experience in leveraging Twitter data to uncover insights and trends. Explore data analysis techniques, collaborate on real-world projects, and develop valuable data science skills. |
• Deep Reinforcement Learning - on experience and expert guidance from UC Berkeley's CS 285 course. |
• Machine Learning
• Introduction to Machine Learning
• Reinforcement Learning
• Introduction to Reinforcement Learning - on exercises using OpenAI Gym. |
• Advanced Deep Learning
• Reinforcement Learning
• Pattern Recognition & Machine Learning - world applications. Ideal for data science and AI enthusiasts. |
• Information Geometry & Applications
• Machine Learning Part 1a
• Machine Learning Course - Champaign. |
• Information Theory, Pattern Recognition & Neural Networks - world examples. |
• Probabilistic Models
• Large Scale Machine Learning - level course covering advanced machine learning techniques, including Bayesian methods, graphical models, and sequential data modeling. Hands-on experience with real-world datasets and programming assignments. |
• Statistical Inference in Big Data - on demonstrations for practical learning. |
• Machine Learning
• Probabilistic Modeling - making. Taught by experienced faculty at the University of Utah. |
• Clustering - on exercises and real-world projects included. |
• Machine Learning - on exercises, real-world case studies, and practical applications. |
• Machine Learning
• Classification
• Reinforcement Learning - making problems. Covers core principles, algorithms, and real-world applications. |
• Machine Learning Algorithms
• Bandits and Online Learning - armed bandits and online learning, taught by expert Sanjay Shakkottai. Gain hands-on experience with implementing and analyzing cutting-edge algorithms. |
• Introduction to Machine Learning - on exercises and real-world applications. |
• Machine Learning - on experience with real-world datasets and projects, taught by an expert in the field, Bert Huang. |
• Introduction to Machine Learning for Coders - world problems. |
• Mediterranean Machine Learning Summer School 2023 - on workshops, expert instruction, and networking opportunities in a scenic Mediterranean location. |
• Microsoft Research
• Introduction to Machine Learning
• Machine Learning Crash Course 2015
• Machine Learning and Adaptive Intelligence
• Advanced Introduction to Machine Learning - on experience with real-world projects, and exposure to the latest advancements in deep learning and reinforcement learning. |
• Machine Learning for Engineers 2022 - world engineering problems with this comprehensive course covering theory, applications, and hands-on projects. |
• Introduction to Pattern Recognition & Machine Learning - world applications. Hands-on experience with Python and popular libraries. |
• Regularization Methods for Machine Learning 2016 - dimensional learning problems. Suitable for those interested in the latest developments in machine learning and its practical applications. |
• ACP Summer School 2023 - on workshops, networking, and exposure to cutting-edge research. |
• Reinforcement Learning - edge algorithms, and real-world applications in robotics, game AI, and decision-making. |
• Deep Reinforcement Learning
• Reinforcement Learning - world applications in AI, robotics, and more. |
• Deep Reinforcement Learning - on assignments and projects to apply the concepts. |
• Deep Reinforcement Learning Bootcamp - on demos and code examples. |
• CMU Advanced NLP 2021 - depth insights into the latest advancements in natural language processing. |
• Multilingual NLP - lingual information retrieval, and text generation. |
• Natural Language Processing
• Natural Language Processing - to-sequence learning, and more. Hands-on projects and assignments. |
• Natural Language Processing - on exercises and industry-relevant skills. |
• Natural Language Understanding
• Natural Language Understanding
• Natural Language Processing - world applications. Hands-on experience, expert instruction, and collaborative learning environment. |
• Recent Advances on Foundation Models - level course at the University of Waterloo. |
• Advanced Robotics - edge robotics techniques and applications in this in-depth course taught by renowned expert Pieter Abbeel at UC Berkeley. |
• Speech Processing - on projects for practical experience. |