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free-machine-learning-resources

Machine Learning Free Resources | This repo collects 212 of free resources for Machine Learning. 🤖 Unlock the potential of intelligent systems with our Machine Learning Lab repository! Featuring a curated collection of free resources and an online Playground, this is your experimental ground for ...
https://github.com/getvmio/free-machine-learning-resources

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Resources
- Machine Learning Tutorials - learn and TensorFlow. Explore supervised, unsupervised, and neural network methods. |
- Data Science Tutorials - world problem-solving. Become a proficient data scientist. |
- Advanced Artificial Intelligence - edge topics in AI, including Watson, human computation, deep learning, and the future of self-driving cars. Ideal for students and professionals seeking the latest AI advancements. |
- Artificial Intelligence
- UvA Deep Learning Course
- Practical RL - on course on reinforcement learning, covering essential tricks and heuristics for solving real-world RL problems. |
- Deep Learning - on experience in PyTorch. Prepare to understand and extend the current literature on deep learning. |
- Probabilistic Graphical Models - solving. |
- Machine Learning
- Machine Learning: Intro to Statistical Learning - on practical sessions using Torch deep learning framework. |
- Introduction to Matrix Methods
- Deep Learning for Computer Vision & NLP - level research class from Columbia University. |
- Big Data Analytics - depth knowledge on analyzing Big Data, including storage, processing, analysis, visualization, and application. Ideal for graduate students interested in Big Data and data analysis. |
- Deep Learning - on course from NYU's renowned Data Science Center. Explore cutting-edge techniques in computer vision and natural language processing. |
- Convex Optimization
- Machine Learning for Data Science
- Machine Learning - on experience. |
- Advanced Robotics
- Convolutional Neural Networks for Visual Recognition
- Algorithms for Big Data
- Deep Learning for Natural Language Processing - edge research in deep learning for natural language processing (NLP). Implement, train, and invent your own neural network models for a variety of NLP tasks. |
- Introduction to Machine Learning
- Data Science - world data science challenges. |
- Machine Learning - on MATLAB implementation. |
- Tensorflow for Deep Learning Research
- Deep Learning for Natural Language Processing
- Learn ML Algorithms by coding: Decision Trees
- A Simple Content-Based Recommendation Engine in Python - based recommendation engine in Python using machine learning techniques. Covers implementation, pros and cons, and production deployment. |
- Developing a License Plate Recognition System with Machine Learning in Python - to-end solution development. |
- How to build a simple artificial neural network with Go
- Python Machine Learning Tutorials - on examples and practical applications. |
- Machine Learning Specialization
- Deep Learning Fundamentals
- Exploring Fairness in Machine Learning
- Deep Multi-Task and Meta Learning - depth understanding of state-of-the-art multi-task learning and meta-learning algorithms for few-shot learning, transfer learning, and lifelong learning. |
- MIT's Artificial Intelligence Course
- The Little Book of Deep Learning
- Reinforcement Learning: An Introduction - depth understanding for students and researchers in the field of machine learning. |
- Python Machine Learning Projects - world applications. |
- Practitioners guide to MLOps
- Machine Learning from Scratch - a comprehensive guide for beginners and experienced practitioners alike. |
- Free and Open Machine Learning - source machine learning with this comprehensive guide, covering key concepts, architecture, and FOSS tools for practical business applications. |
- Deep Learning for Coders with Fastai and PyTorch
- Approaching Almost Any Machine Learning Problem - solving approaches in machine learning, suitable for beginners and experienced practitioners. Covers a wide range of ML topics and techniques. |
- Algorithms for Reinforcement Learning - Carlo methods, and more. Suitable for researchers, students, and practitioners in AI, ML, and control engineering. |
- A Selective Overview of Deep Learning
- A First Encounter with Machine Learning
- A Comprehensive Guide to Machine Learning
- A Brief Introduction to Machine Learning for Engineers
- Data Mining Concepts and Techniques
- Machine Learning For Dummies, IBM Limited Edition
- Getting Started with Artificial Intelligence , 2nd Edition - world examples. |
- Graduate Artificial Intelligence - level artificial intelligence topics, including search, optimization, machine learning, and planning. Recommended for graduate students interested in a thorough introduction to AI. |
- Artificial Intelligence
- Artificial Intelligence
- Artificial Intelligence - on programming assignments and insights from experienced instructors. |
- Artificial Intelligence: Principles & Techniques - on projects and cutting-edge research opportunities. |
- Introduction to Artificial Intelligence
- Intro to Artificial Intelligence - on projects, and experienced instructors from Udacity. Ideal for students, professionals, and AI enthusiasts. |
- Applications of Artificial Intelligence - on projects and presentations for practical application. |
- Graduate Course in Artificial Intelligence - on experience with AI algorithms. |
- Introduction to Artificial Intelligence - on projects and exposure to cutting-edge AI research. |
- Machine Learning for Computer Vision
- Machine Learning for Computer Vision
- Advanced Computer Vision - on programming projects and cutting-edge research discussions. |
- Data Mining: Learning From Large Datasets - on experience with real-world datasets. |
- Statistical Aspects of Data Mining - on exercises and real-world datasets. Taught by experienced Google instructors. |
- Data Mining Course - world data analysis projects. |
- Data Mining
- Data Mining Course
- Deep Learning CMU - led video lectures from Carnegie Mellon University. Dive into neural networks, CNN, RNN, and more. |
- Deep Learning Systems - on implementation of key concepts. |
- Intermediate Deep Learning - on experience with cutting-edge models and applications. |
- Neural Networks for Machine Learning - on coding exercises. |
- Deep Learning Course - on implementation. Taught by experienced instructor from New York University. |
- Designing, Visualizing & Understanding Deep Neural Networks
- Deep Learning - on implementation experience. Taught by experienced instructors from Stanford University. |
- Full Stack DL Bootcamp 2019 - of-the-art models. Hands-on experience in building and deploying real-world deep learning applications. |
- Nvidia Machine Learning Class - world examples. |
- Statistical Learning in Practice - based models and SVMs. Hands-on exercises and expert instruction. |
- Reinforcement Learning Course - world applications. |
- Statistical Machine Learning
- Foundations of Machine Learning Boot Camp
- Foundations of Machine Learning
- Machine Learning & Data Mining - on programming assignments and real-world applications. |
- CS 156 - depth. |
- Foundations of Machine Learning - on exercises and projects to reinforce key concepts. |
- Introduction to Machine Learning & Pattern Recognition - on experience with real-world datasets and practical applications. |
- 10-601 Machine Learning
- Introduction to Machine Learning
- Introduction to Machine Learning - on experience, and exposure to cutting-edge research in this highly recommended course at Carnegie Mellon University. |
- Machine Learning with Large Datasets - based tools for handling large datasets. Ideal for data-driven decision making and complex problem-solving. |
- Statistical Machine Learning
- Advanced Introduction to Machine Learning - depth exploration of fundamental machine learning concepts and techniques, including deep learning, clustering, kernel machines, and graphical models. |
- Convex Optimization
- Convex Optimization: Fundamentals, Algorithms & Applications
- Optimization - order methods, Newton's method, duality, and advanced topics. Taught by experienced instructors at Carnegie Mellon University. |
- Advanced Optimization & Randomized Methods - scale data problems in machine learning and optimization. Gain a solid foundation for research in this cutting-edge field. |
- Deep Reinforcement Learning & Control - on experience and tackle complex decision-making problems. |
- Applied Machine Learning - on experience with popular libraries and real-world datasets. |
- Learning with Big Messy Data - on experience on real-world datasets. |
- Machine Learning Hardware & Systems - world ML deployment. |
- Introduction to Reinforcement Learning - making, with hands-on programming assignments. |
- Machine Learning - on experimentation. |
- Machine Learning
- Probabilistic Graphical Models
- Learning in Graphical Models
- Introduction to Machine Learning - world applications. Free online video lectures. |
- Stochastic Methods for Data Analysis, Inference & Optimization
- Pattern Recognition - on exercises. |
- Introduction to Machine Learning - on experience with real-world datasets, and a verified certificate upon completion. |
- Pattern Recognition and Application
- Introduction to Machine Learning
- Pattern Recognition - on experience with implementation. |
- Algorithms for Big Data - scale datasets, including MapReduce, streaming algorithms, and sketching techniques. |
- Reinforcement Learning Course
- Statistical Learning Theory
- EPFL CS 233
- Statistical Machine Learning
- Machine Learning
- Efficient Machine Learning
- MIT 6.036 - on programming assignments and accessible for students with computer science and mathematics background. |
- Algorithmic Aspects of Machine Learning - negative matrix factorization, tensor decompositions, and more in this MIT course. |
- Statistical Learning Theory & Applications
- Introduction to Data-Centric AI - world machine learning performance with this first-ever Data-Centric AI course from MIT. |
- Computational Thinking & Data Science - solving. Suitable for beginners and experienced learners. |
- Undergraduate Machine Learning at UBC 2012 - level machine learning course taught by renowned expert Nando de Freitas at the University of British Columbia in 2012. Covers fundamental concepts and techniques. |
- Machine Learning Course
- Probabilistic Graphical Models - world applications in machine learning, computer vision, and natural language processing. |
- Data Science for Dynamical Systems
- High Dimensional Analysis: Random Matrices and Machine Learning
- Markov Chains & Algorithmic Applications - world problem-solving. |
- Foundations of Reinforcement Learning - difference learning. |
- Intro to Machine Learning - on experience using Python. |
- Advanced Machine Learning
- PURDUE Machine Learning Summer School 2011 - on exercises and access to valuable resources. |
- Statistical Rethinking
- Data Science
- Python and Machine Learning
- Statistical Learning with Python - on Python implementations and emphasis on theory and practical applications. |
- Statistical Learning - depth 15-hour video course on machine learning techniques, taught by renowned Stanford professors. Gain theoretical and practical understanding of statistical learning. |
- Machine Learning
- 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. |
- 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. |
- 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. |
- Natural Language Processing
- 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. |
- 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. |
- Information Retrieval
- Data Mining - scale data using MapReduce and Spark. Gain hands-on experience in data science and big data analysis. |
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