awesome-full-stack-machine-learning-courses
github.com/leehanchung/awesome-full-stack-machine-learning-courses ↗Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
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Case Studies
- AI Dungeon: How we scaled AI Dungeon 2 to support over 1,000,000 users
Infrastructure lessons from scaling a generative AI application.
- ByteDance: How TikTok Wins The Social Media Recommendation System War
Technical breakdown of TikTok's recommendation algorithm. (transcription)
- Google: Best Practices for Machine Learning
Rules and best practices for machine learning projects from Google.
- NerdWallet: How NerdWallet Dialed Machine Learning Up to 11
Case study on scaling ML infrastructure at fintech company.
- Spotify: The Winding Road to Better Machine Learning Infrastructure Through Tensorflow Extended and Kubeflow
Building ML infrastructure for music recommendation at Spotify.
- Trigo: How Trigo built a scalable AI development & deployment pipeline for Frictionless Retail
AI/ML pipeline development for computer vision retail applications.
Machine Learning Engineering
- Andrew Ng: Bridging AI's Proof-of-Concept to Production Gap
Practical insights on moving ML from research to production.
- Berkeley: Full Stack Deep Learning
End-to-end ML engineering from research to production. :star:
- CMU: Machine Learning in Production
ML system design with focus on quality and reliability.
- Continuous Delivery for Machine Learning
CI/CD practices for machine learning systems.
- Facebook Field Guide to Machine Learning
Machine learning practices from Facebook.
- Feature Engineering and Selection: A Practical Approach for Predictive Models
Feature engineering techniques and best practices.
Artificial Intelligence
- Artificial Intelligence: A Modern Approach
Comprehensive textbook on AI algorithms and techniques.
- Berkeley CS188: Artificial Intelligence
Foundational AI course covering search, planning, reasoning, and learning. :star:
- edX ColumbiaX: Artificial Intelligence
Core AI concepts and algorithms with programming projects. [
Math and Statistics
- A Students Guide to Bayesian Statistics
Introduction to Bayesian methods and probabilistic thinking.
- CalTech: Learning From Data
Theoretical foundations of machine learning and generalization.
- Introduction to Linear Algebra for Applied Machine Learning with Python
Practical linear algebra with Python applications.
- MIT 18.05: Introduction to Probability and Statistics
Essential probability and statistics for understanding machine learning. :star:
- MIT 18.06: Linear Algebra
Comprehensive linear algebra covering vectors, matrices, and eigenvalues. :star:
- NIST Engineering Statistics Handbook
Comprehensive reference on statistical methods and applications.
Machine Learning
- AutoML - Automated Machine Learning
Techniques for automating machine learning workflows.
- Berkeley CS294: Fairness in Machine Learning
Ethical considerations and fairness in machine learning systems.
- Columbia COMS W4995: Applied Machine Learning
Applied ML with hands-on projects and real-world problem solving.
- Concise Machine Learning
Concise overview of key machine learning concepts.
- Cornell Tech CS5785: Applied Machine Learning
Applied ML techniques with programming assignments.
- Cross-Industry Process for Data Mining methodology
Standard process for data mining and analytics projects.
Computer Science
- Berkeley CS 170: Efficient Algorithms and Intractable Problems
Study of algorithms, computational complexity, and NP-completeness.
- Berkeley CS 294-165: Sketching Algorithms
Algorithms for processing massive datasets efficiently.
- Design Patterns: Elements of Reusable Object-Oriented Software 1st Edition
Foundational book on software design patterns.
- edX Harvard: CS50x: Introduction to Computer Science
Comprehensive CS fundamentals from Harvard University.
- edX MITX: Introduction to Computer Science and Programming Using Python
Learn Python fundamentals through problem-solving and applications. :star:
- Google Python Style Guide
Industry standard Python code style and conventions.
Specializations
- Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural NetworksReinforcement Learning
Deep neural network fundamentals with RL applications.
- Berkeley CS285: Deep Reinforcement LearningReinforcement Learning
Deep RL covering policy gradients, Q-learning, and actor-critic methods. :star:
- Berkeley CS294-158: Deep Unsupervised LearningUnsupervised Learning and Generative Models
Deep learning methods for unsupervised learning tasks.
- Berkeley: Deep Reinforcement Learning BootcampReinforcement Learning
Intensive bootcamp on deep reinforcement learning fundamentals.
- ColumbiaX: CSMM.103x RoboticsRobotics
Robotics fundamentals covering kinematics, dynamics, and control.
- Coursera: Reinforcement Learning SpecializationReinforcement Learning
Comprehensive RL specialization recommended by Richard Sutton, the author of the foundational RL textbook. :star:
How to Use This List
- Course Name
Brief description of the course and what you will learn.
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