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awesome-full-stack-machine-learning-courses
Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
https://github.com/leehanchung/awesome-full-stack-machine-learning-courses
- MIT: The Missing Sememster of Your CS Education
- edX Harvard: CS50x: Introduction to Computer Science
- MIT 18.05: Introduction to Probability and Statistics
- Columbia COMS W4995: Applied Machine Learning
- MIT 18.06: Linear Algebra
- Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks
- Berkeley: Full Stack Deep Learning
- Grokking Algorithms
- Google Python Style Guide
- Python Design Patterns
- Python3 Patterns
- Design Patterns: Elements of Reusable Object-Oriented Software 1st Edition
- MIT: The Missing Sememster of Your CS Education
- edX MITX: Introduction to Computer Science and Programming Using Python
- edX Harvard: CS50x: Introduction to Computer Science
- SQL for Data Analysis
- PostgreSQL Exercises
- U Waterloo: CS794: Optimization for Data Science
- Berkeley CS 170: Efficient Algorithms and Intractable Problems
- Berkeley CS 294-165: Sketching Algorithms
- MIT 6.824: Distributed Systems - WkMbsvGQk9_UB)
- NIST Engineering Statistics Handbook
- MIT 18.05: Introduction to Probability and Statistics
- MIT 18.06: Linear Algebra
- Stanford Stats216: Statiscal Learning
- CalTech: Learning From Data
- A Students Guide to Bayesian Statistics
- Introduction to Linear Algebra for Applied Machine Learning with Python
- Artificial Intelligence: A Modern Approach
- Berkeley CS188: Artificial Intelligence
- edX ColumbiaX: Artificial Intelligence - 101x-AI)]
- Mathematics for Machine Learning
- Concise Machine Learning
- The Elements of Statistical Learning
- Mining of Massive Datasets
- Pattern Recognition and Machine Learning
- Columbia COMS W4995: Applied Machine Learning
- Stanford CS229: Machine Learning
- Harvard CS 109A Data Science
- edX ColumbiaX: Machine Learning
- Berkeley CS294: Fairness in Machine Learning
- Google: Machine Learning Crash Course
- Google: AI Education
- Google: Applied Machine Learning Intensive
- Cornell Tech CS5785: Applied Machine Learning
- Probabilistic Machine Learning (Summer 2020)
- AutoML - Automated Machine Learning
- MIT: Data Centric AI
- Machine Learning Engineering
- Machine Learning System Design
- Microsoft Commercial Software Engineering ML Fundamentals
- Google Rules of ML
- The Twelve Factors App
- Feature Engineering and Selection: A Practical Approach for Predictive Models
- Continuous Delivery for Machine Learning
- Berkeley: Full Stack Deep Learning
- Stanford: CS 329S: Machine Learning Systems Design
- CMU: Machine Learning in Production
- Andrew Ng: Bridging AI's Proof-of-Concept to Production Gap
- Facebook Field Guide to Machine Learning
- Udemy: Deployment of Machine Learning Models
- Spark
- Udemy: The Complete Hands On Course To Master Apache Airflow
- Deep Learning
- Dive into Deep Learning
- The Matrix Calculus You Need For Deep Learning
- Berkeley CS 182: Designing, Visualizing and Understanding Deep Neural Networks
- Stanford CS 25: Transformers - 8-Y&list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM)
- Deeplearning.ai Deep Learning Specialization
- NYU: Deep Learning
- Mining of Massive Datasets
- Speech and Language Processing
- Dive into Deep Learning: Chapter 16 Recommender Systems
- Stanford CS246: Mining Massive Data Sets
- Introduction to Information Retrieval
- Stanford CS224U: Natural Language Understanding - NLU and Information Retrieval
- TU Wein: Crash Course IR - Fundamentals
- UIUC: Text Retrieval and Search Engines
- Stanford CS276: Information Retrieval and Web Search
- University of Freiburg: Information Retrieval
- Deep Learning
- Introduction to Natural Language Processing
- Speech and Language Processing
- Stanford CS224n: Natural Language Processing with Deep Learning
- Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks
- NYU: DS-GA 1011 Natural Language Processing with Representation Learnin
- Deeplearning.ai Natural Language Processing Specialization - nlp-specialization)]
- Deep Learning
- Stanford CS231n: Convolutional Neural Networks for Visual Recognition
- Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks
- Stanford CS236: Deep Generative Models
- Berkeley CS294-158: Deep Unsupervised Learning
- Stanford CS234: Large Language Models (Winter 2022)
- Stanford CS234: Advances in Foundation Models (Winter 2023)
- Reinforcement Learning
- Deep Learning
- Coursera: Reinforcement Learning Specialization
- Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks
- Stanford CS234: Reinforcement Learning
- Berkeley CS285: Deep Reinforcement Learning
- CS 330: Deep Multi-Task and Meta Learning
- Berekley: Deep Reinforcement Learning Bootcamp
- OpenAI Spinning Up
- Video 1 - FCNryj-GUn2a.21yA0Q1WPwhwZMgF?startTime=1610560965000) [Slides](https://drive.google.com/file/d/1KSFVptieJ-b115mLqAYfp2pVhJZ02qWh/view?usp=sharing)
- ColumbiaX: CSMM.103x Robotics
- CS 287: Advanced Robotics