- Open ML Course
- ML from Scratch
- Stat 451: Intro to ML - Fall 2020 - Sebastian Raschka - Youtube Playlist
- CS231n CNNs for Visual Recognition - from Stanford
- PyTorch Lightning Masterclass
- PyTorch Lightning Tutorials
- NLP Course For You
- Putting ML in Production
- End to End ML - some classes are free
- Practical DL for Coders
- Bot Creation
- Deep Lizard - especially for RL
- ML & Privacy
- YLC course on DL
- Berkeley FSDL Course
- FSDL 2021 Course
- Learn DS with Jovian
- Causal Inference for the Brave & True
- Statistical Rethinking
- MIT 6.S191 Intro to Deep Learning
- Open Mined Course on Privacy
- Clean ML Course - Moussa's course on Udemy
- Jovian.AI classes - in particular, algorithms class
- Graph NN - on WanDB
- Made With Ml - Foundations & Applied
- Microsoft Learning Paths Also Azure ML: https://docs.microsoft.com/en-us/azure/machine-learning/ Python on Azure: https://docs.microsoft.com/en-us/learn/modules/explore-analyze-data-with-python/?WT.mc_id=cloudskillschallenge_8022ffc7-0f54-40ee-ab4e-3b0585a81b93
- Machine Learning System Design CS392S Stanford W21
- Applied ML CS5787 Cornell
- Stats 337
- Data Acquisition
- Walk with FastAI
- Graph Powered ML Slides: Workshop: https://github.com/joerg84/Graph_Powered_ML_Workshop
- Data Science with Big Datasets
- Stanford MLSys Seminars
- PyText: PyTorch NLP Framework
- CML: Implementing Continuous Machine Learning
- igel - similar to Uber's Ludwig
- STUMPY - time series analysis
- PyCaret - auto ML
- MLRose - Randomized optimization
- Trax - Sample NB
- Simple Transformers
- Bert or X as service
- BodyWork - ML Deployment service
- Neural Prophet - Forecasting Prophet on Steroids?
- Haystack - end-to-end framework for Question Answering & Neural search
- NBProcessing - package for data preprocessing: https://medium.com/swlh/the-ultimate-python-package-to-pre-process-data-for-machin-learning-c87bcc39fa66
- ZenML - MLOps Framework
- PySyft - Also other resources from OpenMined
- ExplainerDashboard - also https://explainerdashboard.readthedocs.io/en/latest/index.html & https://medium.com/analytics-vidhya/explainer-dashboard-build-interactive-dashboards-for-machine-learning-models-fda63e0eab9
- Click
- Scikit-Plot
- Optimization Tools in Python
- Surprise - Recsys library
- Cheat.sh
- Tmux on steroids
- TODS: Time Series Outlier Detection
- Luminaire: Anomaly detection from Zillow
- Fairlearn
- Alibi Explain
- InterpretML
- Operator - cmd line tool to create lambda functions in cloud
- modelstore Also https://operatorai.substack.com/p/releasing-modelstore-005-and-the
- Machine Learning Engineering Book by Burkov, 100 page ML
- Interpretable ML
- Clever Algorithms
- Feature Engineering
- ML_Story - A story about ML
- Python DS Handbook
- Practical MLOps - Noah Gift (Newer version of Pragmatic AI: https://learning.oreilly.com/library/view/pragmatic-ai-an/9780134863924/)
- Data Pipelines
- Intro to Adversarial Examples in DL
- PyTorch Forecasting
- NLP Exercises
- PyTorch Lightning
- LabML - interactive way to understand ML code
- Influx Data Blog - Time series DB
- Google Research 2020
- Made With Ml - Foundations & Applied
- AI Hub - Sample notebooks
- Kubernetes Training
- Deep Learning in Production
- Multi Arm Bandits
- Math behind A/B Testing
- SuperDuper NLP Repo
- Applied ML Masterlist
- Remote DS Jobs
- Training Data Guide
- Phone Price Example of end-to-end ML Deployment
- Categorical Transformations
- Graph Neural Networks
- Monte Carlo simulations
- Survival Analysis
- Predictive Power Score
- Best ML Tutorials (2018)
- I like Notebooks - Jeremy Howard
- BogoToBogo - Tutorials - on video streaming, ffmpeg etc.
- Lime
- NLP Pandect
- WTF Python - Amazing Resource on Python
- OOP Python - From O'Reilly Tutorial
- Pandas things
- ML Monitoring
- VSCode Day 2021
- Finetuning Transformers
- 100 days of ML
- Python Algorithms
- Python Patterns