- Introduction to Pandas for Data processing and Analysis Notebook
- Combining datasets, Group by and Pivoting operations Notebook
- Working with time series data Notebook
- ML pipeline with scklearn Notebook
- 10-708 (CMU) Probabilistic Graphical Models, Kayhan Batmanghelich
- CSC2541: Scalable and Flexible Models of UncertaintyRoger Grosse, University of Toronto, Fall 2017.
- CS 228: Probabilistic Graphical Models, Stefano Ermon, Stanford University
- Statistical Rethinking course winter 2022
- NYU-DLSP21
- DS-GA 1008 Deep learning
- Deep Learning: Do-It-Yourself
- DEEP LEARNING COURSE OF LAKE GENEVA François Fleuret. T
- CSC 321 Winter 2017: Intro to Neural Networks and Machine Learning, Roger Grosse, University of Toronto, Fall 2017.
- Introduction to Machine Learning, Matt Gormley Carnegie Mellon University.
- 6.S191: Introduction to Deep LearninNick Locascio MIT.
- Deep Learning course Olivier Grisel and Charles Ollion
- Deep Learning Institute.
- Machine Learning: 2014-2015 Nando de Freitas University of Oxford.
- CS231n: Convolutional Neural Networks for Visual RecognitionFei-Fei-Li, Justin Johnson and Serena YeungStanford University, Spring 2017.
- Practical Deep Learning for Coders, Jeremy Howard,Fast.ai
- Cutting Edge Deep Learning For Coders Jeremy Howard, Fast.ai
- Computer Vision
- CS231A: Computer Vision, From 3D Reconstruction to Recognition Winter 2022
- Deep Implicit Layers - Neural ODEs, Deep Equilibirum Models, and Beyond
- An Introduction to Deep Generative Modeling: Examples
- Computer vision NYU
- EECS 498.008 / 598.008 Deep Learning for Computer Vision Winter 2022
- Spinning Up in Deep RL
- CS 294: Deep Reinforcement Learning, Fall 2017, Sergey Levine.
- Deep RL Bootcamp 26-27 August 2017 - Berkeley CA
- Full Stack Deep Learning
- Berkeley Spring 2021 - Full Stack Deep Learning
- CS 329S: Machine Learning Systems Design
- MLOps
- Reproducible Deep Learning
- MLOps-Basics
- A collection of various deep learning architectures, models, and tips
- CS 159: Data-Driven Algorithm Design Spring 2020
- Integrating optimization, constraints, and control within deep learning models
- Deep Implicit Layers - Neural ODEs, Deep Equilibirum Models, and Beyond
- An Introduction to Deep Generative Modeling: Examples
- Process Dynamics and Control
- Optimization Techniques (Doctorate Program in Power Systems or in Engineering Systems Modeling, year 2021-22)
- Bayesian Modeling and Computation in Python
- Intro to Probability for Data Scienc
- Computer Vision: Algorithms and Applications, 2nd ed
- Probabilistic Machine Learning" - a book series by Kevin Murphy
- Model-based machine learning
- PATTERNS, PREDICTIONS, AND ACTIONS
- Statistics and Machine Learning in Python
- Scientific Visualization: Python + Matplotlib
- Forecasting: Principles and Practice
- Time Series Analysis With R