Machine Learning¶
One of the hottest topics right now, covers foundational concepts related to the field of ML.
The first few topics are common to all aspects of machine learning.
References¶
- Machine Learning | Dr. Pranav | BITS Pilani Dubai Campus
- Modern Data Analysis for Economics
- Machine Learning From Data
- Machine Learning From Data | Prof Yaser Abu Mostafa | Caltech
- Machine Learning From Data | Rensselaer Prof. Malik Magdon-Ismail
- Machine Learning | Gary Holness | Clark University
- Machine Learning From Data | Uzma Mushtaque
- Machine Learning From Data | Course Handbook
- Quantum Machine Learning | University of Toronto
- Machine Learning | Stanford
-
Machine Learning| Andrew Ng Courseratoo introductory - Stanford CS229: Machine Learning | Andrew Ng | 2018
- Stanford CS229M: Machine Learning Theory
- Advanced Machine Learning | Sergey Plis | Georgia State University
- Online Machine Learning | IIT Bombay
- Machine Learning | mathematicalmonk
- MIT 9.520/6.860S - Statistical Learning Theory and Applications
- Statistical Learning with Python | Stanford Online
- Intro to Machine Learning and Statistical Pattern Classification | Sebastian Raschka
- Machine Learning | WQU Saul Leung
- Multimodal Machine Learning | CMU Fall 2022
- MLOps | Andrew Ng Coursera
- Machine Learning Yearning | Andrew Ng
- Advanced Machine Learning | Florian Marquardt
- Applied Machine Learning (Cornell Tech CS 5787, Fall 2020) | Volodymyr Kuleshov
- Machine Learning for Intelligent Systems | Kilian Weinberger | Cornell
- Applied Machine Learning | Andreas Mueller
- Probabilistic Machine Learning | Tübingen Machine Learning | Philipp Hennig
- Towards Bayesian Regression | Kapil Sachdeva
- Advanced Machine Learning 2020, CSE, IIT Kharagpur
- Introductory Applied Machine Learning | University of Edinburgh | Victor Lavrenko
- Gaussian Processes | Imperial College
- From Data to Decisions: Measurement, Uncertainty, Analysis and Modeling | Chris Mack | University of Texas
- Learning Theory | Se Young Yun
- Machine Learning Explainability | Stanford
- Machine Learning with Graphs | Stanford
- Fairness in Machine Learning | MIT
- MLOps | Pragmatic AI Labs
- Cluster Analysis | TU Dortmund
- Outlier Detection | TU Dortmund
- Evaluating Machine Learning Models and Their Diagnostic Value | Gael Varoquaux
- Machine learning in Python with scikit-learn | Gael Varoquaux | Inria
- Anomaly Detection | Aric LaBarr
- Bayesian Data Analysis
- Bayesian Evidential Learning
- Model Development | Dimitri Bianco
- Pitfalls
- Machine Learning for Engineers
- Machine Learning with R | Equitable Equations
- Modern Anomaly & Novelty Detection | LLMs Explained