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
- Machine Learning | Stanford
-
Machine Learning| Andrew Ng Courseratoo introductory - Stanford CS229: Machine Learning | Andrew Ng | 2018
- Stanford CS229: Machine Learning | 2022
- Stanford CS229M: Machine Learning Theory
- MIT 6.S897 Machine Learning for Healthcare, Spring 2019
- 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
- Machine Learning | NUS School of Computing
- Statistical Learning with Python | Stanford Online
- Intro to Machine Learning and Statistical Pattern Classification
- Machine Learning | WQU Saul Leung
- Machine Learning With Large Datasets | CMU 10-605
- 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
- Spring 2019
- Spring 2020
- Probabilistic Machine Learning | Tübingen Machine Learning | Philipp Hennig
- 2021
- 2023
- Towards Bayesian Regression | Kapil Sachdeva
- Machine Learning Concepts (Simply Explained) | Pedram Jahangiry
- Machine Learning Codes and Concepts (2023) | Pedram Jahangiry
- Machine Learning in finance - 2021 | Pedram Jahangiry
- Business Analytics Using Data Mining (BADM) | Galit Shmueli
- Steve Brunton
- Data Driven Science & Engineering | Machine Learning, Dynamical Systems, and Control
- Machine Learning for Fluid Dynamics | Steve Brunton
- Quantitative Social Science Methods, I (Gov2001 at Harvard University)
- Machine Learning | WIT Solapur - Professional Learning Community
- The Foundations of Machine Learning | Ion Petre
- Advanced Machine Learning 2020, CSE, IIT Kharagpur
- Introductory Applied Machine Learning | University of Edinburgh | Victor Lavrenko
- Machine Learning | University of Utah
- Machine Learning | VU University Amsterdam
- CPSC 330: Applied Machine Learning | University of British Columbia
- CPSC 340: Machine Learning and Data Mining (2018) | University of British Columbia
- Machine Learning Techniques | IIT Madras
- Statistical Machine Learning | University of Guelph
- Dimensionality Reduction and Manifold Learning | University of Toronto
- Gaussian Processes
- Gaussian Processes | Imperial College
- From Data to Decisions: Measurement, Uncertainty, Analysis and Modeling | Chris Mack | University of Texas
- IE343 Statistical Learning | Se Young Yun
- Learning Theory | Se Young Yun
- Machine Learning Explainability | Stanford
- Machine Learning with Graphs | Stanford
- Intro to Machine Learning and Statistical Pattern Classification | Sebastian Raschka
- Fairness in Machine Learning | MIT