Harvard University
Fundamentals of TinyML
4.567 ratingsFocusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
Exploring Fairness in Machine Learning for International Development
via MIT
Overview
In an effort to build the capacity of the students and faculty on the topics of bias and fairness in machine learning (ML) and appropriate use of ML, the MIT CITE team developed capacity-building activities and material. This material covers content through four modules that an be integrated into existing courses over a one to two week period.
Skills you'll practise
Advantages and disadvantages
Advantages
- The real MIT course: lecture notes, problem sets and exams, often with solutions.
- Free, with no sign-up or deadlines.
- Taught by Massachusetts Institute of Technology, one of the strongest names in its field.
- Completely free.
- Self-paced: start any time.
Disadvantages
- No certificate, no grading and no forum: you need to be self-driven.
- Many courses are recordings of past semesters and some have notes only, no videos.
- No certificate.
Some points apply to every course of this kind; see how we rank.
Free to learn
- Free: All the materials are free to use, with no sign-up. MIT OpenCourseWare gives no certificate.
Taught by
- Dr. Richard Fletcher
- Prof. Daniel Frey
- Dr. Mike Teodorescu
- Amit Gandhi
- Audace Nakeshimana
Massachusetts Institute of Technology is in Tier 1: world-leading universities and India's top institutes of our institution ranking (98/100). OpenCourseWare is the gold standard for free university material.
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