Artificial intelligence and machine learning have become popular areas of study in both the business and academic sectors. When it comes to creating thoughtful recommendations or predictions based on a vast quantity of data, machine learning is remarkably effective.
How and where can one begin to learn machine learning, if one wishes to become an expert? One must have the appropriate resources in addition to a strong interest in machine learning in order to learn about it.
Therefore, to assist you in getting started or even helping you get better if you are already familiar with it, we have put together and listed the Top 10 Free Machine Learning Online Courses and Tutorials from top researchers in the area. I strongly advise learning the fundamentals of machine learning by reading What is Machine Learning? if you’re completely new to the field. then feel free to browse through these excellent machine learning courses and select the one that most interests you.
1. An Overview of Machine Learning and Neural Networks
The “inverted classroom” approach is used in this University of Toronto course taught by Geoffrey Hinton. Put simply, it implies that rather than being exposed to the subject matter in a big lecture hall, students can watch the lecture as a series of three short films at home prior to the start of class, and then they can debate the videos in class.
Link: http://www.cs.toronto.edu/~tijmen/csc321/
2. A Synopsis of Machine Learning
In 2015, Apple’s Director of AI research Russlan Salakhutdinov presented this course at the University of Toronto. A portion of the theory and methods pertaining to the statistical aspects of machine learning are covered in this course. Among the numerous subjects that are discussed, some of the key ones are as follows:
Linear methods for regression
Linear models for classification
Regularization methods
Neural Networks
Link: http://www.cs.toronto.edu/~rsalakhu/CSC411/4
3. Machine Learning and Pattern Recognition
This course is by Yann LeCun, who was the director of AI Research in Facebook 2010. The prerequisites are Linear Algebra, vector calculus, elementary statistics, and probability theory.
Read Also: How to Open WebP Images on Windows 11
The course not only provides an individual with a wide variety of topics related to pattern recognition, machine learning, statistical modelling but it also covers the mathematical methods and theoretical features, still mainly focusing essentially on practical and algorithmic topics.
Link: https://cs.nyu.edu/~yann/2010f-G22-2565-001/index.html
4. AI
This is a Kilian Weinberger course. Giving the student a fundamental overview of machine learning is one of the course’s main objectives. Additionally, it teaches students the fundamental abilities needed to write their own learning algorithms and select the best learning algorithms for various kinds of situations, followed by debugging.
Link: https://courses.cis.cornell.edu/cs4780/2017sp/
5. Machine Learning and Adaptive Intelligence
This course was taught at the University of Sheffield by Neil Lawrence, director of Machine learning at Amazon. The prerequisites required are linear algebra, probability, and calculus.
The goal of this course is to give students an overview of the essential technologies that underpin contemporary machine learning and artificial intelligence. More specifically, it will serve as a tool for introductory probability and statistical modeling, supervised and unsupervised learning for regression and classification, and data exploration.
Link: http://inverseprobability.com/mlai2015/
6. Overview of Machine Learning and Neural Networks
Roger Grosee instructed this course at the University of Toronto in 2017. Calculus, probability, and linear algebra are prerequisites.
The main goal of this course is to provide students with an overview of machine learning and neural networks, covering both the fundamentals and the most recent developments in the fields. It serves as a tool for supplying information on ideas like probability and algorithms.
Link: http://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/
7. Machine Learning (University of Standford)
Andrew used Coursera to deliver this course at Stanford University.
During this course, students will learn about the several machine learning approaches that are most effective and put them into practice to get experience. In addition to studying about the academic aspects of education, students will acquire vital practical knowledge during the course and be able to effectively apply these strategies to brand-new challenges.
Additionally, one will also learn about some of the best practices of the Silicon Valley when it comes to the invention in machine learning and AI. Thus, this course very efficiently provides a wide introduction to data mining, statistical pattern recognition, and machine learning.
Link: https://www.coursera.org/learn/machine-learning
8. Tom Mitchell and Maria-Florina Balcan on machine learning
Tom Mitchell and Maria-Florina Balcan instructed this course at Carnegie Mellon University in 2015. This course requires prior knowledge of probability, statistics, linear algebra, and algorithms.
The primary goal of this course is to give graduate-level students a comprehensive understanding of the technology, mathematics, and algorithms—as well as the methodologies—that are now required by those working in machine learning research.
Link: http://www.cs.cmu.edu/~ninamf/courses/601sp15/
9. Machine Learning-Michael Littman, Charles Isbell and Pushkar Kolhe
This course was taught at Georgia Institute of technology by Michael Littman, Charles Isbell and Pushkar Kolhe in 2017. It requires a strong familiarity with probability theory, Linear algebra, and statistics.
The course is divided into two parts. The initial part of the is aimed to cover all aspects about Supervised Learning – a machine learning task that makes it possible for phones today to recognize your voice, your email to filter spam, and a lot more.
In part two, you will learn about Unsupervised Learning.
Link: https://in.udacity.com/course/machine-learning–ud262
10. Sargir Srihari’s Introduction to Machine Learning
Sargir Srihari instructed this course at the University of Buffalo in 2017.
The fundamental theory, concepts, and algorithms of machine learning are covered in this course. The techniques rely on probability and statistics, which are currently essential for creating artificial intelligence-displaying systems.
Link: http://www.cedar.buffalo.edu/~srihari/CSE574/
In conclusion, one of the hottest subjects in the information technology industry is machine learning. Nowadays, a lot of businesses are looking to hire people who are experts in machine learning as they prepare to roll it out on a larger scale. Should you wish to follow this area, taking one of these free courses would undoubtedly make a significant contribution to your resume. learn more
Discover more from ugamasontech
Subscribe to get the latest posts sent to your email.
