As we all know that machine learning or artificial intelligence has increasingly gained more popularity in the past couple of years and still continues to do so. As at the very moment Big Data is the present trend in the tech industry, machine learning proves to be incredibly powerful when it comes to making predictions or calculated suggestions that are based on large amounts of data. The importance it carries along with the world of amazement it carries is well known and understood. But where individuals mostly lack or stop at is one main question:

So if an individual wants to learn more about machine learning, how do you start and from where?

In order to know about AI/Machine Learning, one not only needs keen interest but also the right resources that can provide the same.

In the same context, Given below is a list of Top 10 Free Machine Learning Online Courses and Tutorials by leading researchers in the field to help you with starting from scratch or even improving if already familiar. Most of the courses mentioned below are not only free but also self-paced without the need of any registration.

For more information related to each course, the link has been provided.

1. Introduction to Neural Networks and Machine Learning:

The teaching of this course is done making the use of the "inverted classroom" model. This in simple terms means that instead of being introduced to the related material in a large lecture hall that limits itself to one-way communication, one can first watch the lecture that has been recorded by Geoffrey Hinton as a set of about 3 short videos at home before the commencement of the class, and then in class, takes place a much more dynamic discussion about it.

If one is already registered for the class, you will be able to view all these videos on the Coursera website. Further details of how to do this will be given in the first lecture period.

Link: Click Here

2. Introduction to Machine Learning:

This specific course covers some parts of the theory as well as the methodology of statistical aspects of machine learning. The few among many topics that are covered include:

• Linear methods for regression

• Linear models for classification

• Regularization methods

• Neural Networks

Link: Click Here

3. Machine Learning and Pattern Recognition:

The course not only provides an individual with a wide variety of topics related to pattern recognition, machine learning, statistical modelling, as well as neural computation but it also covers the mathematical methods and theoretical aspects, still mainly focusing primarily on practical and algorithmic issues.

Link: Click Here

4. Machine Learning:

The ultimate goal of this course is to provide the individual with an introduction to the field of machine learning. The course will help in teaching basic skills to code up your own learning algorithm, to decide which learning algorithm is to be used for what problem and to evaluate as well as debug it.

Link: Click Here

5. Machine Learning and Adaptive Intelligence:

This course aims to provide an individual with an understanding of the fundamental technologies within which underlie the modern artificial intelligence. To be particular, it will act as an aid in providing a foundational understanding of probability and statistical modelling, supervised learning for classification and regression, as well as unsupervised learning for data exploration.

Link: Click Here

6. Introduction to Neural Networks and Machine Learning:

This course provides an individual with an overview of both the ideas that act foundational as well as the recent advances that have taken place in neural net algorithms.

Link: Click Here

7. Machine Learning:

In this class, an individual will learn about the machine learning techniques that are known to be most effective, and gain practice by their implementation and getting them to work for yourself. Above it all, you'll get to learn about not only about the theoretical side parts of learning, but also gain the practical knowledge that is required to very quickly as well as powerfully apply these techniques to problems that are new. Not ending here, you'll also learn about some of the best practices of the Silicon Valley when it comes to innovation in machine learning and AI. This course thus very efficiently provides a broad introduction to data mining, statistical pattern recognition and machine learning.

Link: Click Here

8. Machine Learning:

This course has been mainly designed for a student who is of graduate-level in order to get them with a thorough grounding in the technologies, mathematics and algorithms, methodologies, that are currently needed by people who are involved with research in machine learning.

Link: Click Here

9. Machine Learning:

The first part of the course covers all 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: Click Here

10. Introduction to Machine Learning:

This course covers the necessary theory, principles as well as algorithms related to machine learning. The methods are based on statistics and probability which have now become essential when it comes to designing systems that exhibit artificial intelligence.

Link: Click Here