Machine learning goes mainstream and overruns the digital world. ML techniques allow making our everyday life easier by the means of mathematical modeling. But how to make machine learning work for your business purposes? The best way is to start learning it. Check this article to find out the best machine learning online courses!


Machine learning is fairly considered one of the most high-demand specializations in the digital world. It provides the basis for building advanced chatbots, search engine improvement, and AI software tools development. 

The demand for machine learning goes together with the necessity to collaborate with experts who can adopt sophisticated ML algorithms for diverse business purposes. That is why machine learning courses and tutorials gain traction. The conventional thinking of the 21st century is that the best way to learn something is to learn it online. Thus, you can find a variety of machine learning online courses on the Internet that will teach you how to apply ML algorithms and use them in programming. 

What are the best machine learning certifications in 2021? Let’s find it out!

The Best Online Machine Learning Courses

We prioritized ML courses with a clear, coherent structure and a focus on open-source libraries and programming languages. Some of them are designed to teach you fundamentals and others will meet the needs of more advanced learners. Anyway, all of them are self-paced which means that you won’t suffer from procrastination, can study any time, having breaks when necessary, or spending any amount of time on them during a week.

1. Introduction to Machine Learning for Coders ( 


This great free ML course created by will be suitable for those who have basic programming skills in Python. It is underpinned by the Data Science program of the University of San Diego and comprises lectures held in a real classroom. The course focuses on providing essential knowledge on a topic, therefore, includes a lot of video lessons, notes, and tests. But it does not imply a certification. However, the main strength of this machine learning course is that it is practically applicable. So, in the end, you will be able to employ ML algorithms and models for the needs of cloud ecosystems, for example. 

The training is based on the open-source library. And for some learners, it can be a downside as is often considered not being in the same street with such ML libraries as PyTorch. Nonetheless, if you are interested in learning the basics to employ machine learning in practice, it is a good choice.

2. Machine Learning (Coursera) 

This is a gold standard among all other machine learning online courses. It was developed and produced by Andrew Ng, co-founder of Coursera and Google Brain, a Stanford University professor. The course is perfect for beginners as it teaches the fundamentals of machine learning through the use of Octave, an open-source programming language. In comparison with R or Python, Octave is easier to learn, so it is great for getting familiar with the basics. The course is to be covered within about 3 months. 

Every lesson is well-explained, but linear algebra knowledge will be essential to understand it. This course is a good start after which you can pass to a more complex theme such as deep learning. It is free to audit, but if you need a certificate, it will cost you $79.

3. Machine Learning Course A-Z: Hands-On Python & R In Data Science (Udemy) 

This course was produced by Forex Systems Expert and Data Scientist, Kirill Eremenko. And it is one of the most comprehensive ML courses by now. It focuses on teaching how to employ reinforcement learning, deep learning, or NLP and how to handle ML modeling for your projects. There is a particular model for every specific problem. And this course shows how to select them, building your intuition. It does not require any deep math or programming knowledge, basic linear algebra is enough to handle it. The course comprises 40 hours of video lessons, 19 articles as well as homework assignments and quizzes to check your understanding of the subject. 

The program is based on the Python and R languages, every lesson is coupled with practical examples and comprehensive guidelines on how to work with particular software or tools.

4. Machine Learning (EdX) 


This course is developed for advanced users and requires sufficient math expertise as well as programming skills. It teaches machine learning through the use of Octave and Python and also includes some assignments in both of these languages. The course touches upon the probabilistic perspective in machine learning that makes it different from all other courses on this list. 

It is supported by the Columbia University, free to audit, but the certificate will cost you $300. Most themes in the program are covered within courses for beginners, but the edX one allows investigating them in more detail through the use of sophisticated ML algorithms. That is why Probability, Linear Algebra, and Calculus are vital to delve into this program.  

5. Machine Learning with Python (Coursera) 

This is one more machine learning course for beginners, providing insight into the basic algorithms. This is fairly the easiest way to learn fundamentals, using Python. The course employs an interactive Jupyter notebook to help you study new algorithms and use them in practice. You do not need in-depth math knowledge to get started with this program. It was developed by IBM and you need to invest $39 per month to obtain a certificate. Plus, by the end of this course, you will need to complete a final project.

Every ML algorithm you learn throughout the program goes together with practical recommendations on where and how you should use it. The guidelines usually include insight into the nature of a particular algorithm, its pros and cons, and a brief overview of its practical implementation.

In Wrapping Up

Machine Learning opens up the world of opportunity in the digital age. Machine learning is widely applicable in different fields and has a great potential for practical implementation in various industries. For example, crypto trading bots are also based on deep learning methods and techniques. Plus, they are employed for creating advanced spam filters, fraud detection software, and various digital tools. Although studying it is no easy feat, modern online ML courses are designed to let everyone learn the basics. Even if you do not have a strong programming background, it is not a big deal to get started with it.  

Randa Khaled

Randa Khaled

Author Since: November 19, 2020

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