Machine learning is a fast growing field of computer science, and online courses are quickly becoming one of the best ways for beginners to study machine learning. However, online courses are not just for beginners; because the field is rapidly evolving, staying up to date can prove particularly challenging. With this in mind, many choose the structured format of online classes to learn new machine learning concepts and stay up to date.
There are many educational platforms hosting online machine learning courses including EdX, Udemy, and Udacity, but in this article I’ll be focusing on one of the most popular, Coursera. Each course hosted on Coursera is taught by a university instructor and their material is often recycled from their top-level courses. Some of the most popular machine learning courses come from such institutions as Stanford, IBM, the University of Michigan, and Google Cloud.
To help navigate the 882 machine learning courses available as of writing, I’ve listed the 5 best machine learning courses on Coursera. Each course targets a different aspect or application of machine learning. The selection of the top five courses was made by combining a range of data points, including the popularity of the course measured by how many students are enrolled, the value of the specific topics discussed, the prestige of the supporting institution, and reviews written by students who completed the course.
Please note that most courses on Coursera provide certificates of completion for a fee of around $49 per course. However, this is subject to change based on the specific topic and affiliated university. Unlimited access to courses is also available as part of Coursera Plus for an annual subscription fee. However, for those who do not necessarily want a certificate, all courses on the site may be audited free of charge.
Anyone who spends time on Coursera will recognize a common name on many of the most popular courses: Andrew Ng. Andrew Ng is the CEO and founder of Landing AI, and a professor at Stanford University. He is the creator of many courses on the platform, but his most popular is simply titled Machine Learning.
This course boasts over 3.7 million enrollees and covers 60 hours of machine learning basics. It’s an excellent choice for beginners looking to touch on a wide range of applications and professionals wanting to diversify their knowledge. Over the course of 11 weeks, learners will start with the basics of linear and logistic regressions, then apply these skills to neural networks and machine learning system design. The course also covers classification vectors and recommendation systems, and ends with a discussion of big data and ML.
The course has an overwhelmingly positive reception with a 92.5% five-star rating. Reviews suggest the course is all-inclusive, and helps beginner/intermediate level students learn a lot in a short period of time. Many commenters also compliment Andrew Ng for creating a fantastic course.
This is another Andrew Ng course, but you’ll have to dig deep into the Coursera search results to find it. In fact, only around 300,000 students have enrolled in the course. However, for those who already know the basics of machine learning, understanding how to develop a clear, defined project is a critical skill. Those already part of a data science team or currently in search of employment can leverage the skills learned in this course to optimize their projects.
This course is only two weeks and five hours in length, but learners will explore how to diagnose errors in a machine learning algorithm and be able to prioritize the next most important step to take when in the midst of a project. The course also introduces end-to-end learning, transfer learning, and multi-task machine learning. These skills are critical in working efficiently and developing the best possible ML algorithm.
When discussing the course, Andrew Ng writes, “I’ve seen teams waste months or years through not understanding the principles taught in this course.” Student reviews echo similar sentiments, with 14% of course completers reporting a pay increase or promotion as a result of taking this course.
For those looking for industry specific training in a short period of time, the Google Cloud machine learning courses on Coursera are an excellent option. The Machine Learning Fundamentals course is just two weeks long and includes 12 hours of content. This course is at the intermediate level and prerequisites include knowledge of SQL, data modeling, basic statistics, and Python programming.
The course uses the Google Cloud platform to teach the fundamentals of machine learning, including CloudSQL, BigQuery, Datalab, and TensorFlow. Since Google Cloud created the course themselves, those who complete the course will also be able to unlock access to the Google Cloud Professional Certificate as an added benefit. Certificate programs on Coursera require paid access, but many students in these programs report tangible career benefits. Those who complete the certificate, including all the graded projects, will have a solid foundation to immediately apply Google Cloud tools in their workflow.
IBM is a major player in the Coursera computer science course offerings and their Data Science Professional Certificate is one of the most popular certification programs on the platform. Machine Learning with Python is one of the nine courses included in the certificate and can be taken or audited independently of the program. The certificate program revolves around project development and this class is no different. Learners will work on their machine learning portfolio by creating a project using classifications, clustering and/or recommendation systems. While most machine learning courses on Coursera include project-based learning, the projects in the IBM courses are some of the most robust. Taking these courses will provide excellent end products to list on a resume or portfolio.
Newcomers and intermediate students alike will benefit from this accelerated, active learning course. Many students who completed the course reported a pay increase or promotion. 23% of learners also reported starting a brand new career after completion, making this class a good candidate for those pivoting into a ML career.
The first four recommendations in this list have been beginner or beginner-intermediate level courses as, overwhelmingly, most courses on Coursera fall under this category. However, more advanced learners looking for a deep dive into a specific topic can still benefit from Coursera.
This course has less than 2,000 students but a 4.5 rating, making it a niche but highly ranked course. It’s the culmination of a series of courses from IBM regarding the development of AI workflows. More specifically, it looks into computer vision and natural language processing workflows, helping learners design optimal methodologies from the ground up. The course goes into the technical details of creating evaluation metrics, designing neural networks for supervised learning, working with TensorFlow, and finally working with Watson NLU and Visual Recognition.
Coursera is an excellent resource for students looking to learn more about machine learning or pivot into a new career. Of the hundreds of machine learning courses on the site, these are consistently the top rated and most recommended. Some other honorable mentions include the rest of the IBM Machine Learning courses, any other courses produced by Andrew Ng, and the beginners Machine Learning specialization from the University of Washington.
For those looking to start a course on Coursera, it’s important to remember that all the courses are entirely self-directed. Learners should come with motivation to succeed and strong organizational skills to make the best of these technical courses. With the help of these courses, keeping up with new developments in data science should be a bit easier.