Today, machine learning is among the most fast paced and interesting fields of computer science. There is a long list of industries and applications that use machine learning to make them more intelligent and efficient. Spam filtering, search engines, chatbots, image and video manipulation, etc… are some real-life examples where machine learning models are used. And, if you want to take up some of the world’s best online courses for machine learning, then you could consider platforms such as Coursera, edX, Skillshare, Udacity for some of the best online courses and programs for machine learning.
Top 17 Best Online Machine Learning Courses
Let’s take a look at the very best online courses for machine learning you can take today and find the perfect one for your needs and skill level.
Machine Learning – Andrew Ng (Coursera)
First on my list of the best online course on machine learning is the course from Stanford University and is available on the Coursera platform.
The Machine Learning course is taught by Andrew Ng, Founder & CEO of Landing AI, Co-founder of Coursera, Adjunct Professor at Stanford University and former founding lead of Google Brain and Chief Scientist, Baidu. The course can be completed in 56 hours.
In the Machine Language class, you’ll learn the most effective techniques of machine learning and also how to implement these techniques to new problems practically. The course offers a broad introduction to the concepts of machine learning, statistical pattern recognition and data mining.
Machine Learning with Python – IBM (Coursera)
This is an intermediate-level course offered by IBM and can be completed in 15 hours. The course is taught by Sayeed Aghabozorgi, Ph.D., Sr. Data Scientist, IBM. Machine Learning with Python is available on the Coursera platform and covers machine learning basics using Python.
In the course, you will learn about the use of machine language and its application in the real world. You will also get an overview of other topics like supervised/unsupervised learning, machine learning algorithms and model evaluation. You also learn with real-life examples and see how machine learning affects society.
On completing the course, you get a certificate in machine learning that you can share offline, online, on social media and your LinkedIn profile along with an IBM digital badge. The course is available for a subscription of just $39 per month, which gives you access to the graded materials and the course certificate.
The Machine Learning with Python course is part of many other programs and the course can be applied to other Professional Certificate or Specialization programs. Completing the course will contribute towards learning of other programs such as IBM Artificial Intelligence and IBM Data Science.
Machine Learning Using SAS Viya – SAS (Coursera)
Machine Learning Using SAS Viya is an intermediate-level course, which is offered by SAS and is available on the Coursera platform—it can be completed in 22 hours. The course is taught by Jeff Thompson, Senior Analytical Training Consultant and Catherine Truxillo, Director, Analytical Education.
The course provides a theoretical basis for various techniques that are associated with supervised machine learning concepts. The course includes a business case study that makes use of exercises and demonstrations to reinforce concepts and how to use an analytical approach to solve business problems. The course makes use of Model Studio in SAS Viya to prepare, create, compare and deploy analytics models and the SAS applications enable you to learn machine learning without any coding or programming.
Advanced Machine Learning Specialization Certificate – HSE & Yandex (Coursera)
The course is an advanced-level Specialization available on the Coursera platform and is in English along with Spanish, Korean and English subtitles. The specialization is taught by several faculty members from HSE and Yandex.
The specialization provides an introduction to reinforcement learning, deep learning, computer vision, natural language understanding and Bayesian methods. The course includes experience sharing by top CERN scientists and practitioners of Kaggle machine learning. On completing the 7 courses in the Specialization, you will be able to understand the caveats of the real-world settings and data and apply machine learning methods to modern enterprise scenarios.
Mathematics for Machine Learning Specialization – Imperial College of London (Coursera)
This is a beginner-level Specialization offered by the Imperial College of London and is available on Coursera. The Specialization will take around 2 months to complete and is taught by faculty from the Imperial College and Dyson School of Design Engineering.
The Specialization aims at bringing you up to speed on mathematics and helps to build an intuitive understanding of the same and relate the maths to data science and machine learning. At the end of the Specialization, you will gain sufficient knowledge of maths to take up more advanced courses in machine learning. The Specialization comprises three courses.
Machine Learning for Analytics MasterTrack Certificate – University of Chicago (Coursera)
Offered by the University of Chicago, the Machine Learning for Analytics MasterTrack Certificate is an intermediate-level course. In the MasterTrack Certificate, parts of the Master’s programs have been split into various online modules that enable you to earn a high-quality career credential that is university issued with an extremely interactive format and affordable price.
The program offers a highly interactive and engaging experience, live instruction and real-world projects. On completion of the program, you will get a MasterTrack Certificate, which will count towards your master’s degree. However, to join this program, you need to have an undergrad education or some professional experience that is related to basic Python programming, linear algebra and statistics.
Master of Science of Machine Learning – Imperial College of London (Coursera)
The Master’s degree in Machine Language is offered by Imperial College of London, which is ranked among the top 10 universities in the world. The program provides in-depth learning of machine learning models and the skills and experience to apply them to real-world problems. The curriculum is designed to advance your career in engineering or data science. The program allows you to build a strong foundation in maths and statistics, helps to build your confidence in analytical skills and also acquire knowledge and expertise in implementing machine learning solutions using various tools.
The master’s degree is excellent for students beginning their career in data science or already working as business analysts, bioinformatics scientists, senior data analysts, statisticians, etc. However, to enter into this program, you must already have an undergraduate degree in a subject like math, computer science, economics, statistics or physics. The credentials and title for the degree program are approved and awarded by the Imperial College of London.
Principles of Machine Learning – Microsoft (edX)
Available on the edX platform, the Principles of Machine Learning is a 6-week intermediate-level course offered by Microsoft. The course is taught by Graeme Malcolm, Senior Content Developer, Microsoft, Cynthia Rudin, Associate Professor at MIT and Duke, and Steve Elston, Quantia Analytics, LLC. This is a free course, but you can get an instructor-signed course completion for $99, which you can add to your resume or CV or post on LinkedIn.
The course can be done on its own or as a part of the Microsoft Professional Program in Artificial Intelligence and Microsoft Professional Program Certificate in Data Science programs. This data science course provides explanations of machine learning theory, which is combined with practical scenarios, hands-on building, validation and deployment of machine learning models using Azure, Python and R Machine Learning.
Machine Learning – Columbia University (edX)
The Machine Learning course is an advanced level course offered by Columbia University and can be completed in 12 weeks. The course is part of the MicroMasters Program in Artificial Intelligence. The course is free to join, but you can get a course completion certificate for $375. The course is taught by John Paisley, Department of Electrical Engineering, Columbia University.
In the course, you will learn the methods and models and apply them to real-world scenarios. The course follows probabilistic vs non-probabilistic modeling and supervised vs. unsupervised learning. The course includes topics such as classification and regression, sequential models, clustering methods, topic modeling, model selection and matrix factorization.
Data Science: Machine Learning – Harvard University (edX)
This is an introductory level course offered by Harvard University. The 8-week course is part of a Professional Certificate course, Data Science, but it can also be done separately. The course is taught by Prof. Rafael Irizarry, Professor of Biostatistics, Harvard University. You can join the course for free, but you need to pay $99 if you want a certificate of completion.
In the course, you will learn all the skills that are fundamental to machine learning like principal component analysis, machine learning algorithms and regularization by building a movie recommendation system. You will also learn how to make use of datasets to discover predictive relationships. You will learn about training algorithms, about overtraining and techniques such as cross training to avoid overtraining.
Machine Learning Fundamentals – UC San Diego (edX)
This is an advanced-level course offered by UC San Diego. The course will take around 10 weeks to complete and is taught by Sanjoy Dasgupta, Professor of Computer Science and Engineering, UC San Diego. You can join the course for free, but you need to pay $350 if you want a certificate of completion.
The Machine Learning Fundamentals is part of the MicroMasters program, Data Science, where you’ll learn supervised, as well as unsupervised learning algorithms and the theory behind them. You’ll learn classification of the images, identifying important topics in documents, categorizing people according to their personality profiles and capturing the semantic structure of words and using it to categorize documents by making use of real-world case studies. Using the knowledge from this course, you’ll be able to analyze different kinds of data and also build predictive and descriptive models.
Principles of Machine Learning: R Edition – Microsoft (edX)
Offered by Microsoft, this intermediate-level course can be completed in approximately 6 weeks. The course is part of the Microsoft Professional Program Certificate in Data Science and can be done on its own also. You can enrol into the course free of cost; however, you need to pay a fee of $99, if you want a certificate of completion. The course is taught by Graeme Malcom, Senior Content Developer, Microsoft, Cynthia Rudin, Associate Professor at MIT and Duke, and Steve Elston, Quantia Analytics, LLC.
This data science course provides explanations of machine learning theory along with practical scenarios, hands-on building, validation and deployment of machine learning models using Azure Notebooks and R.
Principles of Machine Learning: Python Edition – Microsoft (edX)
The course is an intermediate-level course that is offered by Microsoft and will take around 6 weeks to complete. The course is taught by Graeme Malcom, Senior Content Developer, Microsoft, Cynthia Rudin, Associate Professor MIT and Duke, and Steve Elston, Quantia Analytics, LLC. The enrolment into the course is free of cost; however, if you want a certificate of completion, you need to pay a fee of $99.
The course offers explanations of machine learning theory along with hands-on building, practical scenarios validation and deployment of machine learning models by making use of Azure Notebooks and Python.
Machine Learning with Python: A Practical Introduction – IBM (edX)
Offered by IBM, the introductory course is taught by Sayeed Aghabozorgi, Ph.D., Sr. Data Scientist, IBM and can be completed in around 5 weeks. The course is free to enroll into but for a certificate of completion, you need to pay a fee of $39.
The Machine Learning with Python course is part of the Python Data Science Professional Certificate and gets into the fundamentals of machine learning, using the popular programming language Python. The course teaches you supervised/unsupervised learning, how statistical modeling compares to machine learning. Real-life instances of machine learning and the ways in which it affects society and you will also explore many algorithms.
Machine Learning – Georgia Tech (edX)
This intermediate-level course, Machine Learning is offered by Georgia Tech and takes around 14 weeks to complete. The course is taught by Charles Isbell, Executive Associate Dean and Professor, Georgia Tech. You can enroll into the course free of cost; however, you need to pay a fee of $99 if you want a certificate of completion.
The course provides allows you to learn a variety of topics such as supervised and unsupervised learning methods, Bayesian learning methods, reinforcement learning and randomized search algorithms. The course covers various theoretical concepts, which includes programming and hands-on projects.
Data Science and Machine Learning with Python – Hands On! (Skillshare)
This course is available on the Skillshare platform. The course is taught by Frank Kane, the founder of Sundog Education. The course is 9 hours and 56 minutes long and comprises 80 lessons. If you have some experience in programming and scripting, then the course will teach you techniques that are used by data scientists in the industry and help to prepare you if you want to move into this industry. The course covers the techniques of data mining and machine learning that employers are looking for.
If you’re new to Python, then the course begins with a short crash course to Python; however, if you have done a bit of programming earlier, then you will be able to pick up quickly.
Become a Machine Learning Engineer – Kaggle & AWS (Udacity)
This course is offered in collaboration with Kaggle and AWS and is available on the Udacity platform and will take around 3 months to complete. The prerequisites to do the course are the knowledge of intermediate Python and machine learning algorithms.
In the course, you will learn advanced machine learning algorithms and techniques and how to package your models and deploy them to a production environment. The course includes:
- Software Engineering Fundamentals
- Machine Learning in Production
- Machine Learning Case Studies
- Machine Learning Capstone
Machine learning is extremely interesting that opens up a plethora of interesting ideas and techniques and we hope that our reviews of some of the best online course on machine learning will help you choose the one that works best for your journey into this exciting area.
If you want to discover more learning opportunities, then be sure to take a look at our list of the best online courses. Thanks for reading and as always, have a great day!