Machine learning is a powerful subfield of AI that involves studying computer algorithms (i.e., programs that run on electronic devices) that organically improve through experience, and learning from patterns in historical data.
These machine learning algorithms build increasingly accurate models based on sampled data which they then use to make predictions or decisions. They can be used for many things including product recommendations, website optimization, network security, and even web searches! It is common in the fields of business intelligence & big data management.
TOP 11 Best Coursera Courses for Machine Learning (With Certificates)
- Machine Learning – Stanford
- Machine Learning Specialization – University Of Washington
- IBM Machine Learning Professional Certificate
- Machine Learning – DeepLearning.AI
- Mathematics for Machine Learning Specialization – Imperial College London
- Applied Data Science with Python Specialization – University Of Michigan
- Machine Learning Engineering for Production (MLOps) Specialization – DeepLearning.AI
- Machine Learning for All – University Of London
- Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate – Google Cloud
- IBM Data Science Professional Certificate
- Advanced Machine Learning Specialization
If you’re looking to brush up your skills in a particular area, we’ve put together this handy guide of the best courses available on Coursera for honing your machine learning capabilities. Specializing in partnering with universities and companies, Coursera is one of the world’s top online education platforms, now boasting more than 77 million users across the globe. Here’s a breakdown of the best machine learning courses on Coursera and what you can expect to learn from each one.
In this course, you will gain experience with machine learning and AI. Specifically, you will learn about some of the best practices in creating innovative solutions for new problems, and also gain experience implementing those solutions using powerful algorithms to make sure your approach works.
This course offers a comprehensive overview of machine learning and statistical pattern recognition. Topics include supervised methods (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)unsupervised methods (clustering, dimensionality reduction recommender systems), innovation best practices in machine learning, and artificial intelligence.
You will work with many real-world case studies and apply these technologies to robotics perception for control systems, understanding natural language (web search and anti-spam), computer vision, medical informatics applications, and audio systems among others.
With all that covered in just 61 hours, this course feels a little cramped. On the other hand, is extremely valuable for getting a basic understanding of a broad range of topics. You can join other courses later to drill down into an area that interests you.
This is another Introduction to Machine Learning course: This short course will walk you through developing algorithms in machine learning that can create solutions for a very wide range of issues. It covers many specific techniques–including Artificial Neural Networks, Support Vector Machines, and Tree-Based Methods–allowing you to select the best algorithm for even the toughest of problems!
This course is part of a broader ai program offered by The University Of Washington. You can of course join this course on its own but may need a little background knowledge of ai or computer science beforehand.
This course is broken up into 4 sections which are:
- Machine Learning: A Case Study Approach
- Clustering And Retrieval
This course delves into fewer subjects than our top pick but goes deeper into each topic covered. With an expected turnaround of 7 months at 3 hours a week, you could knock this over quickly if you decided to study full time. Alternatively, you could pick up one of The University Of Washington’s other related courses to complement your learning.
This course is a professional certificate in machine learning from one of the most respected computer companies in the world. In addition to the Coursera certificate, you will also receive one from IBM. This makes it one of the better courses for improving your resume. It applies real-world concepts that are present in most machine learning working environments.
This course is targeted towards people who are already working in the industry and want to up their skills.
You should have some background in statistics, linear algebra, and Python programming but this mid-level course is suitable for anyone who has, an interest in leveraging data, some decent computer skills, and a good attitude towards learning.
They begin with the basics and give code-along demos, to build up to the more complex topics. However, it’s easy to see how someone with no background knowledge would find themselves very lost in this course quite quickly.
This certificate is broken down into 6 courses that cover a broad range of topics from data analysis to specialized models.
This is a dedicated specialization (about Coursera Specializations) from a reputable course provider that pretty much only works with ai. You can rest assured that this is a perfect course to cover pretty much every topic under the umbrella of deep learning.
Deep learning is a particularly complex branch of machine learning and may not be exactly what you’re after so it may be a good idea to do a little research before selecting this specialization.
Machine learning is a method for allowing computers to act and learn without having explicitly been programmed. Deep learning refers to a type of machine learning that allows computers to learn more naturally in the same manner as the human brain absorbs information from an environment.
Deep learning can sort through conflicting data, suggests hypotheses, and fit inherited models more accurately. It is particularly valuable when analyzing data that hasn’t had the opportunity to be labeled previously beyond simple yes/no answers or when one wants a clear answer in regards to possible future outcomes which may not have occurred before.
In this specialization, you will learn about a number of technologies to help achieve computer vision aims. With a big emphasis on creating and maintaining networks and nodes. You will also take part in practical projects that involve the creation of computer deep learning solutions based on the above concepts and tools.
This course also covers image recognition, speech recognition, music synthesis, chatbots, natural language processing, and machine translation.
This is one of the most beginner-friendly courses related to machine learning. It is an introductory course dedicated to freshening up your maths skills before you dive into the more complex equations that are required for more difficult machine learning courses.
If you’ve struggled in the past with subjects like calculus or linear algebra, this specialization’s for you. It’ll give you a solid grasp of the underlying concepts as well as their applications within computer programming instead of the fields (like Finance and Economics) that utilize similar math which is how you would have encountered this type of math before.
As this is sort of like a pre-requisite course, it is much shorter than many others in our top picks. It’s a 4-month course with a recommended 4 hours a week. It does have a flexible schedule but like any other math-related course we recommend you do the classes regularly to make it easier to build upon prior knowledge.
Through this specialization, you will use your Python knowledge to complete assignments or on interactive notebooks. They are like pieces of paper or notepads on your computer that have a canvas where you can write things. You can also easily print them if needed for this assignment or that. Its purpose is to apply the theory and equations you learned about during the class assignments so you can get some more practice with real-world problems.
This course is designed to introduce you to data science through the use of Python. Once again, you do need a basic understanding of Python to engage with this course. Expect to learn a lot more about this program as you learn though. There are many intermediate and advanced Python techniques you will visit during this specialization.
You will visit matplotlib, pandas, scikit-learn, networkx, and nltk Python toolkits to gain insight into your data.
Within this specialization is 5 courses but the one that is a standout for this specialization is “Introduction to data science in Python”. Few other machine learning Coursera courses are so specific and beginner-friendly. It’s a perfect starting point if you haven’t worked much with data science in the past and have minimal Python experience.
This course will help you learn how to use Python, work with pandas, CSV files, and NumPy. It’s important to understand the fundamentals behind these vital technologies that every data scientist uses in their daily lives. By taking this course you will have gained a basic understanding of the programming environment in which most data science projects are created.
Much like the other DeepLearning.AI course dedicated to deep learning (imagine that!), this course is also targeted toward a specific branch of machine learning.
This course is all about the production engineering side of machine learning.
Machine learning model deployment is a bit like app development for the web. It’s not enough to have a functioning machine learning model; you need to effectively deploy it in a production environment so that it can be used by your product team. Like with modern software engineering, machine learning production engineering combines concepts of machine learning with the function of modern engineering and software development roles. So, you can see how this course can create career growth opportunities.
This MLOps specialization covers how to build, and maintain integrated systems that continuously grow and improve. You will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently in order to give the product managers the best experience possible with the project. They need effective, efficient systems that can be maintained while working their way through AI to produce quality digital products.
On top of the production engineering, you will learn how to create a machine learning sequence from end to end. That means this course touches on subjects as many others in our top picks. It does not go into as much depth but the perspective of this course being from an operation standpoint may suit many learners or those trying to shift their already blossoming machine learning career in a new direction.
As the course title implies this course is intended to be accessible to everyone. That means the math and engineering components of the course are much easier than many of our other top picks. This can help avoid feeling overwhelmed by tackling new complex maths and new programming concepts at the same time.
This course if probably the best choice for you if you are completely new to programming and have not tackled any complex math since you finished high school (or are still in high school).
A negative of this course is that it doesn’t cover the programming side of machine learning at all. That means no Python or TensorFlow. You could gain an understanding of these programs from another short course and incorporate them into the knowledge gained in this course. Yet, learning in this fashion lacks cohesion when compared to an all-inclusive course.
A hot tip would be to follow this course with the Mathematics For Machine Learning from the Imperial College London. That way you can learn the more complex math and programming skills needed to progress into a career of machine learning.
In this course, there are 4 short modules that range from 3 to 6 hours to complete. The first 3 modules are delivered as video lectures, some reading, and quizzes. The final module is a practical project. You can complete this entire course in 22 hours so it is definitely a highly recommended course for newbies to dip their toes into the world of machine learning.
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate – Google Cloud
Obviously, Google is one of the biggest companies in the world. Their platform for machine learning known as Google Cloud is used across Google and many other web-based machine learning applications.
This course works a little differently from most other Coursera courses. In most courses, you will get a Coursera certificate at the end to say you attended and completed all modules to an acceptable level.
Some courses provide you with a professional certificate such as the IBM course we discussed earlier. The way that the Google Cloud course is different is that it is a preparation course to be able to pass an exam to obtain your certificate from Google.
This is a prerequisite to work for Google themselves and is regarded highly by other employers that work on the same platform.
This professional certificate includes online Google Cloud Platform hands-on labs and you will gain practical skills with the Google Cloud Platform products. They use a system called QwikLabs to deliver your lessons.
This is an intensive course with plenty of career opportunities upon completion. However, you will only learn about the Google Cloud platform so you miss out on learning Python or TensorFlow skills.
This is another highly regarded professional certificate offered by IBM. This is a more beginner-friendly course than the other IBM certificate in our top picks but is not positioned as high as it is not as targeted towards machine learning.
However, there are still a ton of machine learning-related skills that are covered in this course. Data science skills themselves are extremely important to machine learning. This course could be a good place to find your feet with data science before moving into machine learning.
You still cover machine learning algorithms as well. Not in as much detail or as broad of scope as other courses but definitely enough to decide if you want to pursue machine learning further.
Do you already know your way around the programming suites and are comfortable with the complex equations associated with machine learning? Great! This course is targeted at learners like you.
If you want to push your career to the next level, you will need to expand your understanding of the guru level.
This specialization takes you through several different topics of machine learning that would be way too complicated for a beginner so be careful about enrolling if you don’t know what you’re doing.
Upon completion of 7 courses in this specialization, you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.
You’ll be able to lead teams more effectively as well as increasing your own understanding greatly through this advanced 10-month course.
If you’re an absolute beginner, check out “Machine Learning For All” from the University Of London. It’s a great course for dipping your toes without getting bogged down in complex programming.
If you have a little knowledge of programming already our top pick from Stanford is probably the best fit for you.
Each other course serves a purpose that may suit an individual above all the others. We recommend checking out the course outlines of each on Coursera for yourself to check which is best for your needs. Whether it’s progressing your career, starting a new one, or just learning something new for the fun of it, these courses have you covered.