Machine learning allows computers to make sense of the extreme amounts of data that’s too much for humans to understand, and it’s currently one of the quickest growing sectors in information technology.
Specialists in machine learning are becoming highly sought after, with big tech companies offering generous salaries to any trained professional willing to join their team. As of the writing of this article, Google has 328 open job positions related to machine learning, many of which are above $100,000 per year. The job market demand for machine learning is there, and it’s growing. All you need to do is to develop your skills and competence in the field. Learning a highly technical field such as machine learning will not be easy, but with the right mindset, it’s more than possible.
From the top-rated courses on this list, you can expect to learn all there is to know about machine learning algorithms. Topics such as neural networks, data science algorithms, artificial intelligence, and of course – lots of coding! You can also expect to see linear regression models, logistic regression models, decision tree learning, and much, much more.
Here at E-Student, we place the highest value on courses that are practice-based. Instead of taking lectures on theoretical concepts day in and day out, we prefer to get our hands dirty with programming algorithms and a little quiz every now and then. We also love hands-on student projects and strong mentorship whenever possible, so you can expect that to be a common theme in our picks for the best machine learning course.
What is the Best Machine Learning Course?
Here are our top picks for the best machine learning course:
- Machine Learning (Coursera x Stanford)
- Intro to Machine Learning & Machine Learning Engineer (Udacity)
- AI & Machine Learning Career Track (Springboard)
- Advanced Machine Learning Specialization (Coursera)
- Machine Learning Certification Course (Simplilearn)
- Google Cloud Platform Big Data and Machine Learning Fundamentals (Coursera)
- Machine Learning: Andrew Ng, Stanford University (YouTube)
- Machine Learning by Columbia University (edX)
- Machine Learning Crash Course (Google)
- Bayesian Machine Learning in Python: A/B Testing (Udemy)
Machine Learning (Coursera x Stanford)
This is an online machine learning course that is as close to being perfect as a course will ever get.
If the name “Andrew Ng” doesn’t ring a bell for you, I’ll give you a quick rundown as he really is the soul of this course. Andrew Ng is the co-founder of Coursera, ex-director of Stanford’s AI Lab, head of the AI Fund, and former VP of Baidu. The list goes on and on, but I’ll stop here. What matters most is that he is one of the most recognizable names in the AI industry, and there is no better person than him to learn AI and machine learning from.
With a nearly perfect 4.93/5 rating on Coursera, this course is in a league of its own. It is one of the three initial courses that led to the massive success of Coursera, and it’s been updated with new information on a constant basis. The massive popularity of this course should not come as a surprise to anyone who is a graduate of it. Even if you removed the highly valuable Stanford certificate from the picture, you’d still be left with a well-built, easy to follow, and highly influential machine learning class. But, of course – the Stanford ML certificate is a perfect icing on the cake.
Another advantage of this machine learning class is that it can be taken by anyone regardless of experience level. It’s meant to be suitable for everyone interested in how machine learning techniques work, and while prior computer science and programming experience will be beneficial, it’s not a must by any means. For beginners and experts alike, this is one of the very best machine learning courses one can take.
Intro to Machine Learning & Machine Learning Engineer (Udacity)
Two superb machine learning classes from Silicon Valley experts.
NEW YEARS UPDATE: Right now you can get 50% off these Udacity courses by using the code “NEWYEARS2021” at checkout.
Udacity offers two courses in machine learning, each aimed at different levels of experience:
- Intro to Machine Learning. This online course is ideal for beginners who are looking to build foundational knowledge in machine learning. Intermediate Python knowledge (~40 hours) together with basic knowledge in probability and statistics is recommended, but not required. Udacity also offers an “Intro to Programming” course which teaches all the recommended prerequisite material for this machine learning course.
- Machine Learning Engineer. This online course is considerably more advanced than the previous, and so are the requirements. Students who wish to take this course need to have proficiency not only in Python but also in machine learning algorithms. Our recommendation – start off with “Intro to Machine Learning”, and then continue with “Machine Learning Engineer”, as the two training programs complement each other perfectly.
Udacity’s beginner-oriented course, “Intro to Machine Learning”, will cover:
- Using Python & SQL for data access & analysis from different data sources.
- Building predictive models using both unsupervised & supervised machine learning tools & techniques.
- Performing feature engineering to improve current machine learning models.
- Optimizing, tuning, and improving algorithms according to various metrics.
- Comparing the performances of learned models using various metrics.
As the curriculum shows, the course material here is still highly technical, despite it being a machine learning course aimed at beginners. Therefore, we suggest that you start off with developing the prerequisite skillsets mentioned earlier (Python, probability, statistics), before attempting this course.
Udacity’s advanced machine learning online course, “Machine Learning Engineer”, covers the following topics:
- Building and testing machine learning Python codes.
- Building predictive models using various unsupervised & supervised machine learning tools & techniques.
- Cloud deployment terminologies and best practices.
- Using Amazon SageMaker for deploying machine learning models.
- A/B testing different deployed models and evaluating their performance.
- Utilizing an API for deploying a model to a website in a way that it responds to user input, dynamically.
- Updating deployed models to respond to changes in the underlying data source.
In addition, the two courses will teach you how to create real-life projects in the field of machine learning. Being able to create machine learning projects independently is highly valuable. That is because independently developed projects can be shown to potential employers to demonstrate expertise. No matter the industry, job applicants who have a real portfolio of projects under their belt will always be chosen first, compared to those candidates with purely theoretical knowledge.
Additionally, Udacity’s machine learning courses will provide you with a clear overview of the existing career opportunities in the industry.
With support from industry professionals, highly in-depth course material, and actionable exercises, these two programs are worth any budding coder’s time and effort. Start off with the basics of machine learning with the Intro to Machine Learning certification program. Then, take your expertise one step further by learning how to create innovative machine learning programs and building your very own portfolio, by taking the Machine Learning Engineer certification program. Overall, these two are fantastic machine learning courses which work the best when taken consecutively. If you’re a beginner with some basic Python knowledge, then these two will be the best machine learning courses for you to take.
AI & Machine Learning Career Track (Springboard)
A superb and information-packed course for intermediate coders.
This machine learning course is designed for students who already have a solid foundation in software engineering and who want to develop further knowledge in machine learning. Only those who can pass a programming challenge provided by Springboard will be eligible for admission.
A wide range of topics is covered in this machine learning certification program. Some of the topics which are covered in the curriculum are:
- Overview of AI and Machine Learning Engineering Stack
- Data Wrangling at Scale and Statistics for AI
- Foundations of Machine Learning
- Deep Learning
This online course finishes off with each student developing a capstone project – a full-scale machine learning API application. The application needs to be fully functional and approved by the Course instructors, who are true experts in the field of machine learning. This capstone project will be your foot-in-the-door when landing a machine learning job, as you can use it to demonstrate your practical know-how and expertise. The project takes approximately 100 hours to finish, and the complexity of it is one of the main reasons why Springboard chooses all students admitted into the course with the utmost care.
This machine learning certification will be the most difficult to finish out of all the courses on this list. An intermediate level of knowledge in programming is required for admission, and the course material will be difficult to chew through if you’re not fully dedicated and motivated to start a career in the field of machine learning. The students who do manage to finish this online course, though, will be rewarded appropriately. All students who graduate will be certified experts in machine learning, and they will be rewarded with a guaranteed job proposal from a company specializing in machine learning. All things considered, Springboard’s AI & Machine Learning Career Track is one of the best machine learning courses available online (as long as you have some prior experience in programming).
Advanced Machine Learning Specialization (Coursera)
An expert-oriented ML class that teaches advanced machine learning techniques and requires prior programming skills.
It’s the best match for students who are already familiar with the basics of machine learning, Python, linear algebra, probability theory, and calculus. As it’s an advanced specialization course, it’s not meant for beginners.
This course provides a high-level overview of what machine learning can offer, and it covers subjects such as neural networks, deep learning, and advanced data analysis. You’ll learn the methods used for creating machines that can play games, automate workflows, and even generate their own images and content. Later sections of this course can help you branch out into a field that you want to focus in, like games and multimedia.
One of the most valuable section of this machine learning training program could be considered the second part, which is “How to Win a Data Science Competition: Learn from Top Kagglers”. This section of the course syllabus teaches students how to create machine learning projects which can be used to win international competitions, and it ends with a final project created individually by each student. Billion-dollar companies have been started from open competitions in the past, and with the help of this course, you will be one step ahead of the competition. The material in this course is difficult but highly rewarding.
This isn’t a course for the faint of heart. Only take this if you’re already well on your way towards a machine learning career and want to get more hands-on experience as a supplant. As this training program was co-developed by CERN (The European Organization for Nuclear Research) it’s highly technical, and previous programming knowledge is a must. However, all of the content covered by the course is highly valuable, and any budding machine learning enthusiast will learn something new from the material in this program. For intermediate to advanced students, it’s one of the top machine learning courses available. And, it’s by far the best machine learning course that is available for free. It’s worth mentioning, though, that while the course content is free to access, you will not get a certificate of completion for this course until you buy it.
Machine Learning Certification Course (Simplilearn)
A well put together short course that is filled with student projects.
Students who have basic knowledge of Python, statistics, and mathematics.
This online course was built with hands-on experience in mind, and the whole course is structured to combine theory with practice at every step. During every lecture, you will have to utilize theoretical knowledge in order to create working machine learning applications.
Here are some of the topics covered in Simplilearn’s Machine Learning Certification Course:
- Supervised & unsupervised learning
- Recommendation engines and time series modeling
- Statistical and heuristic aspects of machine learning
- Various Python-based machine learning models
In addition, all students who take this online course will create four end-to-end projects, thus solidifying their practical knowledge even further. As a result, students who graduate from this program will be able to program various types of machine learning models independently.
Simplilearn’s Machine Learning Certification Course is a comprehensive and well-structured online course. It does an excellent job of covering all major topic areas in machine learning, and the inclusion of practical student projects into the curriculum is very important. All in all, this is a very high-quality machine learning course, and we can give it a strong recommendation. It is considerably shorter than many of the alternative courses on this list, but the mandatory student projects add exceptional value to an otherwise short course.
Google Cloud Platform Big Data and Machine Learning Fundamentals (Coursera)
A beginner-level ML training program that narrows down on the Google Cloud platform.
Machine learning engineers who are looking to build their expertise with Google’s cloud services. This course is not very beginner-friendly, as it requires students to have at least 1 year of experience in the course subjects.
Aside from the basics of machine learning, this course dives deeper into the data processing aspect of machine learning. You will be taught how to create an infrastructure that can help your program understand the data it is working with. In latter parts of the course, you can even build your own neural network as a proof of concept for your theory classes.
Most of the course material covered here is related to the Google Cloud Platform, so if you don’t plan on using Google’s cloud services for your machine learning applications, you are likely better off with another course.
If you are, however, interested in the Google Cloud Platform, then you will find plenty to learn from this course. You will be taught subjects such as:
- Differentiating the different big data and machine learning toolsets offered by Google
- Migrating existing services over to the Google Cloud Platform
- Employing BigQuery and Cloud Datalab for interactive data analysis
- Creating neural networks with TensorFlow
- Differentiating the different data processing services of the Google Cloud Platform
Any engineer that uses the Google Cloud Platform should take this course. It’s filled with highly useful information about the platform, and it’s an undisputed asset that can help any data scientist understand the intricacies of data processing through the tools offered by the Google Cloud Platform. The course is rather limited to Google’s cloud platform, though, so keep this in mind before starting the course.
Machine Learning: Andrew Ng, Stanford University (YouTube)
A series of video lectures from the famous Andrew Ng.
This course is designed for all students interested in machine learning. Both complete beginners and advanced data scientists will find something to learn from the course material, and there are no direct prerequisites.
Andrew NG, the author of this course, covers a wide range of topics in his video lectures, some of which are:
- Supervised & Unsupervised Machine Learning
- Linear Regression & Regularized Linear Regression
- Linear Algebra
- Neural Networks
- Support Vector Machines
- Dimensionality Reduction
- Anomaly Detection
The video lectures contain plenty of real-life examples, illustrations, and practical tips, which make the whole series surprisingly easy to chew through, despite the highly technical topics. Andrew is an expert at his craft and he does an excellent job at simplifying complex ideas to students unfamiliar with the technical details of machine learning.
If you’re a beginner and want to learn machine learning online for free, then Andrew NG’s series of YouTube videos will be an excellent starting point for you. YouTube has some limitations as an e-learning platform, but despite this, we can highly recommend this series of video lectures on machine learning.
Machine Learning by Columbia University (edX)
An excellent online ML program from Columbia University.
Students who wish to take edx’s Machine Learning course should have knowledge in calculus, linear algebra, probability, statistical concepts, and basic coding & data manipulation.
The creator of this course, John W. Paisley, is a researcher who has focused his efforts on statistical machine learning, and this also shows itself in the course material. In this course, John takes his extensive knowledge on the topic of statistical machine learning and passes it on to the students. If you are interested in the inner working of statistical machine learning, then you will find this course best suited for you.
Supervised learning techniques for regression and classification are covered in the first half of the course, while the second half focuses on unsupervised learning tools and techniques. Although this course is mostly focused on theory rather than practice, it does show some excellent real-life examples in order to make the theory easier to grasp for the students.
Some of the lectures covered in this online course are:
- Probabilistic & non-probabilistic viewpoints
- Optimization & inference algorithms for model learning
- Data modeling & analysis
This machine learning course goes very deeply into the field of statistical machine learning, and it tends to focus more on theory rather than practice. The theoretical nature of the lectures could cause some students to shy away after a few sessions, but those who do stick around get to develop true expertise in a very promising direction of machine learning. If you’re capable of handling 3 months of advanced theory in machine learning, then you will definitely stand to benefit from John’s teachings. His expertise in the field of statistical machine learning is unparalleled, and while his course is not the most beginner-friendly, it IS one of the most resourceful.
Machine Learning Crash Course (Google)
A short but sweet crash course.
This course is for those of you who have knowledge in basic algebra and programming and want to learn machine learning online for free.
As the “crash course” name implies, this program is a brief introduction into the world of machine learning. It will explain to you what’s the value of machine learning and what are the core principles that make it work. The course also gives learners a quick overview of the more advanced concepts of machine learning such as building multi-class neural networks.
The Machine Learning Crash Course will not be enough by itself to develop full expertise in machine learning. However, due to its introductory nature and free pricing, it still serves a very important role on this list. The course material will get you up to date with the fundamentals of machine learning, and you will develop a better understanding of whether machine learning is the right career choice for you. Without question, the Machine Learning Crash Course by Google is one of the best free machine learning courses available, despite its relatively short duration.
Bayesian Machine Learning in Python: A/B Testing (Udemy)
A Python-based machine learning class.
For software engineers looking to combine machine learning with marketing, this is a course that’s perfect for the job. In addition, this course is a good match for any Python programmers looking to refine their comparison algorithms.
This program focuses on the integration of the Bayesian machine learning method into traditional A/B testing, allowing your programs to make more sense out of two data sets. Filled with simple explanations and further readings on how these can affect marketing strategies, this course gets to the bottom of how probability factors into machine learning.
Savvy marketers will understand the importance of integrating machine learning into their strategies, and this online course is one of the best resources for information on this topic. The course material will be difficult to chew through for marketers without any technical background, but those who do manage to complete the curriculum will be one leap ahead of the competition. It’s a high-quality course throughout, but it’s best suited for a niche audience of students who want to combine marketing with machine learning.
A Beginner's Guide to Picking a Good Software for Building Online Courses
Some Tips for Choosing Your Course
If data informs the machine learning structure that you’ll build, it should also inform you about the kind of course you want to pick. Each one of these programs offers small glimpses into the fascinating world of machine learning—but it ultimately falls to you as to how best to use the knowledge and tools that they will provide.
Above all else, it’s the kind of data that you’re expecting to handle that should inform your decision. Will you be handling big data to formulate company strategy, or are you a budding engineer that simply wishes to expand their repertoire? What are the resources you have at your disposal, and how do you wish to handle and interpret the data you’ll be using machine learning on?
It’s also critical to remember that machine learning should be accessible. There’s no point in throwing yourself into a technical course if you aren’t really looking to use machine learning in that way—sometimes, learning why it matters and how can it inform your strategy moving forward is enough. Taking your needs into consideration is a key part of choosing which kind of machine learning course you should take.
A Few Good Reasons to Learn Machine Learning Online
Machine learning has been at the heart of many cutting-edge technologies and it is a critical component to unlocking the full potential of artificial intelligence. And, on the everyday level, machine learning can be used to improve how we navigate the world and the information that’s available to us—a necessity in the information age. Data science, computer science, deep learning, machine learning – these are all topics that are becoming increasingly in-demand, and learning them right now will secure lifetime careers. Lucrative ones, in fact.
The role of machine learning in regards to developing artificial intelligence is crucial in developing more sophisticated systems. As a subset of this field, machine learning is a key component in creating better programs that can anticipate what we need aside from merely processing data, and it can be used as a springboard to make complex programs that can automate entire fields of work.
Machine learning is a paradigm shift: it allows us new insights into the vast amounts of data that we generate on a daily basis and helps us interpret it in ways that can benefit the people who generated it. It’s a cornerstone of many innovations in smart machines and has contributed to technological wonders such as the Internet of Things.
Taking up machine learning is also a very good introduction to the basics of artificial intelligence: particularly, the core processes that allow our machines to be more “human-like.” Teaching them how to properly integrate the inputs regarding their world informs our innovations on how they choose to use this data and can be positioned in a variety of ways to help countless industries.
Are Online Courses the Answer for You?
Online courses in machine learning are best suited for you if:
- You’ve been looking for a way to get into machine learning, without any prior knowledge in the field. If this is the case for you, then check out our “best for beginners” pick.
- You already have expertise in the field of machine learning, and you’re looking to step it up a notch by expanding on your current knowledge. In this case, check out our “best for experts” pick.
- Your primary goal is to start a career in machine learning. In order to guarantee a job offer, your best bet will be the “best for a career” pick.
These are just some of the best machine learning courses that we’d recommend for you to take. Varying in the application, the expertise required, and the field of machine learning, we will cover plenty of other top-rated machine learning courses in this review.
Before you buy any of these courses, it’s important to note that many of them do require some level of knowledge in basic and advanced mathematics, programming languages (usually Python), and some familiarity in the structures that make up essential computer code.
If you want to learn machine learning online, then keep in mind that it’s far from simple. While some of these courses may be short and aimed at beginners, they still require lots of time and effort in order to be learned effectively. Even the most introductory of these courses are heavy on the technical details.
If you don’t have the prerequisite skill sets required by some of these courses, it might be easier to start from the basics of computer programming with the help of a Python Course. Or, before learning machine learning, you could start from another field that gives a more general overview, like artificial intelligence courses, or deep learning courses if your main interest lies in neural networks. If you’re dead set on going all in on machine learning, though, then it’s time to break down the top machine learning courses, one by one.
The Role of Data Science in All This
The goal of machine learning is simple: it answers questions using the data provided. It’s a tool that we’ve formulated to help us interact with the world by sorting, organizing, and changing the data we generate with our every action, from the smallest purchases we make to the largest shifts in our economy.
More importantly, it allows us to make smart, actionable, and sustainable goals using the data that we feed it. Machine learning has the potential to open a new dimension of understanding previously inaccessible to us, and that dimension can help in all sorts of application to improve the way our world works.
Every company uses some level of machine learning in order to run efficiently. As a career option, studying machine learning online is a viable path for many tech-minded people out there. As a feature, it’s rapidly becoming a necessity in order to keep up with this competitive and data-driven world. If you’ve ever wanted to see what the fuss about machine learning is about, take a look at the list we’ve compiled above—and you’ll be amazed at what you can discover.
Data science is becoming increasingly prevalent in our lives, and it will continue to grow as a crucial field in the coming years.
As you can see, there are many high-quality machine learning courses out there, all with different learning goals. They can give you a machine learning certification of your choice once the program is completed, allowing you more opportunities in your career as a data engineer. Machine learning courses fulfill a much-needed and rapidly-growing demand in many companies today, giving programmers a new horizon to explore when it comes to skills, growth, and job security.