Utilized in many different sectors of technology, machine learning is by far the most prominent field of computer science today, and it’s likely to remain the most prominent for a long while.
Machine learning, in simple terms, is the implementation of artificial intelligence within computer programs so that such programs are able to access data and learn for themselves without having to be explicitly programmed.
Luckily, you don’t need to be within college grounds to learn machine learning. The vast variety of learning platforms available online has made it easy for anyone and everyone to study just about anything, including computer science, from their place of comfort and without having to spend lots of money.
In this article, we shed light on some of the best machine learning courses offered by reputable online learning platforms such as Coursera, Udemy, Udacity, edX, and others, so stick around.
3 Top Machine Learning Courses
[Best Overall]
1. Machine Learning (Coursera)
The course is tutored by Stanford professor, Google Brain founder, Coursera co-founder, and former VP of Baidu, the famous Andrew Ng. This course is actually responsible for the founding of Coursera.
4.9/5
[Best for the Money]
2. Machine Learning (edX)
Compared to some of the other courses on the list, this course is relatively new. However, this doesn’t take anything away from the fact that it’s one of the best courses for machine learning, proven by the astounding number of positive reviews and a 4.8-star average rating.
4.8/5
3. [Best For Beginners] Machine Learning A-Z (Udemy)
Edureka Excel VBA Certification Training is a good way to go if you want to become a Microsoft Excel expert and looking to get a career in that field because this is a very advanced course.
4.7/5
What Are the Best Machine Learning Courses?
With so many online machine learning courses that you can enroll in from the comfort of your home, pinpointing the best one can be hard, especially if you’re looking for a course designed for a certain skill level - think of online game development, as reviewed here, for example.
Whether you’re an absolute beginner, intermediate, or professional in machine learning, we’re here to help you find the course that best suits your needs. Below are eight of the best machine learning courses you can partake in today.
Best Machine Learning Course Reviews
4.9/5
When it comes to ratings, syllabus fit, and reviews, there’s really no beating Coursera’s Machine Learning course presented by Stanford University.
The course is tutored by Stanford professor, Google Brain founder, Coursera co-founder, and former VP of Baidu, the famous Andrew Ng. This course is actually responsible for the founding of Coursera.
Coursera’s Machine Learning course was released in 2011. Even though it’s presented at a smaller scope than the original course taught at Stanford University, it does an excellent job of covering all aspects of machine learning, including various algorithms and techniques.
The estimated completion time for this course is 11 weeks, two of which are dedicated to deep learning and neural networks.
You'll only need to punch in a few hours per week. This is a free machine learning course, but if you wish to receive a certificate, opt for the paid option.
Worried about your lack of knowledge in linear algebra and calculus? Worry not, as this course brilliantly sheds light on the aspects of linear algebra and calculus that are relevant to machine learning.
The course relies heavily on Octave: an open-source programming language. If you’re looking to use Python or R programming language, this isn’t the course for you. If you’re a complete beginner, however, you’re in luck since Octave is one of the best programming languages you can use to learn the basics of machine learning because of its simplicity.
It’s also worth mentioning that a lot of machine learning instructors found that they’re able to achieve much faster progress with their students when using Octave rather than using Python, Java, C++, NumPy, and R, despite how compelling some of these programs might be.
Evaluation is carried out through multiple-choice quizzes following each lesson. There are also plenty of programming assignments that’ll help you hone your machine learning skills. Note that evaluation is carried out automatically.
All things considered, the course is quite extensive, and Andrew Ng is one of the most dynamic and experienced instructors in the field. What we love the most about Ng is that not only does he tell you what to do while inspiring confidence in you, but he also sheds light on the common pitfalls that you should avoid.
Pros
Cons
2. [Best For The Money] Machine Learning (edX)
4.8/5
Second, on our list is Columbia University’s highly popular Machine Learning course available on edX. Compared to some of the other courses on the list, this course is relatively new. However, this doesn’t take anything away from the fact that it’s one of the best courses for machine learning, proven by the astounding number of positive reviews and a 4.8-star average rating.
The course is delivered by Columbia University’s own, professor John Paisley, who, according to those who have taken the course, is as clear and clever as a professor can be.
The course is remarkably thorough, covering all aspects of machine learning, from its workflow to its algorithms.
According to reviews, this course is more extensive than Coursera’s, which is why we think it’s perfect for intermediates and professionals, as well as beginners with a solid mathematics background.
Please bear in mind that, unlike Coursera’s course, this one comes with prerequisites that have to do with calculus, statistics, linear algebra, coding, and probability.
This course’s evaluation system is based on quizzes, assignments, and a final exam. There are 11 quizzes and 4 programming assignments in total. Enrollees are allowed to utilize MATLAB, Python, or Octave to carry out their assignments.
The course can be audited for free, but it comes with a verified certificate that you acquire via purchase. The timeline of this machine learning course is estimated to be 12 weeks, punching in 8-10 hours per week.
One of the reasons behind this course’s success is that professor John Paisley, along with his supervisor, are both students of the father of machine learning, Berkeley’s very own, professor Michael Irwin Jordan.
Additionally, Dr. Paisley has consistently maintained traction at Columbia, garnering hundreds of students every semester and surpassing all other ML professors.
Before signing up for this course, we recommend brushing up on the mathematics required to keep up with the information that’ll be shared. The curriculum itself isn’t hard for a beginner to grasp, but the mathematics isn’t watered down or revised with this course.
Pros
Cons
3. [Best For Beginners] Machine Learning A-Z (Udemy)
4.7/5
Not quite fond of working with Octave, and you’re looking to utilize Python and R when studying machine learning? Udemy’s Machine Learning A-Z is right up your alley. This is one of the site’s most popular courses, garnering over 8,000 positive reviews and a 4.7/5 star average rating.
Machine Learning A-Z is possibly the most extensive machine learning course on the internet, offering over 40 hours of on-demand video content. It covers everything you need to know about the machine learning workflow. It also covers more machine learning algorithms than competitor courses.
This course has almost 450,000 students, courtesy of not only its content but its tutors as well.
The course is developed and delivered by data scientists Kirill Eremenko and Hadelin de Ponteves. The former happens to be a Forex Systems expert, too.
What we love the most about this course is that it’s more applied than theoretical, which, in turn, means lighter mathematics. Another thing we love is that every single section begins with an intuition video that sheds light on the theory on which the concept of the whole section is based.
Machine Learning A-Z features a dedicated implementation section containing videos based on Python and others based on R so that students can pick the route that suits them the most.
One more thing we like about this course (there’s a lot to like) is that it features a lot of Python and R templates that students can download and implement in their projects to save time.
There are a lot of strong selling points in this course, but assignments and quizzes aren’t one of them. Nonetheless, the course’s applied approach makes up for the lack of high-quality quizzes and homework.
Last but surely not least, this course is of the best courses and perfect for those who are intimidated by the complexity of the two above-mentioned courses from Stanford and Columbia, as well as those who don’t have the prerequisites needed for said courses.
Machine Learning A-Z is a course that gracefully simplifies complex concepts and that doesn’t require anything but high-school maths as a prerequisite, which makes it perfect for beginners.
Pros
Cons
4. Understanding Machine Learning with Python (Pluralsight)
4.7/5
Understanding Machine Learning with Python from Pluralsight might not be as popular as some of the other courses on the list, but it’s definitely one that will walk you through the process of creating machine learning solutions from A to Z.
This course covers three main topics: how to format a problem to ensure solvability, how to prepare the data required to solve the problem at hand, and how to combine the prepared data with the right machine learning algorithms to create future-proof models. Interesting, huh?
This course is developed and tutored by one of InStep Technologies’ most prominent solutions architects, Jerry Kurata, who is also the developer and tutor of other Pluralsight courses such as Deep Learning with Keras, TensorFlow: Getting Started, and Build, Train, and Deploy Your First Neural Network with TensorFlow.
By the end of Understanding Machine Learning with Python, you’ll be able to implement Python, in addition to the scikit-learn library, to create effective ML solutions. You’ll also be able to evaluate and improve upon the different ML solutions you may come across.
What is scikit-learn, exactly, you might be wondering? It’s a free ML library dedicated to Python. It features a wide range of tools that you can utilize for clustering, regression, classification, and statistical modeling, to name a few.
There are a few prerequisites that you need to brush up on before starting this course, though. You need to be familiar with basic statistics and software development. Your experience with software development doesn’t necessarily have to be in Python, by the way. In fact, no previous Python experience is required.
You’ll be doing your demos with the aid of Jupyter Notebook, which combines narrative text and lives code to documents that can be executed and presented in the form of web pages.
The content of this course is presented as follows: an introduction to machine learning, installing and using Jupyter notebook, the machine learning workflow, data preparation for ML, choosing initial algorithms, training your ML model, testing your ML model’s accuracy, and much more. All in all, this course is perfect for those who want to learn the basics of ML via Python.
Pros
Cons
5. Machine Learning for Everyone (DataCamp)
4.6/5
DataCamp is one of the most favored online learning websites when it comes to data science. The platform hosts more than half a dozen machine learning courses, with Machine Learning for Everyone being one of the most popular.
Unlike the rest of the data science courses on this list, Machine Learning for Everyone is a non-technical course, meaning you won’t have to do any coding. The main purpose of this course is to teach students why machine learning is the future, justifying the hype behind the whole field.
Machine Learning for Everyone features hands-on exercises that will enable you to grasp how this remarkable technology is implemented in various sectors, from self-driving cars to Amazon shopping suggestions.
Machine Learning for Everyone is composed of three sections. The first section, ‘What Is Machine Learning?’ is all about introducing the technology, recognizing handwritten digits, what’s true and what isn’t about machine learning, the lingo used in machine learning, and more.
The second section, ‘Machine Learning Models,’ covers topics such as supervised and unsupervised machine learning, regression, clustering, evaluating performance, improving performance, and many more.
The third and final section, ‘Deep Learning,’ covers everything you need to know about deep learning, from what it is and how it’s used to computer vision, image data, natural language processing, sentiment analysis, classification, and more.
One thing we like and also hate about DataCamp is that their data science courses are really good at training students on a particular skill. We view this as a positive because it helps the student encompass a certain skill, but we also view it as a negative because it doesn’t instill critical thinking within the student, which is vital for when it’s time to deal with complex, unforeseen real-world problems.
If you’re looking to get to the zenith of machine learning with DataCamp, you can combine this course with other DataCamp courses like Introduction to Machine Learning, Machine Learning with the Experts: School Budgets, Unsupervised Learning in Python, Supervised Learning with scikit-learn, Machine Learning Toolbox, and Statistical Machine Learning, to name a few.
Pros
Cons
6. Intro to Machine Learning with PyTorch (Udacity)
4.6/5
Intro to Machine Learning with PyTorch is a nanodegree data science program offered by Udacity. The program consists of three courses: Supervised Learning, Neural Networks, and Unsupervised Machine Learning, in that exact order.
The first course, Supervised Learning, will teach you about the most common class of methods used in model construction.
The course comes with its very own project called “Find Donors for CharityML.” The project involves using Python to write a specific script using a dataset obtained from a fictional charity organization.
The purpose of the above-mentioned project is to identify the different categories of people that are likely to donate to that fictional charity. The project will have you evaluating and improving upon three supervised learning algorithms in order to determine the one that yields the highest donation.
You’ll be using models such as Logistic Regression, Decision Trees, Naive Bayes, Support Vector Machine, and Random Forest. If you have basic experience with Python, you’ll find this project to be fairly straightforward.
The second course, Neural Networks, is all about training in PyTorch and foundations of neural network design. Like the first course, this one comes with its own project called “Build an Image Classified,” which requires you to use deep neural networks to create an image classification application with the aid of PyTorch.
The application you’re going to create is supposed to train a deep learning model with a host of initial images. That training will then enable you to use the same model to classify a new dataset of images.
Compared to the first project, this one is more challenging and will take some time. You need to have a solid background in Python programming in order to complete this project.
The third course, Unsupervised Learning, will teach you how to utilize the different methods of unsupervised machine learning in various domains.
This data science course’s project is called “Create Customer Segments.” It requires you to use clustering algorithms like Principle Components Analysis to the customer data of a certain business to external demographic data to identify different categories of customer populations. The third project requires basic knowledge of the MatPlotLib and Seaborn packages in Python.
As you can probably tell, this is a comprehensive course designed for those with intermediate Python programming knowledge. The estimated time of completion for this course is around 3 months, giving it only 10 hours per week.
Pros
Cons
7. Machine Learning Certification Training (Edureka)
4.5/5
Edureka’s Machine Learning Certification Training is yet another excellent data science program that will teach you the concepts of machine learning and artificial intelligence from A to Z. This program is also designed to help students get started with reinforcement learning, which plays a major role in artificial intelligence.
By the end of this course, you’ll be able to apply machine learning and automate different domains with the aid of numerous machine learning algorithms that you can pick from. This program is very practical in the sense that it teaches you the different roles that a machine learning engineer plays.
It will also enable you to work with real-time data, automate data analysis via Python, implement predictive modeling, validate different ML algorithms, learn about the different concepts of time series, and acquire in-demand expertise for when it’s time to apply for that job of your dreams.
Edureka presents a very good argument as to why you opt for their course rather than other machine learning programs. Their argument is that this course is tailor-made for developers who are looking to become machine learning engineers, business analysts who are looking to learn and implement ML techniques, and analytic managers leading a team of analysts.
Moreover, this data science course is perfect for information architects who are looking to acquire predictive analysis skills as well as Python experts who are looking to design automatic predictive models. If you’re one of the above-mentioned individuals, we can’t argue that this course of tremendous value to you.
That being said, it’s quite obvious that this course comes with a few prerequisites. You must be experienced with development via Python, and you need to be familiar with the fundamentals of data analysis, having practiced it with tools like SAS/R.
Note that the course comes with a complimentary self-paced Python statistics course that you can take at your own leisure. By the end of the course, you’ll receive a certificate solidifying your experience.
There are a few things we like about this course. Firstly, it features a life project that’s based on a real-life case study. Secondly, the course is around 36 hours’ worth of content, so it’s concise and straight to the point. To add, every class is accompanied by practical assignments. Lastly, enrollees get to enjoy 24/7 expert online support. What more can one ask for?
Pros
Cons
Notable Mention: Advanced Machine Learning Specialization (Coursera)
4.5/5
As the name of this course suggests, Advanced Machine Learning Specialization from Coursera is aimed at intermediate and professional machine learning engineers.
This one is of the most extensive courses on machine learning on the net, covering more techniques than the vast majority of other ML courses available. In addition to being extensive, it’s concise and very well-presented, so don’t worry about getting overwhelmed by the curriculum.
Knowing that it’s an advanced course, Advanced Machine Learning Specialization comes with a few demanding prerequisites, including a solid background in calculus and linear algebra.
The course is developed by the National Research University's Higher School of Economics, and it’s offered by Coursera. You can audit all of the content for free, or you can pay for it to receive a certificate by the end of the course.
This specialization is composed of seven comprehensive courses. The first course, Introduction to Deep Learning, features six chapters on its own, including Intro to Optimization, Intro to Neural Networks, and Deep Learning for Sequences. The first course is concluded with a final project that summarizes its entire content.
The rest of the courses are How to Win Data Science Competitions, Bayesian Methods for ML, Practical Reinforcement Learning, Deep Learning in Computer Vision, NLP, and Addressing the large Hadron Collider Challenges with ML.
While most competitor ML courses require around three months to be completed, this advanced course requires 8-10 months. Don't worry, though; you won't have to spend more than a few hours per week on this course. Throughout the months, you’ll be tackling several real-life projects that will be very beneficial to your portfolio and GitHub.
Pros
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Factors to Consider Before Enrolling in a Machine Learning Course
Now that you’ve learned about the top machine learning courses on the web, it’s time to choose the one that suits your needs the most. To do so, there are two important factors that you must account for: course prerequisites and familiarity with the fundamental ML algorithms. Let’s cover each factor briefly.
Prerequisites
Virtually all machine learning courses require prerequisites. The more advanced the course, the more demanding these prerequisites become. For instance, Machine Learning A-Z is perfect for beginners because its only prerequisite is high-school mathematics, while Advanced Machine Learning Specialization requires a solid background in calculus and linear algebra.
Depending on the course’s difficulty level, you’ll probably need to brush up on four components: linear algebra, calculus, probability, and programming.
There are courses that include math refreshers that cover the basics of linear algebra, such as matrices and vectors. A prime example is Andrew Ng’s Machine Learning from Coursera.
We don’t recommend signing up for a machine learning course if you haven’t learned linear algebra. That shouldn’t halt your passion to become an expert in data science and machine learning, though, as there are plenty of excellent courses on linear algebra, including Coursera’s Matrix Algebra for Engineers.
For calculus, we highly recommend MIT OpenCourseWare’s Single Variable Calculus course. For probability, Fat Chance from edX is top-notch. For programming, Coursera’s Programming for Everyone is the way to go.
We also recommend learning how to use Python even if the course you’re interested in doesn’t rely on it. You’ll need it at some point in the future. DataQuest is an excellent place to learn all you need to know about Python.
Basic Algorithms
Many machine learning courses are based on a host of fundamental algorithms that you must be familiar with, including linear regression, k-means clustering, logistic regression, support vector machines, decision trees, naive bayes, random forests, and k-nearest neighbors.
There are more advanced techniques that you may need to brush up on, including ensembles, dimensionality reduction, boosting, neural networks, reinforcement learning, and deep learning.
You don’t need to know how to implement each of these algorithms; after all, that’s what the above-reviewed online courses are for. You just need to familiarize yourself with them so that you’re able to keep up with the ML course you plan on taking.
Final Thoughts
There are plenty of excellent machine learning courses accessible to everyone, but we assure you that the ones reviewed in this article are the best of the bunch and that one of them will help catapult your career in data science and machine learning.
If you’re a complete beginner, you should go for Machine Learning A-Z or Andrew Ng’s Machine Learning. If you’re an intermediate, virtually all of the online courses on the list, apart from Advanced Machine Learning Specialization, are a great fit for you.
If you’re a professional looking to update their knowledge of machine learning, Advanced Machine Learning Specialization and Understanding Machine Learning with Python are right up your alley.
