Want to become an expert in Machine Learning? Start easy and build your way to the top with the best available online Machine learning courses!
There are a lot of online Machine Learning Courses, oriented towards different kind of students, there are courses that are very mathematical, others that are oriented towards developers, and others that sit in between. There are also very easy generic courses, and very specific and advanced ones. Lastly, we have the issue of free vs paid courses, certificates…
Discover the best machine learning course for you in this repository!
- Free Courses:
- Paid Courses:
- Introductory courses:
- Medium experience courses:
- Advanced courses:
Free course: This course is completely free!
Description: This course explains what Machine Learning is in very simple terms. It covers supervised learning in depth, along with cool examples and also goes a little bit onto reinforcement learning to highlight how AI systems have beat professional players on certain games. It is very theoretical.
It ends speaking about Deep Learning and some of the misconceptions and problems with Machine Learning. Each of the 13 chapters comes with a short video and a quick quiz to test your knowledge.
This is a very introductory and theoretical course that is cool to obtain a basic notion of what Machine Learning is and what it can be used for without going into a very long and expensive course or too deep into maths or specific models.
Duration: Its a fairly short course, which can be completed in less than two weeks with not much dedication.
Prerequisites: Not any really, all kinds of students should be able to easily follow this course.
Paid Course: This course costs about 14€ when discounted in Udemy (which is most of the time). If you want to do it just wait until there is an offer; they show up periodically. Again, it is completely worth it if you can find it with such conditions.
Description: This course is very complete and superficially touches everything you need to know about Machine Learning.
From setting up your own environments and quick Python reminder, it first explains how to use the three most well known libraries for Data Science: Pandas, Matplotlib and Numpy. It also covers Seaborn and other advanced plotting libraries. All this previous content is inside the first block, about Data Science.
The second block is dedicated to Machine Learning: An introduction is given and then a lot of supervised and non supervised algorithms are covered, like Linear and Logistic Regression, Support Vector Machines, Decision Trees and Random Forests, K-Means clustering and PCA.
Lastly we have a section dedicated to Deep Learning and to distributed Big Data Frameworks like spark. The course contains programming exercises in the shape of Jupyter Notebooks and Capstone projects for each section.
Overall it is very very complete, and will give you a basic understanding of how Machine Learning works, you will end up feeling comfortable with the main Data Science libraries, and you’ll be able to see what happens under the hood of the most common ML algorithms.
Duration: The course contains 25h of on-demand video. We recommend doing from 1 to 2 hours whenever we can, and completing the exercises slowly and absorbing what has been learned.
If this course is complemented with other resources, by the end of it we will have reached a pretty solid knowledge of Machine Learning.
Prerequisites: Some previous programming knowledge is needed in order to complete this course comfortably.
Free course: Like many others this course is free if you don’t want a certificate!
Description: This course, tough by the famous Andrew Ng, one of the top personalities in Machine Learning, covers a wide range of topics on Machine Learning, and is probably enough to get you up and running building your own projects.
Supervised and Unsupervised algorithms are taught, along with best practices about train/test division, bias-variance trade off, and the general state of Machine Learning and AI.
It is a very good, well explained course, and even people with experience in the field will enjoy it.
Duration: The duration of the course is of 54h approximately. In about a month and a half or two months it can be completed with not excessive dedication.
Prerequisites: it has no formal prerequisites, but some basic algebra and maybe some calculus expertise would make the course easier.
Paid Course: This is one of those Coursera specialisations that can not be audited; it is explicitly paid, however, it is one of the most renown Machine Learning Courses online.
Description: This specialisation is made up of 5 individual courses: An Introduction to Neural Networks course, a course about hyperparameters, regularisation and optimisation, a course about structuring Deep Learning projects, a course on Convolutional Neural Networks (CNNs) and a final course on Recurrent Neural Networks (RNNs).
Like the previous one, it is taught by Andrew Ng. If you have taken the Machine Learning course by Stanford, completing the Deep Learning specialisation will feel easy, and make you carry on with the flow of learning.
It has a similar structure to it: short videos, quizzes and programming exercises at the end of each week. The 5 courses in the specialisation, take you from knowing very little about Deep Learning and Artificial Neural Networks to being proficient.
Again, the explanations are very neat and easy to understand, and a lot of practical examples are given. Clearly, one of the best Machine Learning Courses out there.
You can find a full review of the Specialisation here.
Duration: As it is made of 5 different courses this specialization is quite time-demanding. With a 1h/day dedication it will take from 3 to 4 months to complete, but the outcomes, we think, are completely worth the effort.
Prerequisites: You have to have a decent grasp of programming in Python and fresh algebra and calculus concepts. If you have some previous experience in Machine Learning then the courses will be a lot easier and you will make the most out of the specialisation.
Free Course: Despite being part of the Data Analyst Nanodegree, this course can be taken for free with an student support forum and interactive quizzes. However, it does not have project reviews, mentorship or certification.
Description: The Introduction to Machine Learning Course from Udacity is an introductory course for Data Scientist or Data Analyst that want to learn to implement Machine Learning procedures. Its focused on using Python and Scikit-Learn to implement the different models and algorithms, so being familiar with Python is necessary.
You will learn about the most basic Machine Learning models: Naive Bayes, Support Vector Machines, Decision Trees, K-Means, AdaBoost, and even some outlier detection techniques, however there is nothing related to Artificial Neural Networks or Deep Learning. This is because it is an introduction to the main concepts of Machine Learning for those who already have some programming experience. If you want to learn about Deep Learning, we suggest the Deep Learning specialization from Coursera.
Each section of the course (there are 10 main sections) consist of a series of videos and quizzes, followed by a mini-project (what we enjoyed most) that gives you the chance to implement what you learned in code. While the projects are cool for learning, as they are not guided or reviewed, we might feel like we can skip them and carry on with the course. Our recommendation is to at least try to reach a mini-project each time, with a clear final goal, and not stopping until such goal is reached.
This course prioritises a high level understanding and giving experience with various tools and frameworks over the theory of Machine Learning. While you will not be an expert in any topic by the end of this course, you will be exposed to many of the most important topics of Machine Learning and have an understanding of how they work.
In conclusion, it is a good first step to start your journey towards Machine Learning mastery.
Duration: It is a self-paced course, which can be completed in one or two months with moderate effort. It will take more or less depending on how much effort you put on the mini-projects.
Prerequisites: Basic Python programming and maths skills are required for this course. Check out our Data Analysis books, they are a great way to prepare for this course.
Free course: Like many others this course is free if you don’t want a certificate!
Description: This course is a non-technical course, oriented to business owners or managers that want to learn all the different terminology around AI, what it can and cannot do, and how to incorporate Artificial Intelligence and Machine learning into the strategy and vision of a company to enhance and improve business results. It is a brilliantly delivered course that contains all the most important stuff to help non AI experts orient themselves.
You will also learn how to build an AI team, and how to make it work to give your organisation the best possible results. Lastly, you will know how AI can be used to solve problems in your organisation or how you can use AI to create new applications and services, or incorporate them to your already existing processses.
From startup creators to business owners, this is the perfect course for learning how to leverage Artificial Intelligence to your benefit. It is taught by the brilliant professor Andrew Ng, creator of Coursera and a wide variety of technical courses on the platform, however this time he drives away from any maths or statistics to focus on the influence and impact on the industry of Artificial Intelligence, technology he claims to be ‘the new electricity’. Enrol on the course to find out why!
Duration: It is not a very long course. On the official website it is reported as taking 4 weeks to complete, however if you watch a series of related videos in one day, and cover one topic per day you can finish it in less.
Prerequisites: No real previous technical knowledge is required. Anyone who is curious about AI can benefit from this course, however it is oriented towards those who want to learn to incorporate Artificial Intelligence into their business. Despite of this, if you want to understand what is said in the papers and the news about AI, be comfortable having conversations about it, or just introduce yourself to it, this is the perfect course!
Paid Course: This course can be found on Udemy with discount for about 10€.
Description: It presents an introduction to how to leverage Google Cloud Platform’s different services (Big Query, Storage, AI Platform) to train and put machine learning models into production, along with a brief explanation of Tensorflow and its main components and an intro to Artificial Neural Networks. Read the full review here.
Duration: It is a very short course which can be completed in a week with little effort.
Prerequisites: There are no real prerequisites but a bit of Machine Learning knowledge and Python knowledge is recommended.
Free course: Like many others this course is free if you don’t want a certificate and don’t mind missing out on some of the practical exercises, however we recommend a subscription to Coursera to fully enjoy all the perks it offers.
Description: This course covers the techniques you need to know to build an end to end Machine Learning pipeline and go from idea and data to value. It does so using the R programming language and libraries like Caret. You will cover all the steps from data collection, and preparation to building models using algorithms like regression, Naive Bayes probabilistic models, or ensembles.
The reason why we advice to spend your money on a subscription is that this course is highly practical, and contains a lot of exercises: it does not only cover the theory, but it gives a great emphasis on taking the theory that you have learned and transforming it into a practical application that can be used somewhere.
It contains a final project that can be a very good addition to your Data Science portfolio. Despite the course covering a lot of material, we do feel like it could be updated and increased in content, as in some sections there are additional tips that could be offered. It should be taken as an introduction to building real Machine Learning models in R, after which you will be ready to start building your own little projects or tackling Kaggle competitions.
Duration: As we mentioned above, the course is quite brief and self contained, with approximately 10 hours of content (some extra time should be added to this in order to complete the exercises and the final project)
Pre-requisites: There are no formal pre-requisites, but you will make the most out of it if you have some previous theoretical knowledge of Machine Learning, and also some R programming knowledge, otherwise, it might take you a while to grasp the concepts and advance with ease.
Free Course: Like many courses on edx, this course is free if you don’t want a certificate.
Description: Also known as ‘Learning from Data’, this course goes into the maths and the insights behind the models and is very heavy on the theory behind Machine Learning. It differs from Andrew’s Ng Machine Learning course, which quickly covers the maths and dives more into the intuition and practical applications in that ‘Learning with Data’ goes way deeper into the guts of the algorithms, and that is a great precursor to more practical ways of learning with books like ‘Python Machine Learning‘ or ‘Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow‘. Despite being sold as introductory, it is a challenging course that provides an intricate and deep understanding of the different elements that make up machine learning models and algorithms. It has a companion book that you can find here: Learning from Data - A Short Course with all the material that is covered in the MOOC, and further resources.
Overall it is a very theoretical course, that we think is great for gaining expertise on how Machine Learning models and algorithms work under the hood.
Duration: It is a moderately long course, with in between 10 and 20 hours of dedication weekly for 10 weeks, which has instructor companionship and can therefore only be taken in certain time-frames.
Pre-requisites: Some previous statistics knowledge is recommended before taking this course, as well as some linear algebra and calculus. You can find great statistics courses on our Statistics category, and also Algebra and Calculus on our Tutorials Section. Also some previous Python Programming knowledge is recommended. Find awesome Python books here.
You can find a video resume of the course here:
We hope you liked our section on Machine Learning courses. Check out some great books to complement these in our Machine Learning Books section. Cheers!