The Best MIT Online Resources for You to Learn AI and Machine Learning for Free
Studying at MIT can be very expensive, but currently, more than 200 courses are available for free, and here you have a list of some of the most relevant AI and Machine Learning courses to begin.
MIT is one of the world's leading centers of study and research in science, engineering, and technology. Founded in 1861 in Cambridge, USA, the institute trained professionals to meet the demand of industries growing fast. Only in the mid-1930s did MIT focus its training on basic scientific research and technological innovation.
Studying at MIT is expensive, around $ 60,000 a year, and its average scholarship for international students is the US $ 32,000 — but it can reach 100% in some cases. On average, 62% of its students receive financial aid. In graduate school, almost 90% of students receive some scholarship. But there is another way to be part of it?
More than 200 courses are available — many of them in science and technology, but with options also in economics, business, history, biology, sociology, and others.
Since 2008, the institution started to incorporate videos into online courses, and, currently, more than 100 courses have completed video classes with teachers from the institution. But our focus here will be AI and Machine Learning courses.
Why should you learn AI and Machine Learning?
Artificial Intelligence (AI) and machine learning — its main component today — are two of the most recurring themes regarding innovation. However, although most approaches are rich and positive, there is still a little exaggeration in the expectations of the results. And sometimes even suggestions for applications where AI would not necessarily be the best option.
Artificial intelligence is not a trend. It is a fact that there is still a little exaggeration in forecasts for the future.
However, a recent cycle of advances in algorithms and computational infrastructure has generated commercially relevant results.
Today, even professionals with other backgrounds, such as designers, developers, testers, and product owners, can and should have a broad view of AI.
In this way, they can think about the day-to-day in each of their projects, how to incorporate these concepts, advise customers about it, and generate more value for all involved.
I believe that many professionals outside the exact sciences can and should learn the basics of AI to increase their potential contribution, either to their employer or to our society.
Are you ready?
But before you jump into the MIT online learning experience, I would like to invite you to watch this exciting lecture by professor Jeremy Kepner and Vijay Gadepally from the MIT Lincoln Laboratory that will provide an overview of artificial intelligence and take a deep dive into machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
Now you can go to and find the best training course by MIT that will help you to build your deep expertise in AI and Machine Learning:
Artificial Intelligence
This course introduces students to the essential knowledge representation, problem-solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem-solving, and learning in intelligent-system engineering; and appreciate the role of problem-solving, vision, and language in understanding human intelligence from a computational perspective.
Introduction to Machine Learning
This course explores simulation and prediction concepts, algorithms, and machine learning applications. It involves formulating learning problems, representation definitions, overfitting, and generalization. These principles are exercised in supervised learning and reinforcement learning, applying to images and temporal sequences.
Machine Learning
This course introduces machine learning that provides an overview of many machine learning principles, techniques, and algorithms, starting with classification and linear regression and concluding with recent issues like boosting, supporting vector machines, and hidden Markov models Bayesian networks. The course will give students the fundamental ideas and intuition behind current machine learning methods and a more systematic understanding of how, why, and when they work. The underlying theme is statistical inference since it provides the basis for most of the methods covered.
Introduction to Computational Thinking and Data Science
This course is for learners with little to no programming experience.
It aims to offer students an understanding of computing's role in solving problems and helping students write small programs.
Using Python 3.5 programming language.
Techniques in Artificial Intelligence (SMA 5504)
A graduate-level guide to artificial intelligence. Topics discussed include first-order representation and inference, current deterministic and decision-theoretical planning techniques, simple supervised learning methods, and Bayesian network inference and learning.
Mathematics of Machine Learning
Machine Learning refers to automatic pattern recognition in data. As such, new statistical and algorithmic advances became fertile ground. This course aims to provide a mathematically rigorous introduction to these developments, emphasizing methodology and interpretation.
Introduction to Deep Learning
MIT's introductory course on deep learning approaches with computer vision, natural language processing, genetics, and more! Students gain basic knowledge of deep learning algorithms and experience developing neural networks in TensorFlow. The course ends with a project plan competition, input from staff, and the industry sponsors panel. Preconditions presume calculus (i.e., derivatives) and linear algebra (i.e., matrix multiplication)
Machine Learning for Healthcare
This course introduces students to healthcare machine learning, including the essence of clinical data and machine learning techniques for risk stratification, disease progression modeling, precise medicine, diagnosis, subtype discovery, and clinical workflow enhancement.
Deep Learning for Self-Driving Cars
As with the course above, MIT uses a significant real-world feature of AI as a jump-off point to explore the particular technologies involved.
The self-driving cars widely expected to become part of our daily lives rely on AI to make sense of all data hitting the vehicle's sensor array and safely navigate the roads. This includes training computers to interpret sensor data like our brains to interpret eye, ear, and touch signals.
It will teach you how to use the MIT DeepTraffic simulator, which challenges students to prepare a virtual car to travel along a busy road as quickly as possible without colliding with other pedestrians.
This is a course taught at the bricks' n' mortar university for the first time last year, and all resources, including lecture videos and exercises, are available online — but you won't get a certificate.
Conclusion
Through these fantastic pieces of training, you will have the opportunity to develop your skills in how artificial intelligence and machine learning methods work under various circumstances. In addition, of course, you will appreciate the relevance of problem-solving, vision, and language in understanding human intelligence from the AI and Machine Learning perspectives.
Being part of MIT (the online experience is very inclusive), you'll be part of a community of restless learners, enthusiastic dreamers, and extraordinary doers. And with this list of training I've shared here, you get all this for free.
Bonus
Enter Julia, a rising start on AI development.
Frederik Bussler recently wrote an interesting article about MIT's training course that will teach you Julia, the rising star among the programming languages that could replace python as the favorite language for AI and Machine Learning development.
In the article, Frederik mentions that MIT recently announced a free online course on computational thinking, taught using Julia. I think you should look into Introduction to Computational Thinking with Julia, with Applications to Modeling the COVID-19 Pandemic. A half-semester course introduces computational thinking through data science applications, artificial intelligence, and mathematical models using the Julia programming language. This Spring 2020 version is a fast-tracked curriculum adaptation to focus on applications to COVID-19 responses.
Discovering a brave new planet
The second bonus is not a training course, but it is a podcast by MIT that caught my attention this week:
Hosted by scientist Dr. Eric Lander, president and founding director of the Broad Institute of MIT and Harvard. He is a geneticist, molecular biologist, and mathematician who led the Human Genome Project and served as President Obama's White House science advisor for eight years.
Brave New Planet is a podcast about remarkable new technologies that might dramatically improve our world, or if we don't make wise choices, it could leave us a lot worse off. It delves deep into the most exciting and challenging scientific frontiers, helping us understand them and grapple with their implications.
Other Articles you may want to read.
- These 9 Research Papers are changing how I see Artificial Intelligence this year.
- The most impressive Youtube Channels for you to Learn AI, Machine Learning, and Data Science.
- These are some of the best Youtube channels where you can learn PowerBI and Data Analytics for free.
- What is the MIT MicroMasters Program?
- 5 companies that are revolutionizing recruiting using Artificial Intelligence.
- 5 amazing books about AI that you must read.
- The Best MIT Online Resources for You to Learn AI and Machine Learning for Free.
My books about Artificial Intelligence
Also, I've just published other interesting ebooks on Amazon, and I'm sure some of them may be interesting for you… let's keep in touch, follow me and do it together.
A.I. in 2020: A Year writing about Artificial Intelligence
AI, Robotics and Coding (for Parents): A practical guide for analog parents with digital kids
The Terminator paradox: How neuroscience can help us to understand Empathy and the fear of Artificial Intelligence
Artificial Intelligence from A to Z: Demystifying the essential concepts of AI.
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