Isn’t it amazing how accurately the Uber app calculates the price for your ride or how the autopilot feature on the commercial airplanes flies the craft with conviction? Well, that is the beauty of artificial intelligence and machine learning.
Most of the smart features you find on your mobile apps are powered by some version of AI, and if you are fascinated about this area of study, there are a lot of places where you can find insightful material on AI and Machine Learning. Well, here are 12 resources where you can get training material for AI and ML without spending any money.
Learn with Google AI:
Google, one of the biggest companies in today’s date, has launched their magnificent resource as an initiative to help the general people have a better understanding of Artificial Intelligence (AI). On one side, they have these advanced projects like TensorFlow, while on the other side they have more playful projects like a machine vision and the cat doodles, which are meant for the practical demonstration of AI projects.
Google currently foresees the “Learn with Google AI” site as an open source of knowledge for machine learning (ML) and artificial intelligence, where all kinds of AI enthusiasts – starting from researchers to beginners can get the necessary study help on the core concept of ML and AI. In this manner, they can develop their skills regarding these two technologies, and understand the real-world application of these technologies.
People who are looking for some in-depth learning on the subject of AI and Machine Learning, they can avail that through Udacity, even though the course is mostly designed by Google. At Udacity, you have a number of options. For instance, there is “Intro to Artificial Intelligence” – a four-month course and “Intro to Machine Learning” – a 10-week course, alongside “Deep Learning”.
“Deep Learning” by Google generally serves the seasoned engineers and data scientists as it talks about designing intelligent systems that learn from massive and complex datasets. Interestingly, the course was created in part by the principal scientist and the Brain team technical lead at Google, Vincent Vanhoucke. The Georgia Institute of technology also offers an advanced course in Artificial Intelligence for Robotics, which is similar to “Deep Learning” course.
Professor Patrick Henry Winston is in charge of the AI course at MIT. Even though it has been in practice for quite some time now, it still manages to remain one of the favorites of many AI beginners. This particular beginners’ course has more than 20 lecture videos, which cover the basic concepts of AI, problem solving and learning methods. Interestingly, the lecture videos are available on Apple iTunes as well.
Furthermore, MIT has taken one major real-world aspect of artificial intelligence and designed their curriculum, i.e. self-driving cars. As these self-driven cars are being considered a part of our everyday lives in the future, MIT tries to explore the specific technologies involved in this particular form of a vehicle through the courses. Apparently, AI plays a massive role in the functions of all the sensors and the safety navigations of the vehicle.
There’s something called MIT DeepTraffic simulator, which basically challenges the students to learn how the simulated car can go on the busy road without colliding with the other cars on the road. Bricks ‘n’ Mortar University had introduced this course last year. And all the lecture videos on this are available online. However, you won’t get any certification for that.
The CS229 – Machine learning course at Stanford involves supervised and unsupervised learning, theory learning, reinforcement learning as well as adaptive control. There are total 20 lecture videos that are available online which you can download. Besides, you can also try the assignments which designed to test your skills.
The CS221 – Artificial Intelligence: Principles and Techniques course is not actually free for all, but you can access some of its course materials at the Data Institute at University of San Francisco for free. This seven-week long course offers practical deep learning for coders through this course. People, who have a significant understanding of coding and can dedicate 10 hours a week to the classes, can do great in this course. Areas like how the computer vision model is created and the processing of natural language are covered in this course.
Columbia University – Machine Learning:
Unlike the AI course at Stanford, this Machine Learning course at the Columbia University is available online for free. However, you have the choice to pay for certification if you feel like having one. The course offers a basic understanding of the models, methods and real-world application of Machine Learning using probabilistic and non-probabilistic methods along with the supervised and non-supervised learning.
The materials of the course generally require 8-10 hours a week from the participant for over 12 weeks to get the best out of this course. And since it is a free Ivy League-level education, one can expect it to be a little tougher than the usual. Interestingly, the course is offered through the non-profit edX online course provider.
The web page of the CS188 – Intro to AI course at the University of California (Berkeley) offers around 25 links for lecture presentations and slides online. You need to sign up with edX though, especially if you want to work through the online homework assignments of the course. However, there is no such condition in taking part in the Pac-Man projects, where you can play games, encounter real issues and the modify code to solve those issues.
Apparently, the purpose of the Pac-Man projects is to allow the people to learn the concepts of AI in the form of informed state-space search, reinforcement learning and probabilistic inference. Another course at Berkeley, CS294: Deep Reinforcement learning is available for the AI and ML enthusiasts out there. Apart from all the slides and lectures, there is plenty of other materials you can access for free.
Coursera is already quite popular for its range of online course and a massive library of materials. Interestingly, it also offers an AI course as well. Here, you get the Artificial Intelligence (AI) by ColumbiaX, which involves the intro to AI and the brief history of the technology. Here, participants can learn how to solve the AI problems using Python.
Furthermore, you also get enough insight into the machine learning algorithms, how to build intelligent agents, and a thorough understanding of the natural learning processing, robotics and vision. The course is available for free. However, the students need to pay $300 to get the certificate after the successful completion of the course.
Kaggle is known for being one of the sophisticated platforms for predictive modeling. However, it makes it to the list of AI training resources since it offers analytics competitions that challenge data miners and researchers to create predictive models and data descriptions. Unlike most of the training resources in the list, here organizations sponsor challenges and offer prize money, which can go up to 1 million dollars.
Some of the latest Kaggle challenges include Passenger Screening Algorithm Challenge for the Department of Homeland Security, Planet: Understanding the Amazon from Space, and Zillow Price: Zillow’s Home value Prediction (Zestimate). Beginners are recommended to start with the Titanic: Machine Learning from Disaster tutorial.
Nvidia is a very common name in the field of visual computing technologies. However, you’ll be surprised to learn that it has also started its own Deep Learning Institute (DLI) to help the advanced learners (including developers, data scientists and researchers) get some experience on solving real-life problems with deep learning.
Nvidia has started the initiative in 2017 to enhance the skills of 100,000 developers by 2018. To ensure they achieve the target, the company released a number of free and cost-effective ($30) labs as support. The labs are available on the company’s Online Self-Paced Labs page.
The Data Institute at the University of San Francisco has its own certificate course on Practical Deep Learning for coders online through fast.ai. The seven-week long course is taught by the founder of Enlitic, Jeremy Howard. Similar to any other Deep Learning courses, fast.ai is also ideal for advanced learners.
The course requires 10 hours of class work every week from the participants. However, you can adjust the hours according to your schedule if you want. The course covers areas like computer vision models, exploration of natural language processing, and recommendation systems. The course is free. So you availing the course material won’t be an issue.
While most institutes are focusing on the “Deep Learning” courses, there’s University College London which is offering “Reinforcement Learning” courses to the tech enthusiasts. The institute offers concise course material, which has both lecture videos and supporting slides.
Headed by David Silver, this course focuses on some of the most exciting and actively-researched areas. Since it is constructed by one of the crucial members of Google DeepMind, one can expect a lot of knowledge from this course and its materials, which are available for free.
Even though Elon Musk has some harsh opinions about the whole AI scenario, the OpenAI Gym, which is co-founded by Musk himself, offers a playground for “developing and comparing reinforcement learning algorithms.”
The participants use Python and other frameworks like TensorFlow to write algorithms and then share the results to get feedbacks. The initiative basically offers a tutorial to get started with the concept of AI and tries to teach the participant about the technology by involving them in games and letting them control simulated robots.
While you can visit these online resources and avail the necessary knowledge of AI and ML, there’s another free and open platform that shares useful insights on these particular areas. The platform is none other than YouTube. There one can find a plethora of information, tutorials and even study materials on AI and ML from a variety of sources. In fact, YouTube can be your best bet if you are willing to learn a variety of issues about AI and ML under one umbrella.
Author bio: Donald Trevor is a professor of Computer Science who has currently joined MyAssignmenthelp.com as a paperhelper. Trevor has earlier served as a professor in numerous colleges. Now he uses that experience to help students with projects.