If you’re interested in leveling up in the field of AI, check out these courses:
Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!
What you’ll learn
- Build an AI
- Understand the theory behind Artificial Intelligence
- Make a virtual Self Driving Car
- Make an AI to beat games
- Solve Real World Problems with AI
- Master the State of the Art AI models
- Deep Q-Learning
- Deep Convolutional Q-Learning
AI For Everyone (Taught by Andrew Ng on Coursera)
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take.
In this course, you will learn:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can–and cannot–do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business asects of AI.
Learn essential Artificial Intelligence concepts from AI experts like Peter Norvig and Sebastian Thrun, including search, optimization, planning, pattern recognition, and more.
Learn Foundational AI Algorithms
3 months to complete
Learn to write programs using the foundational AI algorithms powering everything from NASA’s Mars Rover to DeepMind’s AlphaGo Zero. This program will teach you classical AI algorithms applied to common problem types. You’ll master Bayes Networks and Hidden Markov Models, and more.
This program requires experience with linear algebra, statistics, and Python (including object-oriented programming).
Artificial Intelligence (MIT OpenCourseware)
This course introduces students to the basic 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.