Popular AI courses

AI courses are important for a number of reasons:

1. AI is a rapidly growing field with many exciting developments and applications. Taking an AI course can help you stay up-to-date with the latest developments and understand how they may impact various industries.
2. AI courses can provide you with the knowledge and skills needed to pursue a career in AI. Many job openings in AI require a strong foundation in the field, which can be gained through coursework.
3. AI courses can help you gain a competitive edge in your current or desired career. Many businesses and organizations are looking to implement AI technologies, and having a background in AI can make you a valuable asset.
4. AI courses can help you develop problem-solving and critical-thinking skills. AI involves designing algorithms and models to solve complex problems, which requires a strong ability to think critically and creatively.
5. AI courses can help you learn how to apply AI techniques to real-world problems. By working on projects and case studies, you can learn how to apply your knowledge to practical situations and make an impact in your industry or field.

Here are 20 AI courses and training:

1. Coursera: “Deep Learning” by Andrew Ng (https://www.coursera.org/specializations/deep-learning)
2. edX: “Artificial Intelligence (AI)” by IBM (https://www.edx.org/learn/artificial-intelligence)
3. Udacity: “Intro to Artificial Intelligence” (https://www.udacity.com/course/intro-to-artificial-intelligence–cs271)
4. Stanford University: “CS 221: Artificial Intelligence: Principles and Techniques” (https://cs221.stanford.edu/)
5. MIT: “Introduction to Deep Learning” (https://www.edx.org/course/introduction-to-deep-learning)
6. Fast.ai: “Practical Deep Learning for Coders” (https://course.fast.ai/)
7. Harvard University: “CS 207: Introductory Applied Machine Learning” (https://isites.harvard.edu/course/cs207)
8. DataCamp: “Deep Learning in Python” (https://www.datacamp.com/courses/deep-learning-in-python)
9. DataCamp: “Introduction to Deep Learning with Keras” (https://www.datacamp.com/courses/introduction-to-deep-learning-with-keras)
10. DataCamp: “Introduction to Artificial Neural Networks with Keras” (https://www.datacamp.com/courses/introduction-to-artificial-neural-networks-with-keras)
11. Coursera: “Neural Networks and Deep Learning” by Andrew Ng (https://www.coursera.org/course/neuralnets)
12. edX: “Artificial Intelligence (AI) Capstone” by IBM (https://www.edx.org/course/artificial-intelligence-ai-capstone-by-ibm)
13. MIT: “Artificial Intelligence” (https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/)
14. Stanford University: “CS 229: Machine Learning” (https://cs229.stanford.edu/)
15. Carnegie Mellon University: “Introduction to Artificial Intelligence” (https://www.ai-class.com/)
16. Fast.ai: “Computational Linear Algebra” (https://www.fast.ai/2017/07/17/num-lin-alg/)
17. Coursera: “Machine Learning” by Andrew Ng (https://www.coursera.org/learn/machine-learning)
18. edX: “Introduction to Artificial Intelligence (AI)” by IBM (https://www.edx.org/course/introduction-to-artificial-intelligence-ai)
19. MIT: “Deep Learning for Computer Vision” (https://www.edx.org/course/deep-learning-for-computer-vision)
20. University of Oxford: “Deep Learning for Natural Language Processing” (https://www.cs.ox.ac.uk/teaching/courses/2020-2021/dl/)