Скачать или смотреть Andrew Ng - AI for Everyone - COURSERA Review

Link to Course:
https://www.coursera.org/learn/ai-for...

Here is my review of the "AI for everyone" course by Andrew Ing on coursera.
This is beginner course that serves as an introduction into what is artificial intelligence and how it can be used in practice, with particular focus on the business world.
The course is designed for 4 weeks with very light load. If you are super committed and properly caffeinated you can probably get through it in one day.
If you want the certificate of completion you have to pay $50. I would say if you make good salary you might as well get the certificate since the cost is insignificant compared to the value of your time.
If money is tight (e.g. you are a student), definitely audit the course for free. You will get just as much out of it.
After each week there is a quiz, which is very easy to pass if you were paying attention.
There is absolutely no coding in this class, if you are currently already working on machine learning projects this is not for you.
If you are a machine learning person, this is a great course to recommend to people who want to work with you so they can get up to speed. I think in a few years a course like this will be part of general training curriculum at most big companies since it will be business critical for most employees to understand what AI can and cannot do. It will just as important as management courses are today.
Week 1:
General concepts and definitions like narrow AI vs AGI. What is machine learning, what is supervised learning. Also there is a lot of focus on data, which is critical to get any ML to work well.
Week 2:
This is where the business focus of the course is evident.
During the whole week it talks about how to structure a AI project. This is very nicely done since it allows non-AI people to start thinking at a high level about that is needed to practically execute an ai project. It assumes that the technical parts of the project (e.g. coding) will be done by a different department either internally or externally.
Week 3:
In week 3 Andrew presents couple simple case studies so you can see how an AI project would work in practice. There is also an explanation of different roles that might be needed in AI projects. I find this super useful and this was probably the only thing I personally learned from this course. The roles might be different from company to company but this terminology makes sense
Software Engineer
Machine Learning Engineer
Machine Learning Researcher
Data Scientist
Data Engineer
AI Product Manager
Week 4:
The content in Week 4 is probably the least useful from a practical standpoint but it raises few very important issues such as bias in AI and adverse use of AI.

In some part of course is little outdated. Andrew mentions that to generate cohesive text output is difficult for AI. That is clearly not the case anymore since the release of language models like GPT-3 and Chat-GPT.

Overall highly recommended for people just getting into AI.