AI For Everyone

share ›
‹ links

Below are the top discussions from Reddit that mention this online Coursera course from DeepLearning.AI.

AI is not only for engineers.

Workflow of Machine Learning projects AI terminology AI strategy Workflow of Data Science projects

Reddsera may receive an affiliate commission if you enroll in a paid course after using these buttons to visit Coursera. Thank you for using these buttons to support Reddsera.

Taught by
Andrew Ng
and 8 more instructors

Offered by

Reddit Posts and Comments

1 posts • 26 mentions • top 22 shown below

r/learnmachinelearning • post
204 points • sercosan
Coursera: AI For Everyone (with Andrew Ng) is finally open.
r/datascience • comment
8 points • LeFlyingDragon

For lazy people 😂

r/ProductManagement • comment
3 points • jackwghughes

I am the PM at a cyber security company where ML forms (actually forms) quite an important part of our product. We encourage all our staff to take this course and our data science team proves support.

AI for Everyone Coursera course (

It’s a really good entry level course and the guy who leads the course Andrew Ng is excellent.

He also runs slightly more advanced courses for more technical people of this entry level course is too simple for you.

Here are some details that I shared with my team summarising the course above.


Is the course suitable for everyone?

Yes the course is not technical and open for all.

How long is the course?

The course is 4 weeks long It should take around 6 hours to complete Each weeks course involves watching a short video (50-90 mins then completing a short test)

How much does the course cost?

The course can be taken for free if you select the audit only option when you enroll. If you wish you can pay to be accredited for the course and the cost is £37. You can decide to accredit the course after you have completed the free version if you want.

When do I need to enroll?

Enrollment for this course starts (4/3/19) but you can enroll afterwards.

r/cscareerquestions • comment
2 points • smash_teh_hamsta

you might find this useful:

Personally, I'd advise you against being prescriptive regarding the tech stack -- if you hire the right people they should be trusted to make that decision. Plus, that flexibility might actually help you better attract talent.

As for team comp --- you didn't mention what your expertise is. I presume you are an expert in trading, or if you are not, that a rather large skill gap in your team composition right there.

Anyway, I'd imagine that your best shot is to start with a programmers that have dabbled in ML as opposed to hardcore PHD ai types (e.g Machine Learning Engineers / data engineers). I say this because you are going to need someone who can actually build the bot. And moreover before the AI guys can build useful things you are going to need data. With this said, refer back to the 2nd paragraph; don't be too perspective, instead find people who know how to build teams.

r/ProductManagement • comment
2 points • oantila

The AI for Everyone course is pretty good for business/product folks:

r/datascience • comment
1 points • prasannas0501

Check this course AI For Everyone

r/artificial • comment
1 points • CyberByte

You might be interested in Andrew Ng's AI for Everyone course.

r/datascience • comment
1 points • jovianml

AI for Everyone by Andrew Ng would be great place to start.

r/MLQuestions • comment
1 points • PeakNeuralChaos

How about Andrew NG's "AI for Everyone" course? It's aimed at business people and focuses less on math and implementation and more on organizational aspects. You can find it here. There are similar courses on Coursera or other mooc platforms as well.

r/MachineLearning • comment
1 points • delunar

That's what Andrew Ng's new course is aimed at! Take a look:

r/brasil • comment
1 points • dt25

Até os do Coursera? Lembro que já vi alguns introdutórios mas não entrei pra ver...

r/ProductManagement • comment
1 points • chakrvyuh

May I ask you whats your goal behind wanting to gain foundational knowledge about ML/AI?

  1. Do you want to hold a conversation with others about state of the art?
  2. Do you want to become a PM in ML/AI space?
  3. Do you want to build a product powered by ML/AI?

For #1. Start with Andrew NG's machine learning course

For #2&3 you'll need to have a much deeper understanding and you'll need more time. (I would start with #1 and then look at

r/MLQuestions • comment
1 points • python_souls

Well to be honest it is not that small...We are in various sectors from healthcare, real estate, nursing education to hotels and tourism. My high level goal is to use A.I. to:

1) To make it easier to manage these companies

2) Find an edge over my competitors by making my internal processes more optimized

However, since I am still inexperienced in this field, I don't know how to identify areas where I might employ A.I. to achieve these goals while further increasing my knowledge over A.I. I was thinking about taking this course:

to help me understand how to apply my knowledge.

r/learnprogramming • comment
1 points • chris1666

I agree with going for CS, or security, you can always add certs specific to AI from cousera, edx , goggle or whoever, ,, also never good idea to take on momentous courses that might drop your gpa or sidetrack your degree.

r/ProductManagement • comment
1 points • cbsudux

As an MLE myself there's a good course by Andrew Ng I'd recommend - This is specifically meant for non AI folk at companies to get a hang of what AI is. No math, no bs. Just what goes in and what the impact is wrt to business output.

You can skim through this in a few days.

Do not go through his other ML courses or as someone else mentioned Those are math heavy and you'll get lost in them.

r/MachineLearning • comment
1 points • art12400

They should have started with this course:


(my summary of it:

r/datascience • comment
1 points • trnka

I lead a data science team and work with PMs daily. We're in a healthcare tech company and use machine learning to make primary care easier for doctors.

The biggest risk factors in applied machine learning:

  • Building something that doesn't actually help the business. Ways to mitigate this risk:
  • Understand company KPIs/OKRs better than anyone else and practice explaining them. What motivates those KPIs? Which ones can we deprioritize if needed?
  • Push to get more feedback from end users, earlier and more frequently. If possible get your DS team talking to the end users. Help cultivate empathy. Release projects in pieces and get feedback ASAP. Build demos and get feedback ASAP. Get feedback via mockups when possible. DO NOT take project plans too seriously before you get feedback from users.
  • PM or execs asking data scientists to do something too hard or impossible but they may not speak up. Ways to mitigate:
  • Make sure the DS team feels safe speaking their mind. Invite some of them for drinks now and then. Or afternoon coffee. Build social relationships. Make it easy for them to speak their mind and thank them when they challenge you. Some of them may be quiet and just need a little help to communicate effectively.
  • If they don't seem engaged with an idea, ask about that head-on and listen
  • Focus on the goal you're trying to achieve, not the implementation - if they understand the goal they can often find a solution that's possible. But if you're handing them a specific implementation, that often goes badly
  • PM-DS relationship issues:
  • Don't do drive-bys for BI requests. Demonstrate that you're willing to try and answer your own data questions.
  • Help them to get the data they need, when possible. That can sometimes be simple, like getting a spreadsheet from another team once a month and making sure that the column names are identical every time. Or small things like "team B has a free week on the schedule and you've been complaining about the data quality - I'd like to get you together and see if there are any small improvements we could make"


Links that I share with our PMs:

r/learnmachinelearning • comment
1 points • piquiblanco

I think it might be too early for you to actually study machine learning. Math prerequisites are extremely important and 'math in school' won't cut it. From your perspective it's a very narrow subdomain of knowledge, which can be mastered only after you go through the math, I'm afraid. I don't mean to discourage you. I don't know how much time you have to study every week, but unless you treat pursuing things that interest you as a full-time job, then it will be very hard to learn web development, machine learning and whatever comes in between.

That being said, I have some recommendations. First one is a book. It's a nice high level introduction to machine learning techniques. You should be able to follow it easily even without solid maths foundation. The other recommendation is a course: AI for Everyone. Andrew Ng is one of the machine learning most known popularizers. His other course, Machine Learning, is a starting point for many people trying to find out what machine learning is, but it expects some math intuition from you.

Good luck!

r/financialindependence • comment
1 points • Gibson19

100 percent this. Typically a data scientist is gonna have some sort of mathematics background. That's not for everyone. Its why they get paid 6 figures fresh out of college (often Masters/PhD level graduates). But there's still a huge gap between the average business analyst and a data scientist, and its filled by data analysts.

So if you ignore the more advanced aspects like AI/ML, NLP, hell even big data or non relational databases. Understand the core concepts of doing data analysis you'll likely carve a pretty good role for yourself. I've sat on teams of analysts and been a hero for being able to run simple SQL queries.

My curriculum for data analysts would start with a heavy focus in:

  • ETL. Extract/Transform/Load.. essentially prepping the data for analysis

  • Data Visualization. Lots of UI friendly point and click options that you can apply best practices too without advanced statistical modeling.

  • Excel. Not so much because its the ultimate analysis tool. But it can do a lot and can give one a good sense of creative problem solving in both prepping data and running analysis. Lots of novel plugins as well like Fuzzy matching, and the analysis toolpak

Once you're comfortable with those, then you can explore R and Python (numpy, pandas, etc..). Learn how to apply your critical thinking/problem solving to the huge libraries of code that exist in those tools.

A few courses I just quickly snagged from Coursera that would be good for a newbie:





r/CanadaPublicServants • comment
0 points • trngoon

Do you mean like data scientists?

My manager took both of those. To get an understanding of the field to at least a degree.

If you didnt meant data scientists then disregard.

r/ArtificialInteligence • comment
1 points • HelenKandelaki

I think it's a great idea to start thinking about leveraging AI for your business from the beginning.

Here are some resources that can help you get familiar with AI:

  • McKinsey’s guide to AI for executives: in simple language explains AI, machine learning and deep learning.
  • AI for Everyone course by Andrew Ng, the co-creator of Coursera and a founding lead of Google Brain.
  • Google AI: tech giants’ artificial intelligence research and development branch that provides stories, courses and publications on AI and its advances.
  • Accenture’s AI Explained guide for executives: aims to provide a comprehensive overview of AI capabilities and implications.
  • Udacity’s Intro to Artificial Intelligence course: teaches the fundamentals of AI and its potential applications.

I work for an AI consulting firm and we've recently started a blog that aims to educate and demystify AI and its implications for business owners. Our most recent post actually talks about ways to successfully integrate AI into a business.

r/ProductManagement • comment
1 points • dodgyb

\>>How do I quickly learn about DS and their role + output?

Andrew Ng has just released a course which may prove instructive

And a quick overview:

There are lots thoughts on what DS do all day, a definition I came across recently is DS produce analysis to inform business decisions, ML Engineers build working models in production environments. Best hold individual discussions to define roles and work out how they want to develop them.

n.b. follow

\>>I’ve oddly managed (people) engineers before, should I (people) manage them similarly?

Difficult to define as you are taking on an existing team. Find out from them what works and what doesn't, aim to improve incrementally. There is a lot to digest from this series of posts, but well worth the effort:

\>>How do I manage their work/stories/chunks of work?

and as a very interesting counterpoint, Paco Nathan explores the role of curiosity in data science work

\>>How do I understand how they interact with back-end systems, APIs, front-ends, etc?

There must be documented DS process that includes a focus on reproducability of research. This will also help with onboarding new hires. Some ideas:

\>>Is there an opportunity to change how DS are typically treated and utilized?

I guess this depends on organizational maturity.

\>>Which organizations are known for leverage DS well? (And document their culture + principles)

See MS TDSP above

\>>Why does this seem like a great new challenge (like the one I was seeking) and simultaneously my “first day of college” again?

This is a pointless concern, don't give it a second thought, just climb on-board and enjoy the ride.

\>>I’m clearly forgetting dozens of things, care to thrown tips, articles, and general ideas at me?

I hope these links will give you lots of food for thought. But a proverb for you:

'He who asks a silly question is a fool for 5 minutes. He who doesn't is a fool forever'.

Finally, aim to be a servant leader.