the subtle humor in between makes the course so much more engaging.. One of the greatest instructors i've ever seen..i can't believe i completed a 1.5 hr video in one go.. kudos to Cassie, admirable job..
Wow I'm joining Google next month as a intern & have always wanted to pursue ML but was afraid that I might not be competent. This video really encouraged me. Quote "if you're too afraid to start you tend to not do well in applied machine learning". Ur presentation was really great and spot on. Thanks for the dedication in making it easier for us.
You know its great when after reading all the other stuff on ML and endless online material out there that this is the one that gives you that feeling of the "the penny has dropped" and "aha" moment in your understanding - great course. If you are new to ML or even have started on your journey, starting here will give you a great foundation to build upon your knowledge. It's even a great place to revisit concepts to refresh yourself. Thanks Cassie for sharing this with the world and help others in their journey.
This is marvellous! Cassie, I'm in the middle, but already remember more about basics than after 5 previous ML courses I've already did. And it's funny. And I love how you talk about this stuff. First time in years I want to watch more about ML and I don't feel bored after 15 minutes. I will definitely watch all your lectures and finally apply all the knowledge that I'm gaining here. Because what I am missing is the flavour and a concept. I now the math for ML, but I lack the feeling, I think the most important part. Bravo Cassie! I'm so happy these lectures are available.
If every course or subject was taught like that. I'd be an A+ student. Excellent, fun, not boring at all, a joy to watch and learn from course. Thank you so much and please keep doing more of these 😁👌🙏
as a statistician who has been rather skeptical of the entire premise of 'artificial intelligence' i must say this lecture series got me interested in understanding a little more. i think i still have many many reservations which probably stem from familiarity with models which can serve both for prediction but also for inference. whereas with these models, it seems like we are trading away inference and even the ability to analyse the model behaviour in pursuit of better and better quality prediction. i think the work you are doing here to communicate that tradeoff is important. thanks for making this available for free.
I am absolutely impressed by the way you express all the fundamentals, it's just simple and powerful. Your analogies and the way you present them are beautiful. Thank you so much for your time and effort. I'll show this to all of my friends who wants to have some ideas how AI and ML work.
The UA-cam logic will most likely recommend to all of my friends in India especially in the IT field. Most likely this video will hit 10,000 views in another 3 months . This will work because UA-cam recommendation logic will do it's job like a charm
This is wonderful. Had to pause the video because after the ML definition all the previous examples she gave were just the ideas to explain the best possible definition. Very smart and now I need to review everything but so far very entertained yes, many technical topics well explained but need to be digested. So far loving it 😍
Love the approach of taking a complex topic and doing this for dummies version. Moving on to the next lecture … thank you for making these available for the wider world
I am really attracted to and motivated by your way of presentation, teaching, and making people see what is taken as hard become easy. You are amazing, Thanks for this talk.
I can say this is the best course for ML till now. Most of the folks doesn’t know / understand / realise there is two ML types research and Applied. As a designer I am tinkering with fusing design thinking and ML for past 3 years and I am just getting there to work up a process recipe so that anyone can become a wonderful chef & it start with understanding the business problem and user problem. This course helped me understand how what I am doing is super important in the realm of the things today and tomorrow. Thanks Cassie 🙏🏻🤟🏻
Really appreciate you making this public. A great presentation; really made me think differently about how we use data science in business. I've followed your posts on LinkedIn and just stumbled upon your UA-cam... I'm glad I did!
I really relate to the issue of people lacking the ability to apply concepts in the real world. After my MMathStat I felt like I learnt how to pass exams with little idea how to do these things in the real world.
Hey Cassie, Such a wonderful video. Thanks a ton again for making it available for public. I am watching it for third time :) coz i keep understanding more every time. One request if you could make it happen - there are irritating videos which keep popping in between, not sure if you can remove it. Thanks Again, Appreciate the super good content and your calm way of explaining concepts.
Incredible! You speak really well and use wonderful illustrations. Thanks for sharing (I found you by going through Google’s data analytics certification on corsera, so glad I dag through that one page about linkedin even though I didn’t need it lol)
Especially the kitchen one towards the end and how you differentiated between research and applied AI/ML. Good microwave engineers are probably horrible cook lol
Hi Cassie, @1:17:52 Given that at any point in time there are 10 empty spaces on average, X_bar = 990. Lets say there is 1000 trials of the technology and a requirement of 95% accuracy before the green light: With the strategy of full capacity, 1000 spaces occupied and the accuracy requirement of 95% given that X_bar = 990; there is only two ways that the strategy would be a winning strategy: i.e. what combination of the sample space(1000) will lead to X_bar = 990? The strategy needs to be right 95% of the time Therefore 1000*950 = 950,000 There is 50 sample spaces left in the trial Lets say there's a brilliant optimization technique employed and it outputs: 800 * 50 or (900*40) + (400*10) X_bar = ((1000*950) + (800*50))/1000 = 990 or X_bar = ((1000*950) + (900*40) + (400*10))/1000 = 990 So out of all the possibilities that will lead to X_bar = 990 there is only two ways given the strategy constraint. The problem therefore is the likelihood of the strategy given X_bar, it's too extreme! On learning, I think your student was right! Here's why: Children achieve mastery of their local language from an early age without ever sitting an exam. They're able to apply their learning directly in social interaction without testing because this is simply the structure of their social interaction. Would you agree to the possibilities of better alternatives?
One question: how AI will decide if the data is based on two opposite examples, such as the example of the Ethics of Kant (Deontological Ethics: shall say the truth always) and the Ethics of Benjamin Constant (Consequentialist Ethics: the truth only according to the consequence)?
@@kozyrkov First ever given, and first course of yours I didn't take live. I felt pretty cheap tbh, it's a great course, I've already applied it and I really appreciate your focus on practicality and keeping it engaging. Thanks for doing these!
I’m curious, how can we ‘improve’ our decision intelligence? You mentioned how that reliable workers (AI or ML) can scale up stupidity as well as intelligence, I really think that if I continue diving in this field I’m going to see exasperated people whom I thought I was going to impress with my answers lol. How do u suggest we improve at being better at decision intelligence?
@datascientific Hey Cassie, thanks for this class! In the parking lot scenario, would would the better question be to ask our system of not “if the space is empty or full”?
I'm really very confused. When you created your wine bottle chooser recipe, you plotted out your real life data of preferences after an undoubtedly memorial evening. Then you drew a line to separate "yes" from "no". So whatever relationship between your wine critic opinions and wine age that this line describes, there is your decision maker. I don't see where there is a "black box" of algorithms. You"decided where to put that thing" based on your visual inspection of your plot. Just don't get it!
She is a very good lecturer (brilliant!) but she failed miserably when she showed utter ignorance and disrespect for some very deep and groundbreaking work done between the 50s and 70s (‘Classical AI’) where some basic understanding of how our own brains work, how we understand the world around us and how we solve problems. Today’s “AI” has no “understanding” of science, physics or biology. All it does is pattern matching between vast amounts of data. And yes-it’s getting intriguing results - but also fails again and again when there’s a limited amount on information on a specific problem. I was expecting a more balanced lecture that would at least do some justice to geniuses like Minsky, Lenat, Newell, Simon, McCarthy, Shannon, Samuel and many others who surely wouldn’t look at today’s calculator and call it ‘intelligent’. This is plain demagogy. Makes me sad that an otherwise very bright scholar would be so uneducated and mislead the audience to think that the new ‘AI’ is more than what it is.
![[UA] Team Vitality проти NAVI | IEM Cologne 2024](http://i.ytimg.com/vi/wttO1zVRTC0/mqdefault.jpg)
This is the way all courses should be. Really engaging and marvelous work Cassie. Thanks a lot for your effort and all others who helped.
the subtle humor in between makes the course so much more engaging.. One of the greatest instructors i've ever seen..i can't believe i completed a 1.5 hr video in one go.. kudos to Cassie, admirable job..
This content is gold, still in the year of 2024. A great explanation & delivery. Fundamentals are top notch.
Wow I'm joining Google next month as a intern & have always wanted to pursue ML but was afraid that I might not be competent. This video really encouraged me. Quote "if you're too afraid to start you tend to not do well in applied machine learning". Ur presentation was really great and spot on. Thanks for the dedication in making it easier for us.
Amazing talk; The content was great, but the delivery was flawless. Not to mention the subtle brilliant humor; even if it was a tough crowd
You know its great when after reading all the other stuff on ML and endless online material out there that this is the one that gives you that feeling of the "the penny has dropped" and "aha" moment in your understanding - great course. If you are new to ML or even have started on your journey, starting here will give you a great foundation to build upon your knowledge. It's even a great place to revisit concepts to refresh yourself. Thanks Cassie for sharing this with the world and help others in their journey.
This is marvellous! Cassie, I'm in the middle, but already remember more about basics than after 5 previous ML courses I've already did. And it's funny. And I love how you talk about this stuff. First time in years I want to watch more about ML and I don't feel bored after 15 minutes. I will definitely watch all your lectures and finally apply all the knowledge that I'm gaining here. Because what I am missing is the flavour and a concept. I now the math for ML, but I lack the feeling, I think the most important part. Bravo Cassie! I'm so happy these lectures are available.
This content is an absolute gem. Thank you for making it available Cassie! Also, I'm still waiting impatiently for "MFML Part 4" :)
Aaaand it's here! ua-cam.com/video/9PBqqx38WeI/v-deo.html
This is a good one to show those who say they dislike school. Learning can be fun. Thank you, Cassie.
Summary of all textbooks in one video to understand ML . Thank you , Cassie
If every course or subject was taught like that. I'd be an A+ student. Excellent, fun, not boring at all, a joy to watch and learn from course. Thank you so much and please keep doing more of these 😁👌🙏
This is absolutely fantastic. I am glad I found this. An absolute treasure for anyone trying to understand the big picture
Just starting my journey in ML and feeling grateful for meeting your lectures, a really delighted learning experience for such a complex technology.
as a statistician who has been rather skeptical of the entire premise of 'artificial intelligence' i must say this lecture series got me interested in understanding a little more. i think i still have many many reservations which probably stem from familiarity with models which can serve both for prediction but also for inference. whereas with these models, it seems like we are trading away inference and even the ability to analyse the model behaviour in pursuit of better and better quality prediction. i think the work you are doing here to communicate that tradeoff is important. thanks for making this available for free.
I am absolutely impressed by the way you express all the fundamentals, it's just simple and powerful. Your analogies and the way you present them are beautiful. Thank you so much for your time and effort. I'll show this to all of my friends who wants to have some ideas how AI and ML work.
The UA-cam logic will most likely recommend to all of my friends in India especially in the IT field. Most likely this video will hit 10,000 views in another 3 months . This will work because UA-cam recommendation logic will do it's job like a charm
An incredible communicator. I now feel I have a foundation to apply ML and AI to problems.
This is wonderful. Had to pause the video because after the ML definition all the previous examples she gave were just the ideas to explain the best possible definition. Very smart and now I need to review everything but so far very entertained yes, many technical topics well explained but need to be digested. So far loving it 😍
Same here I absolutely delighted with explanations and examples, I look forward to more like this!
This is fun. I always thought ML and AI will be difficult to understand.but, you clearly showed otherwise. Thanks Cassie.
Love the approach of taking a complex topic and doing this for dummies version. Moving on to the next lecture … thank you for making these available for the wider world
I have always loved my Math teachers and you are in that list. Cant thank you more!
I just love your sense of humor, you are so full of personality ❤ thank you for using your talents so creatively 🎉
I will dub thee Mary Poppins of AI 😂
Pure gold. I was actually looking for a course on AI to buy and then I found this one for free. Thoroughly enjoyed it !
Your elucidation skills are top notch.
I am really attracted to and motivated by your way of presentation, teaching, and making people see what is taken as hard become easy. You are amazing, Thanks for this talk.
I can say this is the best course for ML till now. Most of the folks doesn’t know / understand / realise there is two ML types research and Applied. As a designer I am tinkering with fusing design thinking and ML for past 3 years and I am just getting there to work up a process recipe so that anyone can become a wonderful chef & it start with understanding the business problem and user problem. This course helped me understand how what I am doing is super important in the realm of the things today and tomorrow. Thanks Cassie 🙏🏻🤟🏻
Really appreciate you making this public. A great presentation; really made me think differently about how we use data science in business. I've followed your posts on LinkedIn and just stumbled upon your UA-cam... I'm glad I did!
excellent content and presented in a way that kept interest levels high ! really enjoyed it :) thanks so much for making it publicly available
Cassie,
This is great. I am excited to watch it.
Congrats on your new domain. Best, Mary.
I really relate to the issue of people lacking the ability to apply concepts in the real world. After my MMathStat I felt like I learnt how to pass exams with little idea how to do these things in the real world.
Such a pleasant voice and such an interesting manner of conveying these things, nice!
Love the presentation; with thanks
Wow, Vadim must be proud of you. Vadim moet baie trots wees met jou. 😊
Afrikaans?
Hey Cassie, Such a wonderful video. Thanks a ton again for making it available for public. I am watching it for third time :) coz i keep understanding more every time. One request if you could make it happen - there are irritating videos which keep popping in between, not sure if you can remove it. Thanks Again, Appreciate the super good content and your calm way of explaining concepts.
Really enjoyed watching the course. Great Job!
loved it! especially the initial kickoff with all those funny connotations and references. that was an engaging experience
Very good presentation Cassie. Keep us interested in ML and AI. Thank you!
i just became fan of you. so much knowledge with simple explanation
I'm so glad I found your channel. Amazing content, thank you!!
The best I've seen so far, amazingGGG!
This is Gold.
Good explanation for machine learning problem solving, great job Cassie
Great talk. It explained everything beautifully.
"AI succeeds at very complicated tasks that programmers can't write instructions for by hand"
your content is amazing and simple, appreciated
Great content, Cassie, a must see for all managers. Are the slides available?
fantastic introduction. i shall steal it.
Great presentation !
Incredible! You speak really well and use wonderful illustrations. Thanks for sharing
(I found you by going through Google’s data analytics certification on corsera, so glad I dag through that one page about linkedin even though I didn’t need it lol)
Loved your analogies, again well done!
Especially the kitchen one towards the end and how you differentiated between research and applied AI/ML. Good microwave engineers are probably horrible cook lol
🆒 Cassie 😊 you beautifully explained it... ❤
Amazing intro lecture love it！
Wonderful for a beginner like me.. Thank you Cassie kozyrkov
Soooooo good! thank you very much!!!
Great presentation
Really great content! Thank you Cassie!
Thanks for sharing. I really hope to see and learn more about it.
Hi Cassie,
@1:17:52
Given that at any point in time there are 10 empty spaces on average, X_bar = 990.
Lets say there is 1000 trials of the technology and a requirement of 95% accuracy before the green light:
With the strategy of full capacity, 1000 spaces occupied and the accuracy requirement of 95% given that X_bar = 990;
there is only two ways that the strategy would be a winning strategy:
i.e. what combination of the sample space(1000) will lead to X_bar = 990?
The strategy needs to be right 95% of the time
Therefore 1000*950 = 950,000
There is 50 sample spaces left in the trial
Lets say there's a brilliant optimization technique employed and it outputs:
800 * 50
or (900*40) + (400*10)
X_bar = ((1000*950) + (800*50))/1000 = 990
or
X_bar = ((1000*950) + (900*40) + (400*10))/1000 = 990
So out of all the possibilities that will lead to X_bar = 990 there is only two ways given the strategy constraint.
The problem therefore is the likelihood of the strategy given X_bar, it's too extreme!
On learning, I think your student was right!
Here's why:
Children achieve mastery of their local language from an early age without ever sitting an exam.
They're able to apply their learning directly in social interaction without testing because
this is simply the structure of their social interaction.
Would you agree to the possibilities of better alternatives?
Awesome presentation style
One question: how AI will decide if the data is based on two opposite examples, such as the example of the Ethics of Kant (Deontological Ethics: shall say the truth always) and the Ethics of Benjamin Constant (Consequentialist Ethics: the truth only according to the consequence)?
Amazing! Cassie.
Thanks for sharing
Thank you very much ! please keep it up 👍
Brilliant, thanks
flashed by this 👌
Awesome.
Thanks!
You're my first super thanks, Micah! Thank you!
@@kozyrkov First ever given, and first course of yours I didn't take live. I felt pretty cheap tbh, it's a great course, I've already applied it and I really appreciate your focus on practicality and keeping it engaging. Thanks for doing these!
Great content, can't thank you enough, Cassie :)
Great talk thank you
I didn't understand the parking lot example, can you/someone explain it to me? 1:18:00
Hi Cassie, would it be possible for the videos to be downloaded? I would love to view it offline.
I’m curious, how can we ‘improve’ our decision intelligence? You mentioned how that reliable workers (AI or ML) can scale up stupidity as well as intelligence, I really think that if I continue diving in this field I’m going to see exasperated people whom I thought I was going to impress with my answers lol.
How do u suggest we improve at being better at decision intelligence?
Start reading here:
bit.ly/quaesita_dmguide and bit.ly/quaesita_di
Bravo !
Hope one day I'll work in your department;)
Thank You :)
@datascientific Hey Cassie, thanks for this class!
In the parking lot scenario, would would the better question be to ask our system of not “if the space is empty or full”?
…perhaps asking which ones are full/empty and measuring that? 🧐
I cant much more understand in english what can I do
Please gives a solution 🙏🙏
Anna Karenina explaining AI
How do we get to know where Cassie is speaking next?
I would like to join and be part of this experience.
How do videos for “How to be a self taught programmer” have millions of views and yet this video BY Google has under 150k
How can I download the presentation?
Is this course uploaded anywhere else?
I'm really very confused. When you created your wine bottle chooser recipe, you plotted out your real life data of preferences after an undoubtedly memorial evening. Then you drew a line to separate "yes" from "no". So whatever relationship between your wine critic opinions and wine age that this line describes, there is your decision maker. I don't see where there is a "black box" of algorithms. You"decided where to put that thing" based on your visual inspection of your plot. Just don't get it!
She did the calorie deficit assumption by 208 live, anyone else see that.
27:00 regions
this video is sponsored by Patagonia :P
AI = Brute Forcing
Was it a car or a cat i saw?
😂❤
What a cutie, with a captivating mind too
Is she a product of artificial intelligence herself?
Most definitely. If you look carefully you'll see she has 19 fingers.
@@kozyrkov awesome lecture though, thank you
She is a very good lecturer (brilliant!) but she failed miserably when she showed utter ignorance and disrespect for some very deep and groundbreaking work done between the 50s and 70s (‘Classical AI’) where some basic understanding of how our own brains work, how we understand the world around us and how we solve problems. Today’s “AI” has no “understanding” of science, physics or biology. All it does is pattern matching between vast amounts of data. And yes-it’s getting intriguing results - but also fails again and again when there’s a limited amount on information on a specific problem. I was expecting a more balanced lecture that would at least do some justice to geniuses like Minsky, Lenat, Newell, Simon, McCarthy, Shannon, Samuel and many others who surely wouldn’t look at today’s calculator and call it ‘intelligent’. This is plain demagogy. Makes me sad that an otherwise very bright scholar would be so uneducated and mislead the audience to think that the new ‘AI’ is more than what it is.
you got a better video reference?
You look like Kathy Griffin
this is super impressive, Cassie. thank you for that.do you mind if I add you on LinkedIn? would love to connect and chat!