I am trying to learn ML in Azure and Gemini and all I keep being told is to use different models. What I am interested in is understanding how models are build and the though process that goes into doing it. Your course answers this question really well. Thank you.
Greetings Professor Jahangiry, thank you for openly sharing your lectures and the course material, it is much appreciated. I am personally coming from the AI side of things so I'm excited to see the financial analysis aspects that will be covered throughout the course.
Something that I wish would have been covered more (or maybe I missed it?) is the difference between econometrics and supervised machine learning besides how complicated the question is. I see them both as you have a couple of variables that you use to find the statistical relationship.
The difference that stood out to me is that we can grow our data set by 60 percentage points in Machine Learning, compared to Econometrics! That's a much larger sample size!
@@pedramjahangiry I thought that you mentioned we could use only 20% of the data in econometrics, but 80% of it we can use in Machine Learning. I could have possibly misheard this statistic though!
I want to hear the joke about the chemist, physicist, and economist! I really enjoyed this discussion. My first reaction was that we should always care about interpretability (maybe that is why I don't belong on Wall Street) but left with a better understanding of why it really depends. I also feel like my eyes have been pried open concerning the prevalence of unstructured data.
I want to start my carrer in machine learning. I am fresher. I have learnt almost all the basics maths required and python programming language along with how to solve a problem logically. I want to know if this playlist is enough to get a entry level job in ml if I do projects based on the learning from this playlist?
I like that you stress that both econometrics and machine learning are important but machine learning is capable of handling more data and more diverse data. Such as unstructured data, whereas econometrics can only handle structured data.
I am trying to learn ML in Azure and Gemini and all I keep being told is to use different models. What I am interested in is understanding how models are build and the though process that goes into doing it.
Your course answers this question really well. Thank you.
you bet!
Greetings Professor Jahangiry, thank you for openly sharing your lectures and the course material, it is much appreciated. I am personally coming from the AI side of things so I'm excited to see the financial analysis aspects that will be covered throughout the course.
this is the playlist i was looking for great work .
Glad you like it!
Something that I wish would have been covered more (or maybe I missed it?) is the difference between econometrics and supervised machine learning besides how complicated the question is. I see them both as you have a couple of variables that you use to find the statistical relationship.
Logan, the is a fine line between econometrics and machine learning. please watch class 2 and 3 as I cover the differences in details.
The difference that stood out to me is that we can grow our data set by 60 percentage points in Machine Learning, compared to Econometrics! That's a much larger sample size!
David, where is the 60% coming from?
@@pedramjahangiry I thought that you mentioned we could use only 20% of the data in econometrics, but 80% of it we can use in Machine Learning. I could have possibly misheard this statistic though!
I'm loving this course
I want to hear the joke about the chemist, physicist, and economist!
I really enjoyed this discussion. My first reaction was that we should always care about interpretability (maybe that is why I don't belong on Wall Street) but left with a better understanding of why it really depends. I also feel like my eyes have been pried open concerning the prevalence of unstructured data.
Here is the joke Logan: en.wikipedia.org/wiki/Assume_a_can_opener
Thanks for sharing this course! I was wondering if there is a way to know what the reading materials are for each class?
this is great suggestion. I will make sure I add the reading list to my GitHub account in the near future.
@@pedramjahangiry Thank you very much!
Just watched the 1st lecture...Very nice introduction...Thank you for sharing this!!
Really hope more people discover your channel...Thanks a lot!!
thanks for your feedback!
I want to start my carrer in machine learning. I am fresher. I have learnt almost all the basics maths required and python programming language along with how to solve a problem logically. I want to know if this playlist is enough to get a entry level job in ml if I do projects based on the learning from this playlist?
of course Sharanabasava! I am quite confident that this course would be a perfect jump start for you!
@@pedramjahangiry is it must to follow your econometrics playlist also for ml and data science in general?
Thank you for your feedback.
@@sharan9993 not necessarily! but studying the econometrics first will be a big help down the road.
I like that you stress that both econometrics and machine learning are important but machine learning is capable of handling more data and more diverse data. Such as unstructured data, whereas econometrics can only handle structured data.
Glad you liked it Clayton!