@sanjaybarnes5717

Thank you for this video. Recruiters need to watch this to really understand the role of a Data Scientist and that there are 101 ways to skin a cat. A Data scienctist job is to figure it out with the data. Hence, the word scientist. Scientist explore. They ask questions, then trying to find the answer and further explore to see what they can find. While a ML engineer's job is to design and make sure the result is functioning well. They also need to understand that what they put on a job ad or description is not the end all and be all in terms of tools as Data scientist. You can be in a roll where you are using a few algo and you can be in a roll where you are using a lot of different algos. Us data scientist and ML engineers still Google things. We use a lot of other people's codes and we use templates. Many data scientists are grossly underpaid. I see Data scientist jobs asking for 5 years of experience and only want to pay $120,000. They are even asking the DS and MLE to use other experiences and skills outside Data Science. KNOW YOUR WORTH, PEOPLE!!!

@ammarabdelaal150

What a concise but clear and to the point explanation! It explains many details in a very simple and straightforward way.
I am truely satisfied by this video and the amount of information it has provided and questions it has answered.😁

@chiragpatil4132

i must have checked my notifications 100 times during this video😂😂

@hhabill

This video is amazing! I really understood what is Machine Learning.  Keep it going!!🖤

@samkelosibongakonke5003

This is really good, makes things much simpler.

@aww2historian

Thank you for explaining the differences between all three roles: makes much more sense now!

@reinierscheltema9818

amazing video, thank you so much!!! I want to start a business that has to do with AI. ML engineering makes so much more sense now. Thanks x100!!!

@sarthak-salunke

ultimate explanation , all video delivers deep insight about the subject ,please keep it up.❤👍

@amarfauzie1826

your video is really amazing concise yet packed with detailed things

@sorvex9

Video started out good, then you kinda started saying ML engineers are "unicorns" and mixing in too many skills. High-performance languages don't have very good ML frameworks, so they aren't usually used. And most frameworks in Python use Numpy underneath, which is written in C... 

Also, you focused more on "big data platforms" rather than general MLOps, being able to write API, containerization, Linux , CI/CD, etc, which I think is much more important to an ML engineer than what you said.

@emrekayax07

Wow, what an incredible video! The production quality is top-notch, and I love how seamlessly the visuals and music blend together. The content is so informative and thought-provoking—I've learned a lot from watching this. It's evident that a lot of time and effort went into creating this video, and it truly paid off. Kudos to the talented team behind it! Keep up the fantastic work, and I can't wait to see what you come up with next. You have a new fan here!:hand-pink-waving:

@melnjada2

This voice is fantastic! I could listen to it for hourssss

@sunnyarora3557

Thank you for the Video, Best explanation on internet.

@DBSQ-wb3ht

Ok so based on this video, I'm working on a project where I'm essentially all three. No wonder why I get so many headaches when i spend hours of my day on this thing.

@varunnayyar3138

Very subtle detail showing a female as an MLE. nice work.

@jamesrosicky2912

Great video! So well explained, thank you!

@o1techacademy

Very clear 💯

@matthewjackson2457

What would your advice be for a data analyst intern from non-CS background trying to break into data engineer/machine learning engineer career? I’m interested in ML engineer but it seems like DE work is closer to data analytics, and easier to break into compared to MLE.

@good_vibesz

Superb & clear Explanations

@MK1T

Can I take this course as a Mechanical Engineering student?