@MisterGeekey
This video is fantastic, I started learning ML recently and planed to build my first projects in 2025. The whole video was as if my mind was being spoken out loud as this is exactly how I was planing on doing. This reinsures me. I have a solid math background but always hated statistics. This Khan Academy resource will be soooo helphul. Great video, thank you !
@kashifayaz2369
My goodness, if anyone wants to really get into ML this video is IT! Note: Not for people who say they want to do ML, but in reality just like planning what they will do. (Note to self as well 😅)
@coomlord5360
That last part about building off tutorials is very real. I find that most of the youtube guides/tutorials are very basic and meant for beginners, but if you take something basic and implement your own ideas it can become a larger and larger project which helps a ton
@que_93
Perhaps, THE MOST honest content on this matter: Learning ML.
@ytann
I'm a full-time data scientist, and I can attest, this IS the way to do it. Excellent video!
@adityamwagh
This is great advice. Coming from someone working in ML and Robotics since 2018. No BS, to the point advice. This is the only ML learning advice video you should watch.
@Pxrpose
I have been mindlessly doing random ML concepts and projects without a clear goal or pathway. This video was an absolute game changer. Thank you!
@notlogic.the.second
the hardest thing about programming and in general these topics is that it is not straight forward and you know what you need to learn. that is the hard part because then it takes a lot of time and gets boring really fast. nice video
@veil877
This video is superb. I have 300 years of experience in the field and this is the best video yet.
@dejoneltahan1974
fastest way to learn is to ask chatgpt to do it and learn the maths behind it by asking the is you find errors in your code try reproducing what chatgpt made
@AaronGayah-dr8lu
Because there’s a lot of content available on the subject, persons wanting to learn something are able to do so, but the lack of a structured approach makes them inefficient. This helps me immensely, thank you for that.
@noblemaster0173
This video is all i needed. I've been into Data Science for over a year now and i have done and learned many of the things mentioned on the video, but at some point i felt like i missed something on my way, and this videos had the answers i was looking for. Such a fantastic road map for ML
@danielmanzano7686
Before starting my master's degree, I thought training a model was as simple as having some data, running model.fit, and using model.predict. But machine learning is far more complex. It involves calculus, linear algebra (everything boils down to linear algebra), and understanding algorithms across traditional approaches, NLP, computer vision, and beyond. While YouTube videos can help with basic concepts, becoming an expert requires diving into state-of-the-art research and thoroughly understanding the underlying mathematics. Skipping the theory leads to gaps, especially when reading papers. If you aim to innovate, develop new models, and truly understand how machines "think," no video can replace a solid theoretical foundation. Mastery demands significant mental effort. Don't be misled by oversimplified tutorials. THEORY IS ESSENTIAL
@soufianeaitlhadj9115
From someone that has (maybe) the majority of the math knowledge that you talked about, I can't emphasize on the importance of the python libraries that you talked about Pandas, Numpy and sklearn, we sometimes forget how important they are especially if you come from a theory based background. Also, If you have some knowledge gaps (especially in the hypothesis tests in stats) you'll have a big trouble trying to make good predictions and/or insights. Also, I want to make a point on the part when you talked about calculus, I think it's important to study not only derivatives of single variable function, but also for multivariable ones, because it's important , for example to understand from where the estimators formulas in multiple linear regression came from. Good advice.
@malinenanjing
When it comes to ML, I often get lost and here it is a game changer. Thank you for your guidance. I have saved a lot of time.
@shamsul262
Great video to learn ML. I have invested lot of time to learn ML.But couldn’t found a path to follow. The zig-zag pattern of learing made me disappointed. Finally got the right path. Thanks a lot.
@kartik5691
Grateful for the video brother, You just saved me hours of banging my head against the wall of youtube creator claiming to build a roadmap for ML and tell BS. Thanks againðŸ˜
@ivds13
I have been stuck in tutorial hell and this video made me realize it 😅 thanks for the content! will try now and do some projects of my own
@sakshamchhatkuli271
-I just realized that I have been doing things all wrong, I need to refine my strategy now! Thank you for your video!
@Nikhil-xw2wc