The TLDR of the text is: To become a successful ML Engineer, you need to: 1.Know Python programming. 2.Understand basic calculus, linear algebra, and probability theory. 3.Have patience and curiosity for continuous learning. 4.Be familiar with core ML libraries like numpy, pandas, and matplotlib. 5.Understand classical ML and deep learning algorithms and their best practices. 6.Master model evaluation for effective ML implementation. 7.Possess strong software engineering skills, including data engineering and distributed systems knowledge. You don't need all these skills at once; they can be learned over time through practice and continuous learning.
@@francescocavalieri5443 I do think they are :) For most people at least, especially if you are talking about ML engineering. To understand the basic ML algorithms, you really don't need complex stuff. You need to know how derivatives work and how to do matrix multiplication. Those are the fundamentals. Sure there are things like cosine similarity. But generally, you just need to know how to apply it. But even understanding it is arguably not that complex. The most difficult thing will probably be Eigenvectors and Eigenvalues, but those are really only relevant for one of the common ML interview questions - questions about PCA. Of course, you can always go deeper. And at some point you will understand the math better and better. But still, from my experience, and that of the ML engineers I have spoken to, the basics really are enough.
Boris, I've been following you now for a little while and I have to say I wish there were others in your field with so much enthusiasm and energy, not to mention the dedication and effort you have put into your videos. You're helping guide an old hardware guy on a great ML journey. Thank you so much. 😊
The advice on CUDA was true gold, nobody is talking about this here in Brazil, it will give me an edge in my selfeducation. You are such a talented teacher brother, wish the very best of life to you.
Hey Boris, I've just found your channel and I'm really appreciating it. I found your video on productivity and your videos on ML research really helpful as a student doing my first ML research project for my Masters. I would love to hear specifically how you go about your machine learning research from a productivity point of view.
I like linear algebra, and calculus but when it comes to advanced probably then i don't like it. So, Can give me some tips that would help me learn it also enjoy it?
I think what makes math fun to me are the amazing things you can do with it! With a list of rules, you can go from having an idea, to formulating it to a single mathematical equation that can be used to predict the future. If you see the "power" and logic behind these math equations, they become much more fascinating and fun to learn :) At least for me!
Hi, thank you for your content, it's very helpful and exciting, I am just wondering, what do you mean by reading paper, do you have a video explaining this? Thank you.
Great videos about ML. Please continue posting videos about state of the art new changes. It will be also to start series how you implement ML projects that are more unique ( not Titanic datasets) ;)
It depends on the company you are applying for. But in general, especially if you are at the beginning, normal SWE internships/ jobs are a perfect place to start!
You can definitely start learning as soon as in high school, yes! But it will take time (very likely several years) to really get good. The learning never stops :)
Yeah, this can be tough... It might be more challenging, but perhaps you might want to look outside of your country for opportunities, or look for remote ones.
Yeah, I see... Try building projects publicly, e.g. by posting about it on LinkedIn or doing pull requests to cool OS projects! Try joining online communities, e.g. on discord. The point is, try to not only build your skill, but also get to know people outside of your country, who you can demonstrate your skill to. Sooner or later, there will be a person who can refer you to an internship/ job or even invite you himself. I know this might seem like an annoying workaround, but if you are struggling to get internships through standard applications, you need to get there by knowing people. It will take time though! That said, knowing people in general is insanely valuable in any case! So I can recommend it even without the use case of getting an internship/ job.
Hey Boris! Thanks a lot for the video, do you think Leetcode is important for an ML engineering job? And if yes, should I practice pandas or python on Leetcode?
When people say basic maths or basic probability, its not basic. Basic math for most of the student think solving a calculus problem with ease. but for ML/DS one should have proper knowledge of how the algorithm works and behind the scene. Algorithms are basically maths, If you really want to get into ML , please get your maths right. And not just basic math. And I haven't come across a person wihtout masters degree/PHD who got into ML.
Boris i have a serious question i'm a college dropout..Now i want to study for being a Data Scientist/ML engineer .If i follow a stuctured curriculum through self learning can i achieve my dream?.
Bro I am 18 year old man from India. I just finished my class 12 now I got admission for School of Engineering for CSE with AI and ML. What do you think I can know before starting my engineering in late September..! Should I do what you said or there is any other things 🤔 I should di
The journey of being AIML Engineer is a bit hard my friend, you mentioned you are from India, I suggest you to do courses on Full stack Data science and land in a job or internship first to the end of your 6th semester, and eventually you can go to ML field job, till then learn python very well, be good at math, learn communication skills and do some competitive coding as well.
I have experience as a Software Development Engineer in Test (SDET) and do not have a Computer Science degree. Can I become a machine learning engineer in the San Francisco Bay Area by taking some courses from Andrew Ng? I am aware of what I need to learn. I just want to ask if I can get a job with a certification, given that I only have a few years SDET experience.
It really depends on where you are. If you have no idea about ML, then start with the ML basics, but if you are already somewhat into it and want to get to deep learning, this one is quite cool! introtodeeplearning.com/ There are really a lot of courses and in the end it really doesn't matter that much. You just need to keep going and you will learn many concepts when you encounter them in the wild :)
Boris you didn't update us on DEEPMIND INTERVIEW, Can you please make a video on it? However It went, Can you share it with us , It gonna be good learning experience for all of us TYSM If you do make it
Wow, that was over a year ago haha, but yeah, you are probably right. I never really gave an update on that. I was rejected :( My next video will all my experiences of past years! I hope that will provide a lot of guidance, help, and perspective :)
@@borismeinardus Thanks for the update boris, I'm sorry to hear about the rejection, but I really appreciate your openness to share your experiences....looking forward to your next video about your past years' experiences. it will be really insightful and valuable for all of us. Keep up the great work
I am in process of taking course for Fall semester I wants to take ML System course, where there will be implementation of ML model with C++ and CUDA But, one person on LinkedIn told me that CUDA is old, and asked me to take something else like AI Ethics or explainable AI Could anyone suggest me what is and what will be in Trend for upcoming 5 years?
Does this person who told you CUDA is old work on AI Ethics or explainable AI? Then he is veeeery biased haha I would definitely argue against what he recommends. AI Ethics and explainable AI are important, but to say CUDA or learning about ML hardware optimization is not relevant is very wrong. At least that is my opinion and that a fairly confident one.
@@borismeinardus He is into DevOps role and works around infrastructure related stuff at fintech company I want to focus more on learning infrastructure and ML components of AI during my masters, so when I messaged him on LinkedIn for guidance about electives He said CUDA programming during 2010 was very best skill to get job. It's relevant today as well, but I will need to put lot of efforts on learning it and related things. He said just like CUDA was at peak in 2010, explainable AI/interpretable ML, AI Ethics, Transfer Learning are good things to learn in 2025 He asked me to get familiar with frameworks which are based on CUDA, but easy to implement things using it as abstraction layer Anything you can suggest me? I know theoretically and practically Supervised and Unsupervised Learning and related topics I was thinking to focus on bit of DevOps and infrastructure related things for third semester and in fourth semester to focus on neural networks and deep learning
One more point he mentioned was I will need to take lot of efforts from programming perspective as the course uses C++ and CUDA And I am from non-CS background (Mechanical Engineering) with intermediate Python skills and OOP using Java
Well, it will be more challenging to learn these skills without a more extensive CS background, but that is always the case. Some topics will be easier for some people and harder for others and vice versa. But to say it is not relevant is simply unfair and false. At least in my opinion, and that of those who I have asked.
If I would have to guess your future job is at Citadel, because it is the only Quant firm in your list, and they don’t have an office in Germany so you need to move 🧐😂
@@borismeinardus hay I am computer science student from India and I am learning about AI ML concept and building a project . My question is 1) To build a project what are the main concerns about the project ? 2) what are the main aspects of building a project and doing research on a particular topic . how we can build documentation as a production level project or research . 3 ) what should be the focus to solve the problem and to do research?
Those are amazing questions! You are looking for the right things, which makes you already ahead of many others. I will create a video soon where I hopefully answer all these questions :)
@@borismeinardus Thank you so much for your encouragement!😊 I'm looking forward to your video. In the meantime, do you have any resources or tips that might help me get started on addressing these questions?
