🚀 There is so much more to explore in ML. Feel free to grab my FREE cheat sheet of different ML domains and open challenges: borismeinardus.substack.com/p/a-list-of-different-ml-domains
i think reading a paper is a great skill at the end . if u can understand what people did how they did it's great checkmark that u have reached a certain point of advance level of ml
@@boldmeditations I'm doing Angela Yu's 100Days. It starts gearing towards Web Development but she does some great projects which gives you hands-on Python basics to semi-advanced knowledge and applications, with some ML applications too. There's another called Python Zero to Hero which is by someone else, but also quite good, and not as long as the project-wise 100 Days with Angela. Both on Udemy.
I am a Business Consultant with 2.3 years of experience, and I'm planning to transition into the Data Science field. I was just looking out for guidance on how to get started. Thank you so much! I found it very informative.
@@loveUbleach4everWhere did he mention any monetary incentive? Oversaturation exists in almost every field these days, and your passion shouldn't fade because of a few posts or blogs designed to fearmonger and discourage others.
As an MSc Computational Physics student and a beginner in learning machine learning who has done some research on how to teach myself ML, a lot of what you said is consistent with my own conclusions and how I would approach the self-learning process. Great video! Subscribed.
hello sir, i am a junior physics student. can you give me some advice on machine learning? is there something you wish you had done earlier? or can you suggest how I should plan my path? it would also be great if you have any project ideas.
the more knowladge you obtain on a subject gives you more facts about its truth so you have better undetstanding of what you really know and what you dont.... that doeasnt mean tho that you are not in a high level in general
it will never stop... two MSC's om System Design Programming and Comp /Network Security, 25y experience in building systems and design network arch learned like 12-15 different prog langs and yupie Im here watching tutorial from a 22-25 y old about ML and what is the best approach to ML in 2024...
@@kpwlek mind blowing🤯! I think learning is driven by curiosity. When we do a deep dive on a particular topic there's always something which we are not learning. And later that might seems interesting which we start learning. For me this is the cycle
As a PhD student in NLP, I completely approve your recommendations! These are relevant, concrete and feasible steps so thank you very much for presenting this in that very pleasant way. I would maybe add something regarding college students that may wonder if they should start with math or computer science majors to work in machine/deep learning top companies. My advice relying on my experience is: start with math. It is, to my mind, far easier to learn computer science concepts when you already got the maths principles. I do have a math background initially and I struggle a bit at the beginning of my journey with some software engineering aspects of project development, but I am convinced that I would have struggled way more if I had to learn math concepts from scratch by the end of my degree.
Thank you very much for the insight! I really appreciate it and value it, that you approve of what I say 💛 But yes, maths is always the biggest blocker for people wanting to get started with ML. I think both paths (Maths first, or maths second) are viable and depend on the person. I know some (probably most) people might be frustrated right away when they just start with pure maths (I personally love maths :)). That's why I think starting with something fun like programming basics is a good idea. As mentioned in the video, no step really needs to be fully completed one after the other. You can also learn maths and python side by side. Mix and match to your desire, as long as you put in the time, and enjoy it, everyone can learn machine learning. And If you are in college doing CS, you will have linear algebra and calculus courses anyway haha. You just need to appreciate the content and power through! Again, thank you for your comment! 😊
what do you mean by "math"? Do you mean more than second year stats, calc1-3 and linear algebra? if yes, then perhaps the math major recommendation makes sense.
Thank you for this! I'm starting my neuroscience PhD soon, and I want to implement ML to aid my projects. Your video provides a super helpful framework going forward :)
These are three people I would recommend to follow. Andrew NG, Andrej Karpathy, and Jeremy Howard. Sequester from rest of the crowd and videos. Protect your priors
For the math part I highly recommend the "Mathematics for Machine Learning" book. It covers all the important stuff without going too much into the details (and gives you the foundation in case you later still want to get into those details). Oh, and it's free.
Frankly this is where i really messed up. I learned python and straight up jumped to learning the ML tech stack. Completely ignoring the math. When i got into NN and deep learning, my lack of math knowledge sent me crashing down coz i kept learning math stuff in bits and pieces only for what i was learning in deep learning at the time. It got chaotic and led to lots of failure in tasks and interviews. Finally an interviewer told me that there is no point going advance if i dont know the basics. I went back to learning math properly and while its been really challenging to study with a pen and paper instead of coding all the time... i know i am now on the right path.
As a comsci major who did some AI as a hobby then formally taking a module on it, I'm burnt out due to the same issue. Its very easy to say just rely on some existing neural network architecture, and ignore the fundamental math principles behind it. The thing is the math (e.g deriatives, linear algebra, statistics) is what drives the algorithms in the first place. By know the math, you know what metrics are appropriate, what other approaches that are more efficient can solve the problem quickly. But its intimidating since most of the data are working in arbitrary higher dimensions that what I learnt before, and its hard to visualize, but less connect the math you prove into code.
Yes, I see... I am sure this is a common problem people have. Maths is scary. I feel like really understanding the basic is important, but expecting to understand everything in the first paper you read is pretty much impossible. But, (and this is the important part) since you have learned the basics, you know enough to research the more advanced maths, and if you give yourself enough time, you will learn everything you need step by step. Even if there is a maths concept you didn't understand in the first paper, once it arises in a further one you read, you will surely be better prepared to understand it. If you continue on failing and learning, you will master the maths, and then see, that it really is not too scary after all :)
Fantastic video Boris! And excellent practical steps, especially the final one! (Courses = base knowledge, projects/paper replication = specific knowledge)
Great video. ML is not about taking a dataset and training random models on it. Every model is different in their own way and the ability to understand the math behind the models helps you determine which model may fit best with your dataset. While these tutorials are great, I’d recommend getting a graduate degree MS/PhD. Most ML positions require a graduate degree, which force you to truly understand the theory
underrated video, underrated channel . Gonna binge watch all the videos ! so informative and straight to the point, please make more videos on machine learning and artificial intelligence
Words are not enough to express my words , i was just too confused to even start even tho i do have prior basic knowledge, as someone coming from software engineering having a basic idea i did had hard time figuring out what to learn , i cant think just how confused how other people are . Thanks a lot 🔥❤
Here's a step-by-step summary of the video 1) Learn Python: Start with Python programming, as it's essential for ML. 2) Mathematics: Focus on linear algebra, calculus, and statistics. 3) Foundations of ML: Study basic ML concepts through online courses (e.g., Coursera, edX). 4) Deep Learning: Learn about neural networks and deep learning frameworks like TensorFlow and PyTorch. 5) Hands-on Projects: Apply knowledge through projects, Kaggle competitions, and open-source contributions. 6) Advanced Topics: Explore advanced ML topics and research papers. 7) Stay Updated: Follow ML trends and continuously learn.
That is pretty funny. I am currently studying to apply for TU Berlin kolleg. So that is why I followed you because I think saw one of your videos about a day of your life being a TU Berlin student. And ngl I've never seen another video of you. Untill now, about 1 or 2 year(s) later. And this time I was looking for the roadmap for machine learning. And when I was watching your video. I was so confused that where did I see you. And after a little searching I found out why. So the path I am taking is so similar to yours. That makes me not feeling lonely ❤❤
Valuable advices for the begginers! Thank you for sharing! We also see much perspectives in ML engineering. In our last video our colleague who is a Machine Learning Engineer explained his job through an example of the latest AI project he completed at Jelvix. He even shared his work schedule Also quite handy for the begginers
Fantastic video - as a MSc grad and now a PhD Researcher for Cryptanalysis using ML/DL I totally agree with your selection and more importantly your APPROACH, particuarly in not getting beaten down. I ended up recreated several complex cryptanalysis environments and also adding my own coding to use on 'baremetal' and cloud resources, it was hard work but laid the foundations for the research I am doing now. I shall watch your video again as I really like it not jus for the content, but in how POSITIVE you are, its infectious !!! thanks again, Alze.
Starting from my point, I think dont rely on GPT too heavily, though it really helps when you're stuck in some problems. Sometimes you have to create your own constrcution of neural networks based on all the basics from python, torch, numpy etc. And your own construction is way more straightforward than the GPT gives. (My experience learning DL for 2 months😜)
Thanks for the video. I'm a master's student majoring in electronic information. Your video solved my anxiety and made me no longer worry about whether I was progressing too slow and why I couldn't understand the thesis. So just study hard. I have seen and used all the things you mentioned. That's enough.
I'm beginning my conversion MSc in human centred AI and Game development in September, having no computer science degree or strong foundation in maths. I got a C in GCSE. It's a weak point for me but I can see it's crucial in this field. This is really inspiring and it's good to know that I will be okay as long as I work hard. Thank you!
thank you so much for this video Boris Meinardus, I was lost in starting the journey of ML but with this video seems like clear roadmap. Every second of this video is informational #gold.
I graduated and started working at Amazon. I have already taken a year of machine learning, NLP, CV courses and I have built working but not production ready accurate models on object detection, poem writer, and cyberbully detections, and NanoGPT from Andrej. I am really lost in what is the next step. While I have the knowledge, I lack the industrial experience. Because I lack the industrial experience, I cannot work in the related fields of AI. Because I cannot work in the related fields of AI, I lack industrial experience. So this is really been like a chicken and egg problem for me.
Great Video , I think book are also as important as course and I would personally recommend the book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow its a great one for connection theoretical ideas and practical workflow
First I learned Linear Algebra on yt with Strang course. It took me half a year. Now I'm on the challenge to comple all Khan Academy HS and college courses. It's already half a year in. I still have lots to do. Then I'll take specialized math for ML course. You'd say it's overkill and I waste time. You probably would be right. Also I forget the most of what I learn. But I just want to tick off all the boxes, so whenever math difficult comes in, I wouldn't be like "oh I have no idea what's going on"
I think you're just doing unnecessarily too much. Just learn enough to get you started. Don't let imposter syndrome win over you. The thing about it is that if you understood some basics you will still revisit the maths and stats as you're working on projects
Hey Boris, loved the video! I'm starting my AI graduate studies in January and have also contemplated starting a TH-cam Channel explaining the subjects I learn. Just wanted to let you know that you're an inspiration for building a brand for yourself and I'm gonna cheer you on!
I am not sure that basic math such as linear algebra, probabilty in the level of high school, first year of university is enough. I guess some day you will encounter something like of a glass ceiling because advanced ml requires far more advanced math. I am not discouraging the reader to lower his arms, but if you have a choice, it is better to join university where math is taught on above average level. I have a really strong background in pure math, but even with it i sometimes strugge. For e.g. you might need subjects which are really hard to understand such as functional analysis, stochastic processes, descrete math and etc(subjects which is not present in standard engineering math curriculum). But i am working in the research, not on the ml engineer position, so i guess i'm a little bit biased :) But, once again, my advise is to study math as much and as thoroughly as possible. Hope it helps
I was scared that I didn't know enough math after taking linear algebra, but that covers most. I totally agree you can always go back and learn the holes in your knowledge in stead of spending months filling in potential holes.
Highly recommend all the steps in this vid! p.s. if anyone wants a gentle introduction to the math, I'm working on a series called Math You Need For Machine Learning
If starting over in 2024, I’d focus on a structured learning path: begin with foundational courses on platforms like Coursera or edX, dive into hands-on projects and Kaggle competitions, and leverage cutting-edge tools and libraries. Engaging with the latest research and participating in the ML community would also be crucial.
Roadmap is super relevant, contemporary, practical and pragmatic. Coined expectations about schedule are just inadequate. Typical yesterday-neophyte's, "second project" misestimation. Basic python+basic numpy+basic pandas from scratch, in "a few weeks"? Seriously? Even in "a few months" it will be a challenging task for most of students.
Happy to know I followed the right order in the first 4 steps! Thank you for recommending Andrej's course; I subscribed to Ng's Course but I couldn't keep up with backpropagation :( Will include this step in my learning journey!
A “Meta professor” is not a standard academic title. However, it might refer to one of the following: 1. Meta as a Topic: A professor whose area of expertise involves “meta” topics, such as meta-cognition, meta-analysis, or meta-ethics, which explore the underlying frameworks or methodologies of a given field. 2. Meta, the Company: A professor affiliated with or collaborating with Meta Platforms, Inc. (formerly Facebook), particularly in research fields like artificial intelligence, virtual reality, or social media studies. 3. Self-Referential or Philosophical Context: Someone who studies or teaches about the nature of teaching, learning, or academic structures themselves (e.g., “a professor about professors”).
Hey! Thank you for this guide. However, Andrew Ng's specialization's free audit version doesn't include notebooks and assignments and things, just the video content. How do you suggest I get some hands-on practice through other means? I don't know yet if I want to invest so much money into the courses.
Thanks 🤗 You can in theory apply to every ML opening you see, but at some level of company the courses alone will not be enough. You will need to demonstrate skills through projects and experience, that‘s why I added the last point in the video :) But there are of course other ways to stand out and I‘ll actually upload a new video covering more tips on exactly that on Sunday. Perhaps that video might be useful to you ☺️
Wow an amazing video!! Thank you ... When you say Reimplementing a paper and recreating the results what's that supposed to mean? What kinds do you suggest? What are the actual ones that have the chances to have an impact or basically to make a difference (basically solving a problem maybe) And finally what do you recommend?😊😊
I'd imagine he means academic papers where machine learning is used to get some result. For learning purposes, very old ML papers introducing what are now considered basic and standard ML algorithms would perhaps be good, but also modern papers from non ML subjects where older, basic ML is being used to solve a problem in those subjects. The latter are also more likely to have accessible data sets. Modern ML papers are likely to be about cutting edge research into improving existing algorithms or inventing new ones and probably less useful as a beginner. To reimplement a paper, read the paper a few times to understand what they are doing, get hold of the same or similar data, and see if you can implement the machine learning techniques used in the paper to get the same or similar results.
I enjoyed Andrew Ng’s ML specialization, but do with it was more hands on. It’s great for learning about the algorithms but you don’t implement much of the projects yourself.
I know Python and I've taken math up to linear alg (aced all finals in math too) I just need to brush up on my Python and I should be able to deep dive into that ML developer stack!
I might be jumping the gun a little, but I feel like in the near future polars is going to be the favorite dataframe tool instead of pandas. I definitely recommend learning it as well, it's a lot more intuitive and faster and a lot of frameworks are adopting it.
Hello Boris sir, thank you for your youtube videos. Moreover, I want to ask whether buying M4max apple with 128 gb laptop for machine learning, AI and Data science or buy M4 max with 36 gb and use cloud for higher data computation. It would be our pleasure to have on best laptop idea for these categories.
as a programmer whos been at it since the 90s I can tell you.... debug your ML code and have tests for everything. A small bug in the reward function (and many other places) could cost you days in wasted training time! bugs bugs bugs..... fix em.
Machine Learning is not easy. I found myself having some pleasant success by taking MIT's 6S191 course on Introduction to Deep Learning. Here are some pre-requirements: Maths: Calculus Algebra Probability Theory Programming: Python The course is teached on site but the teacher recorded all the classes and gave a lot of online resources to everyone.
I agree with everything except "everything should take few weeks". Depends how much time you learn a day? 3 hours a day won't let you finish basic course of linear algebra and matrix computations. And there is so much other stuf...
🚀 There is so much more to explore in ML. Feel free to grab my FREE cheat sheet of different ML domains and open challenges:
borismeinardus.substack.com/p/a-list-of-different-ml-domains
0:35 0:39 0:41
We need a video for how to get a job in ML?
This link is broken
i think reading a paper is a great skill at the end . if u can understand what people did how they did it's great checkmark that u have reached a certain point of advance level of ml
definitely 🤩
It will help you.
1. Basics of python
2. Learn numpy, pandas, matplotlib
3. Beginner course:
* Supervised Machine Learning: Regression and Classification
* Advanced Learning Algorithms
* Unsupervised Learning, Recommenders, Reinforcement Learning
By- Andrew Ng
4. Neural network
* Neural Networks: Zero to Hero
5. Deep learining specialisation
* Neural Networks and Deep Learning
* Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
* Structuring Machine Learning Projects
* Convolutional Neural Networks
* Sequence Models
Any recommendation for course names for beginner python?
@@boldmeditations I'm doing Angela Yu's 100Days. It starts gearing towards Web Development but she does some great projects which gives you hands-on Python basics to semi-advanced knowledge and applications, with some ML applications too. There's another called Python Zero to Hero which is by someone else, but also quite good, and not as long as the project-wise 100 Days with Angela. Both on Udemy.
@@boldmeditations th-cam.com/video/eWRfhZUzrAc/w-d-xo.htmlsi=9GZENjjQH4hPmoh6
This course helped me a lot.
@@boldmeditations just any course on youtube, check out bro code's python course, gets all the basics
@@boldmeditationsUse the book automate the boring tasks with python.Excellent book with clear illustrations for python basics.
I am a Business Consultant with 2.3 years of experience, and I'm planning to transition into the Data Science field. I was just looking out for guidance on how to get started. Thank you so much! I found it very informative.
May I ask why ? CS field is already over saturated, why yall want to make it more worst for ppl who actually care about AI progress rather than money.
@@loveUbleach4everWhere did he mention any monetary incentive? Oversaturation exists in almost every field these days, and your passion shouldn't fade because of a few posts or blogs designed to fearmonger and discourage others.
As an MSc Computational Physics student and a beginner in learning machine learning who has done some research on how to teach myself ML, a lot of what you said is consistent with my own conclusions and how I would approach the self-learning process. Great video! Subscribed.
hello sir, i am a junior physics student. can you give me some advice on machine learning? is there something you wish you had done earlier? or can you suggest how I should plan my path? it would also be great if you have any project ideas.
Honestly, the grind never stops. I reimplemented many papers and published my own ones and still feel somehow i am still a beginner forever
A fellow traumatized researcher 🫡
Haha, I feel the same way! Freaking self doubt in this domain!
Are you maintaining your implementations notebook somewhere?
the more knowladge you obtain on a subject gives you more facts about its truth so you have better undetstanding of what you really know and what you dont.... that doeasnt mean tho that you are not in a high level in general
it will never stop... two MSC's om System Design Programming and Comp /Network Security, 25y experience in building systems and design network arch learned like 12-15 different prog langs and yupie Im here watching tutorial from a 22-25 y old about ML and what is the best approach to ML in 2024...
@@kpwlek mind blowing🤯! I think learning is driven by curiosity. When we do a deep dive on a particular topic there's always something which we are not learning. And later that might seems interesting which we start learning. For me this is the cycle
As a PhD student in NLP, I completely approve your recommendations! These are relevant, concrete and feasible steps so thank you very much for presenting this in that very pleasant way. I would maybe add something regarding college students that may wonder if they should start with math or computer science majors to work in machine/deep learning top companies. My advice relying on my experience is: start with math. It is, to my mind, far easier to learn computer science concepts when you already got the maths principles. I do have a math background initially and I struggle a bit at the beginning of my journey with some software engineering aspects of project development, but I am convinced that I would have struggled way more if I had to learn math concepts from scratch by the end of my degree.
Thanks for sharing this!
Pin this comment already!
Thank you very much for the insight! I really appreciate it and value it, that you approve of what I say 💛
But yes, maths is always the biggest blocker for people wanting to get started with ML. I think both paths (Maths first, or maths second) are viable and depend on the person. I know some (probably most) people might be frustrated right away when they just start with pure maths (I personally love maths :)). That's why I think starting with something fun like programming basics is a good idea.
As mentioned in the video, no step really needs to be fully completed one after the other. You can also learn maths and python side by side. Mix and match to your desire, as long as you put in the time, and enjoy it, everyone can learn machine learning.
And If you are in college doing CS, you will have linear algebra and calculus courses anyway haha. You just need to appreciate the content and power through!
Again, thank you for your comment! 😊
This video could have been a 3-4 sentence guide. Disliked
what do you mean by "math"? Do you mean more than second year stats, calc1-3 and linear algebra? if yes, then perhaps the math major recommendation makes sense.
Thank you for this! I'm starting my neuroscience PhD soon, and I want to implement ML to aid my projects. Your video provides a super helpful framework going forward :)
These are three people I would recommend to follow. Andrew NG, Andrej Karpathy, and Jeremy Howard. Sequester from rest of the crowd and videos. Protect your priors
For the math part I highly recommend the "Mathematics for Machine Learning" book. It covers all the important stuff without going too much into the details (and gives you the foundation in case you later still want to get into those details). Oh, and it's free.
That book sounds great! Haven't had a look at it, but that you very much for the recommendation! 😊
Who are authors?
@@BrianGachie-d2d Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
Linear Algebra And Learning From Data (G. Strang) is a great book as well.
I was like cool it starts with linear algebra, then kept scrolling, what are these words....
Frankly this is where i really messed up. I learned python and straight up jumped to learning the ML tech stack. Completely ignoring the math. When i got into NN and deep learning, my lack of math knowledge sent me crashing down coz i kept learning math stuff in bits and pieces only for what i was learning in deep learning at the time. It got chaotic and led to lots of failure in tasks and interviews. Finally an interviewer told me that there is no point going advance if i dont know the basics. I went back to learning math properly and while its been really challenging to study with a pen and paper instead of coding all the time... i know i am now on the right path.
As a comsci major who did some AI as a hobby then formally taking a module on it, I'm burnt out due to the same issue. Its very easy to say just rely on some existing neural network architecture, and ignore the fundamental math principles behind it. The thing is the math (e.g deriatives, linear algebra, statistics) is what drives the algorithms in the first place. By know the math, you know what metrics are appropriate, what other approaches that are more efficient can solve the problem quickly. But its intimidating since most of the data are working in arbitrary higher dimensions that what I learnt before, and its hard to visualize, but less connect the math you prove into code.
Yes, I see... I am sure this is a common problem people have. Maths is scary.
I feel like really understanding the basic is important, but expecting to understand everything in the first paper you read is pretty much impossible. But, (and this is the important part) since you have learned the basics, you know enough to research the more advanced maths, and if you give yourself enough time, you will learn everything you need step by step. Even if there is a maths concept you didn't understand in the first paper, once it arises in a further one you read, you will surely be better prepared to understand it. If you continue on failing and learning, you will master the maths, and then see, that it really is not too scary after all :)
Software bros that ignore fundamentals ... hilarious.
can u please tell me from where to learn math I mean resources or tutorial I want to start with machine learning
@@raypamber watch the video again
This is great advice if you want to learn ML for fun. If you want to get an ML job you will need more than understanding the basic math.
Please tell us what is needed to get a job ?
Fantastic video Boris! And excellent practical steps, especially the final one! (Courses = base knowledge, projects/paper replication = specific knowledge)
Many thanks! You summarised it spot on! 💛
Keep in mind that this is a guide to "'learn ML"' not "' get a job in ML"'.
💯
@@borismeinarduswhat are extra things to get job
They employ you to bring more profit to the company than your salary.
@zaraza_ua5948ml😂
You mean getting ML job easier than actually doing ML 😂
Great video. ML is not about taking a dataset and training random models on it. Every model is different in their own way and the ability to understand the math behind the models helps you determine which model may fit best with your dataset. While these tutorials are great, I’d recommend getting a graduate degree MS/PhD. Most ML positions require a graduate degree, which force you to truly understand the theory
Yes so i am looking for ML for Business Analyst. Can you tell me any resources?
underrated video, underrated channel . Gonna binge watch all the videos ! so informative and straight to the point, please make more videos on machine learning and artificial intelligence
wow, I really appreciate the kind words!! Thank you 😊
I will do my best to not disappoint you! Let‘s see if you like this weeks video 😬
It's a helpful video for those who don't know how to get started with ML.
This video was extremely time-effective and simple. Thanks for putting it out!
As someonee who is just starting out, this is so helpful and feels manageable. Thank you!
3rd year here, thank you a lot man
I'll comeback when I publish my first paper (in RL maybe) 😊
Words are not enough to express my words , i was just too confused to even start even tho i do have prior basic knowledge, as someone coming from software engineering having a basic idea i did had hard time figuring out what to learn , i cant think just how confused how other people are . Thanks a lot 🔥❤
Awesome video , really a raw to think the feasible path and thinking
Here's a step-by-step summary of the video
1) Learn Python: Start with Python programming, as it's essential for ML.
2) Mathematics: Focus on linear algebra, calculus, and statistics.
3) Foundations of ML: Study basic ML concepts through online courses (e.g., Coursera, edX).
4) Deep Learning: Learn about neural networks and deep learning frameworks like TensorFlow and PyTorch.
5) Hands-on Projects: Apply knowledge through projects, Kaggle competitions, and open-source contributions.
6) Advanced Topics: Explore advanced ML topics and research papers.
7) Stay Updated: Follow ML trends and continuously learn.
pretty straight forward, thank you my friend, it really helps me get back to learn more and probably dream about work on the field.
That is pretty funny. I am currently studying to apply for TU Berlin kolleg. So that is why I followed you because I think saw one of your videos about a day of your life being a TU Berlin student. And ngl I've never seen another video of you. Untill now, about 1 or 2 year(s) later. And this time I was looking for the roadmap for machine learning. And when I was watching your video. I was so confused that where did I see you. And after a little searching I found out why.
So the path I am taking is so similar to yours. That makes me not feeling lonely ❤❤
Andrew Ng's Machine Learning Specialization course is only free for 7 days. After that you have to pay $50 per month to continue.
Long live piracy
Very insightful video. Thanks for the resources provided!
Andre Ks NN series is a goldmine.
💯
Thank you for this!
Really well put together
Thanks! I‘m really glad you like it :)
Valuable advices for the begginers! Thank you for sharing! We also see much perspectives in ML engineering. In our last video our colleague who is a Machine Learning Engineer explained his job through an example of the latest AI project he completed at Jelvix. He even shared his work schedule Also quite handy for the begginers
Fantastic video - as a MSc grad and now a PhD Researcher for Cryptanalysis using ML/DL I totally agree with your selection and more importantly your APPROACH, particuarly in not getting beaten down. I ended up recreated several complex cryptanalysis environments and also adding my own coding to use on 'baremetal' and cloud resources, it was hard work but laid the foundations for the research I am doing now. I shall watch your video again as I really like it not jus for the content, but in how POSITIVE you are, its infectious !!! thanks again, Alze.
This is brilliant! Thank you so much Boris - definitely needed this guidance to get started in ML/DS
🤩
Thanks!
Welcome! Thank you so much for the support!! 💛💛
Starting from my point, I think dont rely on GPT too heavily, though it really helps when you're stuck in some problems. Sometimes you have to create your own constrcution of neural networks based on all the basics from python, torch, numpy etc. And your own construction is way more straightforward than the GPT gives. (My experience learning DL for 2 months😜)
Thanks for the video. I'm a master's student majoring in electronic information. Your video solved my anxiety and made me no longer worry about whether I was progressing too slow and why I couldn't understand the thesis. So just study hard. I have seen and used all the things you mentioned. That's enough.
I'm beginning my conversion MSc in human centred AI and Game development in September, having no computer science degree or strong foundation in maths. I got a C in GCSE. It's a weak point for me but I can see it's crucial in this field. This is really inspiring and it's good to know that I will be okay as long as I work hard. Thank you!
thank you so much for this video Boris Meinardus, I was lost in starting the journey of ML but with this video seems like clear roadmap. Every second of this video is informational #gold.
Really high quality vid with very useful and good structured information! Keep going and you will be big on yt
I really appreciate it!! Thank you very much 💛
I graduated and started working at Amazon. I have already taken a year of machine learning, NLP, CV courses and I have built working but not production ready accurate models on object detection, poem writer, and cyberbully detections, and NanoGPT from Andrej. I am really lost in what is the next step.
While I have the knowledge, I lack the industrial experience. Because I lack the industrial experience, I cannot work in the related fields of AI. Because I cannot work in the related fields of AI, I lack industrial experience.
So this is really been like a chicken and egg problem for me.
If u don’t have a ML PhD or working towards it, you do not have the knowledge u think u have
@@andiuptown1711 how do I have the knowledge of ML PHD without being in school?
Bist ne gute Seele!
Danke für die Motivation.
Great Video , I think book are also as important as course and I would personally recommend the book
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
its a great one for connection theoretical ideas and practical workflow
First I learned Linear Algebra on yt with Strang course. It took me half a year. Now I'm on the challenge to comple all Khan Academy HS and college courses. It's already half a year in. I still have lots to do. Then I'll take specialized math for ML course.
You'd say it's overkill and I waste time. You probably would be right. Also I forget the most of what I learn.
But I just want to tick off all the boxes, so whenever math difficult comes in, I wouldn't be like "oh I have no idea what's going on"
I think you're just doing unnecessarily too much. Just learn enough to get you started. Don't let imposter syndrome win over you. The thing about it is that if you understood some basics you will still revisit the maths and stats as you're working on projects
Thank you for the video. The Machine Learning Specialization course is now 49 USD/month.
Hey Boris, loved the video! I'm starting my AI graduate studies in January and have also contemplated starting a TH-cam Channel explaining the subjects I learn. Just wanted to let you know that you're an inspiration for building a brand for yourself and I'm gonna cheer you on!
Thank you so much for the kind words! I hope my future videos will be as helpful as this one 😊
Also, best of luck with your studies!!
bro please keep it up . we need more informative video like this . Thanks alot brother.
I am not sure that basic math such as linear algebra, probabilty in the level of high school, first year of university is enough. I guess some day you will encounter something like of a glass ceiling because advanced ml requires far more advanced math. I am not discouraging the reader to lower his arms, but if you have a choice, it is better to join university where math is taught on above average level. I have a really strong background in pure math, but even with it i sometimes strugge. For e.g. you might need subjects which are really hard to understand such as functional analysis, stochastic processes, descrete math and etc(subjects which is not present in standard engineering math curriculum). But i am working in the research, not on the ml engineer position, so i guess i'm a little bit biased :)
But, once again, my advise is to study math as much and as thoroughly as possible. Hope it helps
Some of the realest and best advice out there
for myself
1. learn python
2. math
3.jupyter
4.coursera course (andrew)
5. scikit learn , pytorch, tensorflow
6. Andrej Karpathy (build real project)
7. hugging face , kaggle
How much time taken to learn these all?
I was scared that I didn't know enough math after taking linear algebra, but that covers most. I totally agree you can always go back and learn the holes in your knowledge in stead of spending months filling in potential holes.
Highly recommend all the steps in this vid!
p.s. if anyone wants a gentle introduction to the math, I'm working on a series called Math You Need For Machine Learning
Excellent tutorial, starting that course as we speak
Finally a good video on how to actually get started thanks a lot man
I was literally thinking about making a proyect of ML but i didnt know where to start and then i saw this video on my recommendations.
Sounds like a solid plan for my 2024, ty
How's it coming so far?
Everybody is recommending Andrew NG. He must be the best machine learning prof
Thanks a lot for this. I am about to begin my ML journey and this video showed me the way.
I‘m really happy it could give you a little bit of guidance 🤗
Can we start together... By sharing knowledge with each other and helping it could be helpful for both what do you think
@@shani8175 sure
Which year of college you are in?
@@ananyagupta321 last semester :)
have had some non-serious forage into ML earlier but doing it now seriously.
Just love the way you say 'Mauths'!:)
🫣🫣🫣
If starting over in 2024, I’d focus on a structured learning path: begin with foundational courses on platforms like Coursera or edX, dive into hands-on projects and Kaggle competitions, and leverage cutting-edge tools and libraries. Engaging with the latest research and participating in the ML community would also be crucial.
Roadmap is super relevant, contemporary, practical and pragmatic. Coined expectations about schedule are just inadequate. Typical yesterday-neophyte's, "second project" misestimation. Basic python+basic numpy+basic pandas from scratch, in "a few weeks"? Seriously? Even in "a few months" it will be a challenging task for most of students.
Wonderful upload. Thank you!
The courses are not free.
Does anyone have alternatives?
Happy to know I followed the right order in the first 4 steps! Thank you for recommending Andrej's course; I subscribed to Ng's Course but I couldn't keep up with backpropagation :( Will include this step in my learning journey!
is that course really free bc the only website i can find is with a 50€ subcrition sry to bother you ;D
2:14 I don't remember by calculus class looking like that! :)
thank you for your valuable information 🤩 it really helps me
I‘m really happy I could help you ☺️☺️
That machine learning course by andrew ng is not free?
hey andrew, the machine learning course is not free it seems, any solution to that???? how do i get it for free??
+1 its not free
This video is the coolest, funniest, most informative video I have ever seen about my major that made me fall in love with it all over again!
I don't see Andrew Ng's course being free, am I looking at the wrong direction?
its paid now
It is possible to apply for financial aid on Coursera, so you can get a reduction or possibly access courses for free.
This is something I need. Thank you ML Guru!
A “Meta professor” is not a standard academic title. However, it might refer to one of the following:
1. Meta as a Topic: A professor whose area of expertise involves “meta” topics, such as meta-cognition, meta-analysis, or meta-ethics, which explore the underlying frameworks or methodologies of a given field.
2. Meta, the Company: A professor affiliated with or collaborating with Meta Platforms, Inc. (formerly Facebook), particularly in research fields like artificial intelligence, virtual reality, or social media studies.
3. Self-Referential or Philosophical Context: Someone who studies or teaches about the nature of teaching, learning, or academic structures themselves (e.g., “a professor about professors”).
Mitchel stark is now a software specialist
I wish you had posted this 6 months ago but anyways I can use this video for revision roadmap thanks mate keep it up👏
Would you do a video with a full practical project using all these tools?
Big thanks, im off to achieve great things. 🎉
Stark Brudi, weiter so!
🚀🚀🚀
Thanks man you're great help as always!!
I‘m happy my content can actually help!! 😊
Awesome video, I just subscribed!
Hey! Thank you for this guide. However, Andrew Ng's specialization's free audit version doesn't include notebooks and assignments and things, just the video content. How do you suggest I get some hands-on practice through other means? I don't know yet if I want to invest so much money into the courses.
pure gold, thank you! what kind of roles can I apply after those courses?
Thanks 🤗
You can in theory apply to every ML opening you see, but at some level of company the courses alone will not be enough. You will need to demonstrate skills through projects and experience, that‘s why I added the last point in the video :)
But there are of course other ways to stand out and I‘ll actually upload a new video covering more tips on exactly that on Sunday. Perhaps that video might be useful to you ☺️
Thank you so much sir this video has made me clear on how to learn machine learning🙂
You're voice I find easy to listen to and understand. You have a new subscriber.
Wow an amazing video!! Thank you
...
When you say Reimplementing a paper and recreating the results what's that supposed to mean? What kinds do you suggest? What are the actual ones that have the chances to have an impact or basically to make a difference (basically solving a problem maybe)
And finally what do you recommend?😊😊
I'd imagine he means academic papers where machine learning is used to get some result. For learning purposes, very old ML papers introducing what are now considered basic and standard ML algorithms would perhaps be good, but also modern papers from non ML subjects where older, basic ML is being used to solve a problem in those subjects. The latter are also more likely to have accessible data sets.
Modern ML papers are likely to be about cutting edge research into improving existing algorithms or inventing new ones and probably less useful as a beginner.
To reimplement a paper, read the paper a few times to understand what they are doing, get hold of the same or similar data, and see if you can implement the machine learning techniques used in the paper to get the same or similar results.
@@johnlally3506 ok thank you very much
I enjoyed Andrew Ng’s ML specialization, but do with it was more hands on. It’s great for learning about the algorithms but you don’t implement much of the projects yourself.
Nice video, how would you find research papers though?
i guess the machine learning specialisation course is no longer free ?
As a farmer I just bumped into a wrong channel.The boy is speaking in tongues.
I know Python and I've taken math up to linear alg (aced all finals in math too) I just need to brush up on my Python and I should be able to deep dive into that ML developer stack!
I might be jumping the gun a little, but I feel like in the near future polars is going to be the favorite dataframe tool instead of pandas. I definitely recommend learning it as well, it's a lot more intuitive and faster and a lot of frameworks are adopting it.
brother, hope you gain more. thanks
Hello Boris sir, thank you for your youtube videos. Moreover, I want to ask whether buying M4max apple with 128 gb laptop for machine learning, AI and Data science or buy M4 max with 36 gb and use cloud for higher data computation. It would be our pleasure to have on best laptop idea for these categories.
Y pytorch over tensorflow? Explain deeply 😅
Can you please explain more on what do you mean by rewriting a paper and specifically on what, I am super confused about this
as a programmer whos been at it since the 90s I can tell you.... debug your ML code and have tests for everything. A small bug in the reward function (and many other places) could cost you days in wasted training time! bugs bugs bugs..... fix em.
What do you think about JAX Boris? Seems to be picking up in popularity and in jobs.
Very helpful and inspirational video, love from TU!
Thank you!! ☺️💛
thank you, this video is really helpful.🎉
🤗 I‘m really glad it helped you!!
Thank you for such a clear roadmap.
Machine Learning is not easy.
I found myself having some pleasant success by taking MIT's 6S191 course on Introduction to Deep Learning.
Here are some pre-requirements:
Maths:
Calculus
Algebra
Probability Theory
Programming:
Python
The course is teached on site but the teacher recorded all the classes and gave a lot of online resources to everyone.
I agree with everything except "everything should take few weeks". Depends how much time you learn a day? 3 hours a day won't let you finish basic course of linear algebra and matrix computations. And there is so much other stuf...
thank you , i want to take your opinion about :
which one should i learn first ML or Ai ?
lol