2020 Machine Learning Roadmap (87% valid for 2024)
HTML-код
- Опубликовано: 9 июн 2024
- Getting into machine learning is quite the adventure. And as any adventurer knows, sometimes it can be helpful to have a compass to figure out if you're heading in the right direction.
Although the title of this video says machine learning roadmap, you should treat it as a compass. Explore it, follow your curiosity, learn something and use what you learn to create your next steps.
Links:
Interactive Machine Learning Roadmap - dbourke.link/mlmap
Machine Learning Roadmap Resources - github.com/mrdbourke/machine-...
Learn ML (beginner-friendly courses I teach) - www.mrdbourke.com/ml-courses/
ML courses/books I recommend - www.mrdbourke.com/ml-resources/
Read my novel Charlie Walks - www.charliewalks.com
Timestamps:
0:00 - Hello & logistics
0:57 - PART 0: INTRO
1:42 - Brief overview of topics
3:05 - What is machine learning?
4:37 - Machine learning vs. traditional programming
7:41 - Why use machine learning?
8:44 - The number 1 rule of machine learning
10:45 - What is machine learning good for?
14:27 - How Tesla uses machine learning
17:57 - What we're going to cover in this video
20:52 - PART 1: Machine Learning Problems
22:27 - Categories of learning
26:17 - Machine learning problem domains
29:04 - Classification
33:57 - Regression
39:35 - PART 2: Machine Learning Process
41:57 - 6 major steps in a machine learning project
43:57 - Data collection
49:15 - Data preparation
1:04:00 - Training a model
1:23:33 - Analysis/evaluation
1:26:40 - Serving a model
1:29:09 - Retraining a model
1:30:07 - An example machine learning project
1:33:15 - PART 3: Machine Learning Tools
1:34:20 - Machine learning tools overview
1:38:36 - Machine learning toolbox (experiment tracking)
1:39:54 - Pretrained models for transfer learning
1:41:49 - Data and model tracking
1:43:35 - Cloud compute services
1:47:07 - Deep learning hardware (build your own deep learning PC)
1:47:53 - AutoML (automatic machine learning)
1:51:47 - Explainability (explaining the outputs of your machine learning model)
1:53:38 - Machine learning lifecycle (tools for end-to-end projects)
1:59:24 - PART 4: Machine Learning Mathematics
1:59:37 - The main branches of mathematics used in machine learning
2:03:16 - How I learn the math for machine learning
2:06:37 - PART 5: Machine Learning Resources
2:07:17 - A warning
2:08:42 - Where to start learning machine learning
2:14:51 - Made with ML (one of my favourite new websites for ML)
2:16:07 - Wokera ai (test your AI skills)
2:17:17 - A beginner-friendly path to start machine learning
2:19:02 - An advanced path for learning machine learning (after the beginner path)
2:21:43 - Where to learn the mathematics for machine learning
2:22:23 - Books for machine learning
2:24:27 - Where to learn cloud services
2:24:47 - Helpful rules and tidbits of machine learning
2:26:05 - How and why you should create your own blog
2:28:29 - Example machine learning curriculums
2:30:19 - Useful machine learning websites to visit
2:30:59 - Open-source datasets
2:31:26 - How to learn how to learn
2:32:57 - PART 6: Summary & Next Steps
Connect elsewhere:
Get email updates on my work - dbourke.link/newsletter
Support on Patreon - bit.ly/mrdbourkepatreon
Web - dbourke.link/web
Quora - dbourke.link/quora
Medium - dbourke.link/medium
Twitter - dbourke.link/twitter
LinkedIn - dbourke.link/linkedin
#machinelearning #datascience 
Наука
**2024 Update:** Hello hello! Welcome to the 2020 machine learning roadmap! A few people have asked, "is this still valid for 2024"?
The short answer: yes, mostly.
However, it does not include anything on LLMs or generative AI.
When I made this, LLMs and generative AI were still being figured out. Now they work. Really well.
Not to worry!
A new roadmap is in the planning stage.
I'll update this comment as more progress gets made.
Leave a reply if there's anything in particular you'd like to see :)
In the meantime, happy machine learning!
Came across this roadmap back in 2020 when i was joining University, bookmarked it and never looked back. Moved on to WebDev, CV and Leetcoding.
Now in 2024: regretting that decision to not explore/learn ML. I'm finally starting ML and came back to this vid just to see it gettting updated for 2024.
the rising of chat gpt makes me want to get deeper into LLM, especially the ones from scratch, now im currently learning ur 25H tutorial on PyTorch, but planning it to watch until i am ready to step into LLM,
Hi daniel Bourke i am waiting eagerly for your updated roadmap for machine learning 2024
Thanks for this amazing roadmap !
Prepare new road map
Daniel you, my friend, are a legend. It's so good to see such passion and enthusiasm for your craft, and the ML community is glad to have someone like you blazing a trail so that the new members can follow.
goat for sure
Agreed, you are so appreciated Daniel
@@ayaan3429 Hi, could you let me know if we have to go through these resources just in the order he mentioned it? Like ML problems first and ML Process next and so on?
@@ayaan3429 *Tewari : )
@@arima973 1
Presentations in the technical field such as this rarely have this much quality knowledge packed into them but it's even rarer that they are this aesthetically pleasing!
you obviously are not technical - you must be one of those "visual people" 🙄
I've never left a comment on RUclips, but I feel like I MUST DO after watching this video. It is very organized and useful to understand how we approach ML and keep learning it. I appreciate you made this great one.
Looking forward to this my friend! Great thumbnail 😉
Master ken jee was here ♥️♥️♥️
Look who's here..
@@hardikkamboj3528 I'm everywhere! haha
@Ken Jee Eh man! Glad to meet you here!!!
I really like your organization reminds me of a visual representation of what a tool box would look like to a mechanic
Thank you! I'm showing this comment to my friend who loves cars
Daniel, this is an amazing video. I came back to say thank you for putting extensive work to make this video. The map, instructions, and resources are super helpful. This is the best guidance I have seen so far!
Thank You Daniel
Thank you Daniyal! So glad you enjoyed it my friend
You just contributed to make the world a better place!!!
I wish if there is a roadmap like that for every subject in the world.
Daniel, thanks for this superb video. As someone just starting out on this road, it's very easy to get sucked into the fine details, but this has given me a much better grasp of the big picture. I love your philosophy of not learning for learning's sake, but using this knowledge to build things that matter to people. Keep doing what you're doing!
what are you doing now
First time I watched a 2h+ video without sleep all the way to the end.
WOW!!!! Thank you for the incredible amount of work you put into this project, it is truly an amazing creation!! Very useful and relative information and the interactive map is really cool! Stupendous!
Thank you Zach! So glad you liked it
Finally someone explains ML in an understandable, fun way with a lovely accent :)
With a name like that id find any accent lovely too
This is by far the most visual map ever created for ML. Daniel is a genius. Energy, communication, value is the most I have ever experienced. Keep this up
This is probably one of the best videos out there, congratulations! Perfect compass!
Thank you Leo! So glad you enjoyed it
Thanks, Daniel!
This is epic and helped understand all of the ML more broadly, in a more connected way.
I'm enjoying this so far. I just started using whimsical and I already love it!
What a valuable resource ~ thanks for taking time to produce this, Daniel. I watched David Malon’s Harvard online CS 50 & 100 and wondered where that guy was when I was in high school ~ you both create engaging content. There are a lot of people who appreciate what you do.
David Malon is epic! Same with CS50!
Really Really Really appreciate the time and effort you put into these videos by researching and providing the right info for people to enter the Machine learning space! Keep up the great work man! Cheers.
Thank you so much legend, so glad you liked it, I really appreciate the kind words
Hello Daniel, Just wanted to say thank you for sharing your knowledge and resources. I have been following you for weeks now and your channel is my favorite to learn about machine Learning... very inspiring and insightful ! Thank you and keep up the good work mate!
Cheers, Aymane
Thank you Daniel for the incredible effort. Your passion for ML is so apparent thru out the video. Thanks much!
Thank you Daniel for putting together such an awesome roadmap! It helped me connect all the dots. As you said, there are so many resources out there on the internet but the challenge is to come up with the right path to achieve the goal. I was so confused until I saw this video. I think I have a lot more clarity now. Thank you once again.
Thank you Velusamy! So stoked to hear it helped you
This is probably the best roadmap ever!
Best 2 hrs and 30 minutes ever spent!
Thank you Anubrata! Glad you enjoyed the machine learning feature film
This is gold., Thanks mate, just about to begin my journey of learning Data Science and Machine Learning and this has definitely helped me to orient myself within the field. All the best.
Daniel, thank you for such a detailed and beautiful explanation of ML. It is making our learning journey much easier!
This was literally mind blowing, thank you for taking time to create the roadmap. I'm a junior at a university studying CS, and I just decided during my sophomore summer quarter that I want to specialize in machine learning/data science. But it's been overwhelming and I feel I don't have much time left since I'm already starting as a junior. I hope I can make it out alive and successful; Im gonna utilize all your resources and books and courses in the best of my abilities. Cheers!
how is the progress
Best thing happened to me so far in 2020😌
So glad you enjoyed it!
think POSITIVE, we soon all will be fine
@@mrdbourke I really don't know how I can fully show you my appreciation. THIS IS AMAZING. Thank you so much m8! You're brilliant.
You're a life saver! I was feeling overwhelmed because I was just beginning 😃
Watched the first hour and I would say this is the best foundation I've found so far. Thank You! Nice work brother.
finally after 8 years of watching videos, youtube has recommended smth really good)!
They’ve finally worked out the recommendation engine!
After 5 years of you tube
സത്യം,
Pierre
Mon Entreprise
GCP
Apache
He is Udemy instructor I guess 😀
This dude is great 🤣love how much fun you're having
You guys are so energetic!! Gratitude and greeting from a newcomer on machine learning!
Thank you for sharing this ! Love it. It's the best video I came so far on ML
for intermediate level machine learning practitioners this an excellent reminder, a detailed machine learning landscape.
Very huge contribution to the community.
you did an Excellent job Daniel. Wish you the best
Yeah but for a absolute newb like me, I do t know where to begin, or how long this going to take. I just wanted to create a few AI to work for me
"Data and Model preparation" would make sense from a process perspective. Collection and preparation are not steps of a process of building ML system. (Many of the subheading aren't process either, but concepts and their explanations for understanding.)
I love the concept map and it's graph theory connectivity.
Great teaching material. Truly inspirational. I've been looking at Data Analytics, Machine Learning, Neural Networks, Artificial Intelligence, and Time Series modeling for a while now as an effort to narrow down a PhD dissertation topic, and this really pulls together a lot that I've come to understand and see differently since starting this journey. This is such a great narration of ML that I'll have to watch it again and improve my notes.
I've been exploring the nature of data to see about other angles of attack and I'm impressed at many of your summaries. I've looked a lot at graphs and the information they convey. I've explored your data types in depth. Nominal, ordinal, interval, numerical. Time series has been an interesting dimension as it forces you to see that people can only conceptualize and create systems that are discrete. We have to break a continuous reality (data) into discrete concepts like a person (or an object to be more precise, like the ship of thesius concept, if you cut off my hand, am I still me?) or a word (with an essences of structured properties and characteristics).
Timestamps:
0:00 - Hello & logistics
0:57 - PART 0: INTRO
1:42 - Brief overview of topics
3:05 - What is machine learning?
4:37 - Machine learning vs. traditional programming
7:41 - Why use machine learning?
8:44 - The number 1 rule of machine learning
10:45 - What is machine learning good for?
14:27 - How Tesla uses machine learning
17:57 - What we're going to cover in this video
20:52 - PART 1: Machine Learning Problems
22:27 - Categories of learning
26:17 - Machine learning problem domains
29:04 - Classification
33:57 - Regression
39:35 - PART 2: Machine Learning Process
41:57 - 6 major steps in a machine learning project
43:57 - Data collection
49:15 - Data preparation
1:04:00 - Training a model
1:23:33 - Analysis/evaluation
1:26:40 - Serving a model
1:29:09 - Retraining a model
1:30:07 - An example machine learning project
1:33:15 - PART 3: Machine Learning Tools
1:34:20 - Machine learning tools overview
1:38:36 - Machine learning toolbox (experiment tracking)
1:39:54 - Pretrained models for transfer learning
1:41:49 - Data and model tracking
1:43:35 - Cloud compute services
1:47:07 - Deep learning hardware (build your own deep learning PC)
1:47:53 - AutoML (automatic machine learning)
1:51:47 - Explainability (explaining the outputs of your machine learning model)
1:53:38 - Machine learning lifecycle (tools for end-to-end projects)
1:59:24 - PART 4: Machine Learning Mathematics
1:59:37 - The main branches of mathematics used in machine learning
2:03:16 - How I learn the math for machine learning
2:06:37 - PART 5: Machine Learning Resources
2:07:17 - A warning
2:08:42 - Where to start learning machine learning
2:14:51 - Made with ML (one of my favourite new websites for ML)
2:16:07 - Wokera ai (test your AI skills)
2:17:17 - A beginner-friendly path to start machine learning
2:19:02 - An advanced path for learning machine learning (after the beginner path)
2:21:43 - Where to learn the mathematics for machine learning
2:22:23 - Books for machine learning
2:24:27 - Where to learn cloud services
2:24:47 - Helpful rules and tidbits of machine learning
2:26:05 - How and why you should create your own blog
2:28:29 - Example machine learning curriculums
2:30:19 - Useful machine learning websites to visit
2:30:59 - Open-source datasets
2:31:26 - How to learn how to learn
2:32:57 - PART 6: Summary & Next Steps
Now, this is something else. The best instructions to learning ML I have ever seen, thank you Daniel for the effort you put in this. Now I can really start to learn ML like a true Legend, thank you sir!!!
Lol, man, You. Are. Amazing. Just thank you so much. I'm a software engineer and I don't know any ML engineers in person. It is so helpful to get something like this from the man from ML industry. So many thanks.
You literally have everything I was looking for. Thanks!
Thank you Win! So stoked you enjoyed it
Great work Dan 👍🏽 , My learning path almost aligns completely, One thing i feel is missing is "Joining a local community of ML enthusiasts around".. it can be a lot more difficult being a lone ranger.
Okewunmi! Thank you thank you thank you, that is some great advice my friend! Joining a community is definitely valuable.
@@mrdbourke L. P p po. M. M. M o. L ok. M pm
This is massive! Can't wait to explore these resources on my own. Huge thanks!!
Incredible!!! Thank you for your hard work and spending the time to create this compass that makes my data journey a lot more clearer!! Cheers!
2:36 "don't want this video getting too long"
*looks at the duration of the video*
If you wanted a feature length film on machine learning, I got you!
*Laughs in 2x
@@mrdbourke It's good. The long stuff is always the good stuff.
@@anprabh1 lmao
jayms that's what she said.
You sir, are a legend. Unbelievable helpful, thank you so much for this!
Yes Pandagoggles.
What he said.
Wow, such a nice and organized course, the best resource I found so far! Thank you very much Daniel
What a perfect video for people what wants to start their learning of machine learning but got no idea where to start with!
Thank you Daniel, 😊
This is the best movie I have seen in my life , now I have enough energy to boostup.⚡🔥
learnt a lot. It cleared all my queries.😇
I really love your setup.😁
Thank you Sai! Glad you enjoyed it legend! All the best my friend
Best roadmap for any AI/ML aspirants! . Thank you Daniel for such a comprehensive explanation full of valuable information complemented with inspiration and encouragement.
Thank you Vidhya! I appreciate it :)
I'm only halfway through and I think what you created is amazing and extremely helpful! Thanks so much!
Thank you Jenny! Stoked you’re enjoying :)
Absolute Legend mate! Well done!
Hey there! Happy New Year! Speaking of the new year, you might be wondering "is this still valid for 2021?" and the answer is yes, it's still valid for 2021.
However, you might notice a few changes to the websites mentioned throughout video (some have had a design change), including sites like Made with ML who've recently pivoted: madewithml.com/pivot/
All of the main concepts remain valid for the new year.
If anything changes drastically, I'll look to update/make a new version of this video.
In the meantime, happy machine learning!
YOU HAVE SAVED ME MANY YEARS!!!
I swear, portions of this could be used as an SNL skit with Andy Samberg trying to explain or sell something to me, a dumb idiot...
Sat through this beast (at 1.25x speed; perfect pace & Aussie accent). Gave me lots of clarity as I learn better from building than from watching videos. Guess I won't be needing Coursera Plus (yet)! Thanks so much Daniel!
Initially we can meet
sorry
I've just started to investigate ML as I'm a project manager, not a coder. So this introduction was the best I've seen so far, and I've been looking around for weeks. I particularly applaud the emphasis on being a chef, not a chemist. If you want a student to really get into a subject, you should start by having them fall in love with the subject, not begin at the molecular level. Your enthusiasm and clarity throughout this presentation supported that chef metaphor wonderfully. The only thing I would be interested in hearing your thoughts on are possible "fun" projects for beginners. I am not particularly interested in computer vision, for example, but using ML to create a custom audio engine, or ML to track personal bio-metrics, or something like that. I would love to know your ideas on some fun, easy projects. Thanks again for the wonderful work.
Awesome effort, incredible great job, and most important of all, thank you for your generosity of sharing this!
You are are just so inspiring..You work so hard and there is still this energy you have that just motivates me..
You are truly a legend. 💟
I've been self-studying full-time since January. Had to make my own curriculum and everything. Really interested to see how our roadmap and resources line up.
same here...
One of the viewers reached out to me via email so I thought I'd share it here for anyone else that was curious. This is copied from my email to him so it's LONG.
I mainly used textbooks and Stanford/MIT lectures and coursework freely available on RUclips and the courses' websites.
I guess the biggest insight I learned from self-studying and everything is that the field is developing rapidly. It's getting easier and easier to access certain aspects of ML/DL without necessarily needing a deep understanding of the theory and academics to start working with them. This isn't to say that the foundations aren't important, but that you should actually start getting some hands-on experience sooner than you might think.
If I was to distill the curriculum I had and maybe do things over from scratch I'd probably take the following approach.
Start with basic probability and statistics on Khan Academy and the Statistics and Machine Learning playlists by StatQuest on RUclips. Use python to recreate what you can during those courses (combinatorics, probabilities, mean, standard deviation, etc). Look for standard library tools that can do it as well! Like sampling in the standard library's random module (this came up in a coding interview and I tried to hand-code something that could've been solved in one line!).
Learn to clean data. Numerical, categorical, timedate, EVERYTHING! (Datetimes ate up 2 out of 3 hours I was giving for another coding interview).
Learn how to do a couple basic linear and nonlinear ML models with sklearn (single and multinomial linear regression, random forests, gradient boosting, svm). Add in a video or two on regularization (StatQuest has some I think).
Make a couple models or so on jupyter notebook. Get comfortable with the commands and cleaning and try it out on a problem you're interested. Pick a random dataset and see what it's like to really clean it and have to form a pipeline to feed your model. The modeling is the easy part.
If you're comfortable or bored, go to the Deep Learning for Coders course by FastAI. Jeremy Howard's videos are great and you can immediately start fiddling with things. He also has a free book (FastAI Book) which covers a lot of topics and goes alongside the course. My favorite part is that the course has a section on how to actually deploy these things and not let them die in a forgotten jupyter notebook somewhere.
The truth of the matter is that the majority of the people will not be developing state of the art algorithms or libraries. The FastAI course will kinda show you that. Think of something that interests you, something connected to a hobby or thought.
If you get interested in learning deeper theory on Machine Learning, check out Intro to Statistical Learning with Tibshirani, Hastie, and Witten. For Deep Learning, find Karpathy's CS231n series on youtube then watch the updated version of the course in high speed to find what advances have happened in the last couple years. A very dry but amazing book is Hands-On ML. The first two chapters alone cleared up so much for me as far as how a real project is structured.
Extra: Learn FastAPI, streamlit and plotly/dash and start cranking out some webapps.
@@mperez671 thanks buddy
Dude this is insaneeeee!!!! I love you
Thank you Arpit! Glad you enjoyed it!
absolutely amazing stuff @Daniel, really appreciate your quality work. I am finding this roadmap tremendously helpful. Please keep on adding such insightful videos.
Glad you enjoyed Satish!
One of the best guide videos I have ever seen. Thank you!
Hey, you are awesome, you have given so much of (WELL ORGANISED) content to everyone.....
Great!!!
I was wondering if you can make a similar one for Deep Learning???
Eager for it.
Thank you so much Jeet! There’s a fair bit of deep learning in this one, but if you’re looking for a dedicated deep learning one, I’d check out: github.com/dformoso/deeplearning-mindmap (these are what I originally based the roadmap on)
Me checking the phone during a Pomodoro break: 'Oh, Dan uploaded a video.' I click it. Dan: "...I'm not going to hold you up for long. ..." - I look at the duration of the video. Me: Oh no...
Hahaha! I give permission to skip my videos in order to maintain concentration
I've been considering myself as a legendary procrastinator before watching this video.
didn't even pause once, watched till the end.
the most detailed guide, really, appreciate that
I will be starting this now. From the start. Thank you for the detailed guide.
Man like Daniel sitting on 1000+ Medium notifications 😂😂 5:33
Respect bro hahahah
hahaha my brain can't handle them all so I just let the number increase
@@mrdbourke 😂
@@mrdbourke 😂✅
you explain things exactly the way i think, sound like in explaining this stuff to myself. i also realise why people lose me when i'm explaining things to the haha. but nah i got what you would putting down and loved the professionalism of this video. That food example in the beginning is an amazing way to explain ML
Thank you! So glad you enjoyed it. I liked the food example too haha
This has been very informative especially now that I am working on my capstone project. Thank you very much! Subscribed.
God bless you, sir. This information is a Godsent! I'm very new to ML with a burning passion to help develop self driving cars and so many moments I want to give up because I'm aimless wandering around a sea of infinite overwhelming information. Your video has not only reignited my curiosity but has GIVEN ME A PATH to actually navigate this powerful journey. Thank you so much for gifting us this valuable knowledge. 🙏
pretty good, now we just got to teach 10 years old's this, change the future
Same here, also looking forward to this video 😃👍
Amazing video, man! The best I've seen so far!
Thank you and congratulations!
Thank you! Glad you enjoyed it
Thank you very much Daniel. This video is so valuable! Amazing teaching and communicating skills!
Brah you came out with a machine gun with this content today. 😂🔥❤️
Hahahaha thank you brother! Thought some people might be craving a movie-length field guide to machine learning
I have come up with a Life Goal of verifying everything so I can not be lied to anymore. That project is so vast that the Table Of Contents has become huge. I REQUIRE this kind of information to organize and make my research available to the world. I literally couldn't do it without this materia!!! Your enthusiasm sounds intimately familiar 😁😁😁
I set a goal of reporting in 35 years. This will enable my books/website material. I will have fun getting down to a 3 minutes summary in English. 15 languages total, for less than 1 hour of talking.
This material will end all the lies that I have functioned under.
Now how to structure my data. Cosmology should be interesting area to START! Electric Universe vs gravity only models for fun and profit👍🏻👍🏻👍🏻😁
Hey everyone! I wanted this comment to be the place where you can share, where you are at this point of time into the roadmap. All the best.
95 % confidence interval. Thank you for this amazing mind map
Thank you very much for this roadmap, I will be using it, no doubt! And the video cleared some of the basic questions and fears I had.
Great, is this still valid for 2022?
NOTE!!!!! Please also tell us the resources where we should learn all from the ground zero to advance
Check out 2:17:17 :)
@@mrdbourke ty brother!!!
Wow never seen a 2+ hr roadmap video, grt work!
Of all ML-related videos I've watched so far on youtube: This one is definitely the best. Particularly I like that you also mention other resources available for learning, in which you or your other colleague are not involved in. Makes you seem like a really nice guy. Greetings from Germany
Thank you so much! Stoked you enjoyed :)
Why still nobody did "DNA - to - appearance" Deep learning alghoritm for animals and plants ?
@Newthon Raphson four five one any git or links for your research until now? I am very interested
Because you havn't written it yet.
Best thing on RUclips right now.
Also 2:36 *Don't want video to get long*
Video duration 2 hours lol
😂😂😂
This is the best machine learning introduction video than any others I have seen or sessions that I have attended.
This really brings the big picture together. Great presentation.
the sparke effect was breathtaking!!!
you just made a huge contribution towards learning communities . What you have created here is a milestone . I knew most of the things you discussed here but still i was opened huge amount of resources i didn't know existed . Keep up the good work .
Thank you so much Preetham! Glad you enjoyed it :D
glad i watched this video, just after i made up my mind to go with machine learning. this is sooo helpful. i love all of it. Great job Daniel and thanks alot!!!!! Subscribed :D can't wait to explore more of ur stuff as i move forward this route.
This is the most awesome detailed explanation on machine learning. You sparked a light for ML in me. Thank you very much!!!
This is incredible. Thanks for creating this.
Sweet. Can't wait for this! ❤️
This is excellent, well presented and organised. Thanks for the time and effort put into this 👍🏼
Thank you for the kind words
mindblowing work Daniel!!! Thank you very much for such a roadmap!
Daniel mate, a great comprehensive visual representation of a topic that can be a daunting when starting out. Love the positive waves and the mullet; best in Brisbane I reckon. This is a massive effort to help others learn. Cheers 👍🏽
Thank you Ami! So glad you enjoyed it. And the comment on the mullet made me laugh out loud hahaha
Thank you so much for this. This is exactly what I’m looking for.
Amazing work!!! thanks for existing in this life, bro!
wow thanks for this mate, its so much easier to understand when everything is laid out like this, great work!
Glad you enjoyed Byro!
Wouldn't miss a single update from this channel. Daniel has been a brilliant instructor for me in his Complete ML and DS course (which I would highly recommend to the newcomers)
Thank you Rahul! That’s very kind of you
mate, gotta say, nice job, i appreciate the effort and sourcing of links and definitions...
That's a pure gold. Thank you Daniel for amazing roadmap and amazing presentation!
Thank you Michal!
This is such an awesome learning map for ML! It has everything! Big thanks to you, Dan!
Thank you Ni! I really appreciate it
Thanks Daniel. This is the single most comprehensive consolidation of every resource and avenue related to ML. May you live well ^^
Thank you Rajasimhan! So stoked you enjoyed
You're a great educator bro, thanks for this vid, it probably took a ton of editing
This information is gold, please keep making stuff like this!
You're awesome Daniel! Thank you for all the advice and resources in this video!
Glad you enjoyed Eddy!