I thought "Hands-on Machine Learning with sci-kit learn, Keras and Tensorflow " is a good one. It gives us intuition about how things work in a practical way.
Nice video. Here are some other books that are worth reading/studying. 1) Elements of statistical learning (if you have an advanced math background) or Introduction to Statistical Learning by Tibishirani et al. 2) Machine learning: A probabilistic perspective by Kevin Murphy [I am not a die hard fan of this book but it works well for most cases] 3) The book of why-Judea Pearl (An amazing "semi" technical book that talks about causal inference; a critical piece that's missing in machine learning and deep learning)
Grokking Deep Learning - best book! does not use popular frameworks and libraries this is a wonderful book. if you know the same then tell me. Andrew Trask great teacher
I would like to recommend "Pattern recognition and machine learning" by Christopher Bishop and "Understanding Machine learning" by Shai Shalev both for the Mathematical issues involved in research algorithms and more
I have "Python for Data Analysis" and "Hands-on Machine Learning with Scikit-Learn and TensorFlow". Other book I recommend also "Python Data Science Hanbook" the author is Jake VanderPlas.
Cheers to Daniel, Keep leading.... and finding the best resources for you (and us). Learning happens different for everyone but you help show how to gut it out.... and get substantial amount learned in a short period on you path to success.
Wow, what a great guide! The Hundred Page Machine Learning Book is indeed extremely good, especially for intuitions. As a CS undergrad, I was trying to use The Elements of Statistical Modeling (recommended by my professor), which is insanely hard to understand xD Anyway, thanks Daniel! You definitely deserve a lot more subscribers:)
So glad you enjoyed Tianyao. I agree, I love the 100 page ML book. There’s also going to be another 100 page ML book by the same author on ML engineering (coming soon). When it comes out, I’ll read it and post a review!
Nice list! I have DL and the 100 page book from this list. Love those two specifically for the reason they dont have code. I have a few coding books but the risk or problem there is the code will change as you correctly pointed out. The next level after DL are some phd level math books 50% written with greek symbols lol
Time to buy Physical copy of Deep learning(Ian Goodfellow) Book, since I was reading it online for the most time. Amazing Video Daniel with great content as Always Brother.
9:20 "You should be reading at all times a book that is slightly too hard for you to read." 😊 Well said. I like discovering books on my own but I also like hearing about the books other people find useful. Thank you for sharing your recommendations. I've only read two of these books so far. Have you read "Mastering Feature Engineering" by Alice Zheng? Do you have any recommendations on books about feature engineering?
Ooo I haven’t read that one! Most of my feature engineering knowledge has been through practices from the fast.ai courses, I can’t recall reading much on it.
Hey Daniel, awesome content as always. I'm stuck after finishing the Deep learning specialization course. What should I do next? Do you have any project or course recommendations?
Hey Hari! Thank you my friend. I’d take a moment to think about what your goals are. Are you trying to get a job? What kind of role is it? What are the best next steps you could take to that? Potentially you could start reaching out to people in the field or who work at the companies you’d like to work for and ask them what’s their recommendations. Otherwise, finishing a course is a great time to put together what you’ve learned in a project of your own. This will help to consolidate your knowledge. You can also share your work through GitHub and a blog post which you can show future employers as examples of work you’ve done.
Of course people are different, and it's a matter of personal taste, but here's my unsurpassed Top 3 (in order of LEARNING, not the quality): 1. "Mathematics for Machine Learning" by Marc Peter Diesenroth, A. Aldo Faisal and Cheng Soon Ong, Cambridge University Press, 2019. This has ALL the math you will need (except for the deep learning algorithms alas), in the best possible form, and NOTHING you won't need. You will really master PCA and SVM after defeating this book. Every chapter has a lot of exercises, and, believe me, even if you consider the chapter totally clear, you aren't going to do these exercises easily! 2. "Fundamentals of Machine Learning for Predictive Data Analysis" by John D. Kelleher, Brian Mac Namee and Aoife D'Arcy, The MIT Press 2015. This book not just explains the basic ML methods to you, it really shows the right METODOLOGY, which is extremely important. It represents everything you need to know about the basic non-deep methods (regression, classification and clustering), including some theory and the detailed explanations. My DS course was too hasty, and I didn't even realize that I don't have to encode categorical variables for Decision Trees and Random Forest until I read this book! 3. "Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow" by Aurelien Geron. Grab only the 2nd edition, it describes Tensorflow 2.0! Awesome book. Simply awesome. We have used it in our course for Deep Learning, and it describes them all: ANNs, CNNs, RNNs, even encoder-decoder and many others. All with good and working examples. Of course, "Deep Learning" by Ian Goodfellow and others is a great book, but I feel it's too academic. I guess nearly half of the material you will never need, but who knows? I will take it after I will know the Top 3 extremely well. "One-hundred page book" is a great reference once you already know all the details, but not earlier. And while I am very grateful for Wes McKinley for Pandas, I still prefer Theodore Petrou's "Pandas Cookbook", since it has everything I need, well-described and with good examples.
I should probably add my #4: "Mastering Machine Learning Algorithms" by Guiseppe Bonaccorso (2nd edition). A great book, with tons of practical demonstrations and examples. The second edition is fresh (published in 2020) and uses Tensorfow 2.0. This book "patches the holes" in my learning plan, having a lot of Deep Learning math.
@@mrdbourke I am so sad that i didn't find your channel when i was starting out, people who follow you are lucky. P. S. I highly recommend the book. Especially for people with no Calculus background. It still talks about the Math behind machine learning, but calculus is mininal. (i. e. When they introduced Linear Regression, they solved for the close form of the function of the parameters, instead of Gradient Descent. Although that still requires calculus, they didn't actually compute the gradient.)
@@ralphie123 Agreed! Tho ESL is slightly harder, as it shows PDF's of Continuous Distributions, and hence it uses integrals to get the probability. So might turn people off.
Great list! Thank you for sharing. I would like to recommend "Python Machine Learning - Second Edition" by Sebastian Raschka and Vahid Mirjalili. This books really achieves a nice balance between practical implementation of ML models using SKLearn and the gory algorithmic/mathematical details.
Deep learning Yoshua Bengio, Aaaron Courville and Ian Goodfellow is a book for those who master all concepts of deep learning and have strong mathematical background (at least 2nd year of college math class) , and i don't recommend it if you want to start deep learning because it is for expert and it will threaten you 😄
That's a great book but it wasn't a "starter" book for me. 😁 It showed right away that I had huge gaps in basic statistics skills, so I had to go back to an Introduction to Statistics course. I agree with your recommendation though. It's a must read for any data scientist or machine learning engineer.
It's not about what ml is now, it's what they can become. DL bring the most likely culprit in my mind. I know dl is ml, but dl seems like the most likely culprit
Am a beginner in this field and your video is so helpful for me now so i would like to ask you which book should I read first and so on until the last one?
If you’re completely new, read them in order they appear. Otherwise, do your research on which suits your knowledge. I mentioned them in order of complexity.
Hey Kosi! Everything I’ve learned has been online. I’m self-taught but I’ve had lots of help from great teachers (from different courses) and helpful people in the forums and blog posts. I use my own masters degree to get started. If you Google, ‘Daniel Bourke AI masters degree’, it should come up! I studied food science and nutrition in college.
thanks for the video, can you suggest some books for deployment of machine learning model?? i am building a portfolio and i need to learn model deployment. thanks :)
Deployment is still an active area of development, I’m not sure there are many books on it yet. I’d check out something like Kubeflow or TF Serving in the meantime.
They’re listed in order from least technical to most, I’d start with ML for humans to get an idea of the different fields. Then learn to work with data with something like Hands-On Machine Learning with Scikit-Learn and TensorFlow
Hey, I was wandering for buying a GPU. I have some question that what sort of the GPU provider (MSI, ASUS, Gigabyte, EVGA, Zotac) is best suited for the ML model training. I have an MSI RX 570 card. How'll I set up my station for training?
Hey Ramstein! I have no experience with GPUs other than NVIDIA. And I don’t own one, I only rent them on the cloud. I’m not the best to recommend how to setup a local station.
Yes, you definitely can. ML/AI can be done in any programming language with a numerical component. However I’m not sure about the landscape as I’ve only ever done Python.
Knowing some Python is very helpful, all machine learning projects I’ve worked on have been in Python. If you’re going to get into machine learning, you won’t go wrong learning Python.
Hey Daniel. Do you remember me asking you 4 or 5 months ago that I've started a new course on machine learning basics with python? Well, I finished it and got my certificate. I participated in some kaggles competition (ranked at top 35% in the houses prices). But I am stuck now. I don't know where to continue and what should I do next and what to learn to advance in this field. Can you advice me?
As long as you’ve got a medium level laptop, you’ll be fine. Don’t get the smallest, least powerful one but you also don’t need the most expensive. I use a MacBook Pro for all of my work but when I need more compute power I go to the cloud. Anything with i7 should be enough (but look this up, because I don’t know PC specs very well)
with a mid-range budget, you can buy pc with the following parts processor: ryzen 5 3600 motherboard: AsRock B450M PRO4 SSD: WD Blue 3D NAND 250 GB PC SSD HDD: 2TB with 7200 RPM RAM: 16 GB corsair vengeance LPX DDR4(3200MHz) Graphics Card: RTX 2070 super casing: any micro-ATX casing PSU: Corsair CX Series 550 Watt 80 Plus
Hi guy, I have a question. I am about to write a research paper with machine learning , but I am not so good at Math. Is it okay just to use machine learning approaches like SVM, KNN, Random forest..... ? Without good understanding of Math, it’s hard for me to create a model . I’m new to machine learning , guide me if I misunderstand about this? Thank you in advance.
I’ve never written a machine learning paper. But it will depend on what you need. I have a bias towards using approaches which work rather than inventing new ways of doing things. Only when existing approaches don’t work you’d look into inventing a new method.
@@mrdbourke I've read some research papers, they combined this and that. And I have no idea with it. It's a bit hard to find the gap and write one of my own.
Depends what you mean by worth it, certifications themselves aren’t really worth anything, skills are more important. If the freeCodeCamp certifications lead to you gaining valuable skills, is that not worth it?
Hey Daniel, if you don’t mind, can I send an email and ask you for some information about a career in DS and ml and how the job situation in Australia is
Yo Srinivas! You can always email me, however I don't know what the job situation is like in Australia right now, I haven't applied for a job here in 2-3 years. I do know the space is growing though. My advice around jobs in general is in this post: www.mrdbourke.com/how-can-a-beginner-data-scientist-like-me-gain-experience/
Good video up to one point: I think "no, I've read this book and know what machine learning actually is" is the wrong answer to "are robots going to take over the world". Not only is "no", which implies a 100% certainty and is absolute in value, wrong in way too many cases (including this one) and "I've read this book and know.." sounds more like religion than science, but the question itself also didn't ask "with current ML techniques" or mentioned any restriction for that matter - the question was way too broad for a simple "no, I know it" answer. The right answer should be way longer and shouldn't include a clear "no" at all, at best a "if, then not yet or anytime soon". Just because you can't model an AGI with current machine learning algorithms , or any Artificial Intelligence approach for that matter, doesn't mean it won't ever happen. If a "close to human intelligence brain" is nothing but the sum of it's parts, then it will be artificially created one day, at which point robots would indeed be capable of taking over - unless the AGI was designed properly. Which is hard as we don't even know how to define that "properly" yet. So Instead of saying "no" I would've rather returned a question and asked about the time horizon of the question. I know it was supposed to be an example (and a joke about the apparent fear about AI I guess), but I think it wasn't a good one.
Hands on Machine Learning is outdated as hell. Codes for Tensorflow will not work, a new version of this book is about to be released for Tensorflow 2.0 which will also include a few bits of Keras and it has Francois Cholette as a co-author.
Did I miss any? What are your favourite data science and machine learning books?
I don’t read books, may be I should
Francis Chollet's book. I am biased cause I have a love affair with Keras.
Big fan of Chollet as well. Good recommendation!
I thought "Hands-on Machine Learning with sci-kit learn, Keras and Tensorflow " is a good one. It gives us intuition about how things work in a practical way.
Introduction to / Elements of Statistical Learning
Bro , plz make a machine learning course. You read so many books and worked in industdy.
Max Kelsen, machine learning engineer - www.mrdbourke.com/12-things-i-learned-during-my-first-year-as-a-machine-learning-engineer/
Daniel Bourke thanks for the article!
Nice video. Here are some other books that are worth reading/studying.
1) Elements of statistical learning (if you have an advanced math background) or Introduction to Statistical Learning by Tibishirani et al.
2) Machine learning: A probabilistic perspective by Kevin Murphy [I am not a die hard fan of this book but it works well for most cases]
3) The book of why-Judea Pearl (An amazing "semi" technical book that talks about causal inference; a critical piece that's missing in machine learning and deep learning)
Grokking Deep Learning - best book! does not use popular frameworks and libraries this is a wonderful book. if you know the same then tell me. Andrew Trask great teacher
I agree!
i dont often comment but your work is really good n must appreciate!!! Thank you for helping fellow like me :)
Thank you Amit!
I would like to recommend "Pattern recognition and machine learning" by Christopher Bishop and "Understanding Machine learning" by Shai Shalev both for the Mathematical issues involved in research algorithms and more
you are right , they are books that are also recommended by Yoshua bengio
I have "Python for Data Analysis" and "Hands-on Machine Learning with Scikit-Learn and TensorFlow". Other book I recommend also "Python Data Science Hanbook" the author is Jake VanderPlas.
Great recommendations!
Cheers to Daniel, Keep leading.... and finding the best resources for you (and us).
Learning happens different for everyone but you help show how to gut it out....
and get substantial amount learned in a short period on you path to success.
What draws me to your channel is your Honesty!! You never exaggerate the things. Nice video AS ALWAYS
Wow, what a great guide! The Hundred Page Machine Learning Book is indeed extremely good, especially for intuitions. As a CS undergrad, I was trying to use The Elements of Statistical Modeling (recommended by my professor), which is insanely hard to understand xD Anyway, thanks Daniel! You definitely deserve a lot more subscribers:)
So glad you enjoyed Tianyao. I agree, I love the 100 page ML book. There’s also going to be another 100 page ML book by the same author on ML engineering (coming soon). When it comes out, I’ll read it and post a review!
Daniel Bourke Wow, that's nicee! 👍
I saw this channel at 5k subs and in a month it has crossed 12K, so cool!!!
Machine learning for humans is really good
I'd highly recommend this ...
I agree!
Greatly appreciate you sharing your reading list, so to speak...
Thank you so much for that
Nice list! I have DL and the 100 page book from this list. Love those two specifically for the reason they dont have code. I have a few coding books but the risk or problem there is the code will change as you correctly pointed out. The next level after DL are some phd level math books 50% written with greek symbols lol
guys wait for the 2nd edition of hands on ML, its right around the corner
Yes, I am just going through 2nd edition in Safari Online. Seems to be VERY good book and now updated to latest versions.
You’re right! Good share
Currently reading Elements of Statistical Learning to get the math foundation. Truly recommend
Time to buy Physical copy of Deep learning(Ian Goodfellow) Book, since I was reading it online for the most time. Amazing Video Daniel with great content as Always Brother.
Thank you Harsath! Enjoy the book! There’s something about reading a hard copy that online hasn’t replaced yet.
Excellent video! Thanks so much for the clear and concise reviews as well as the links. Very helpful!
Thank you Raoul! Glad you enjoyed.
thanks a lot for sharing those great books
Thanks a lot for reviewing these books.
Daniel you are awesome and very generous in sharing the knowledge...the love the way you explain the things...great..
Very nice video, few statements are well said. Turns out that scikit tensorflow O'Reilly book is very highly rated amongst the community
I can really recommend Trask's book. Will continue reading it while travelling :D
My university used Artificial Intelligence: A Modern Approach as textbook, so i believe that this book is good enough
Great text book!
no wonder why his voice sounds familiar. I am doing his course on Udemy and its worth it. So good explanation.
Haha small world!
I Want to start machine with all vigir but I didn't knowwhich books to use..
Thank you soo much for the insights
9:20 "You should be reading at all times a book that is slightly too hard for you to read." 😊 Well said.
I like discovering books on my own but I also like hearing about the books other people find useful. Thank you for sharing your recommendations. I've only read two of these books so far.
Have you read "Mastering Feature Engineering" by Alice Zheng? Do you have any recommendations on books about feature engineering?
Ooo I haven’t read that one! Most of my feature engineering knowledge has been through practices from the fast.ai courses, I can’t recall reading much on it.
@@mrdbourke Thank you, Daniel. I'll try the fast.ai courses. I've heard only good things about them.
Reading is learning. You are correct about that.
Hands on machine learning is my fav
Great list. Thanks Daniel !!
Hey Daniel, awesome content as always. I'm stuck after finishing the Deep learning specialization course. What should I do next? Do you have any project or course recommendations?
Hey Hari! Thank you my friend.
I’d take a moment to think about what your goals are.
Are you trying to get a job? What kind of role is it?
What are the best next steps you could take to that?
Potentially you could start reaching out to people in the field or who work at the companies you’d like to work for and ask them what’s their recommendations.
Otherwise, finishing a course is a great time to put together what you’ve learned in a project of your own. This will help to consolidate your knowledge. You can also share your work through GitHub and a blog post which you can show future employers as examples of work you’ve done.
Of course people are different, and it's a matter of personal taste, but here's my unsurpassed Top 3 (in order of LEARNING, not the quality):
1. "Mathematics for Machine Learning" by Marc Peter Diesenroth, A. Aldo Faisal and Cheng Soon Ong, Cambridge University Press, 2019.
This has ALL the math you will need (except for the deep learning algorithms alas), in the best possible form, and NOTHING you won't need. You will really master PCA and SVM after defeating this book. Every chapter has a lot of exercises, and, believe me, even if you consider the chapter totally clear, you aren't going to do these exercises easily!
2. "Fundamentals of Machine Learning for Predictive Data Analysis" by John D. Kelleher, Brian Mac Namee and Aoife D'Arcy, The MIT Press 2015.
This book not just explains the basic ML methods to you, it really shows the right METODOLOGY, which is extremely important. It represents everything you need to know about the basic non-deep methods (regression, classification and clustering), including some theory and the detailed explanations. My DS course was too hasty, and I didn't even realize that I don't have to encode categorical variables for Decision Trees and Random Forest until I read this book!
3. "Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow" by Aurelien Geron. Grab only the 2nd edition, it describes Tensorflow 2.0!
Awesome book. Simply awesome. We have used it in our course for Deep Learning, and it describes them all: ANNs, CNNs, RNNs, even encoder-decoder and many others. All with good and working examples.
Of course, "Deep Learning" by Ian Goodfellow and others is a great book, but I feel it's too academic. I guess nearly half of the material you will never need, but who knows? I will take it after I will know the Top 3 extremely well. "One-hundred page book" is a great reference once you already know all the details, but not earlier. And while I am very grateful for Wes McKinley for Pandas, I still prefer Theodore Petrou's "Pandas Cookbook", since it has everything I need, well-described and with good examples.
I should probably add my #4: "Mastering Machine Learning Algorithms" by Guiseppe Bonaccorso (2nd edition).
A great book, with tons of practical demonstrations and examples. The second edition is fresh (published in 2020) and uses Tensorfow 2.0.
This book "patches the holes" in my learning plan, having a lot of Deep Learning math.
I expected Introduction to Statistical Learning but still a great list!
I haven’t even heard of it! Thank you for the share.
@@mrdbourke
I am so sad that i didn't find your channel when i was starting out, people who follow you are lucky.
P. S.
I highly recommend the book. Especially for people with no Calculus background. It still talks about the Math behind machine learning, but calculus is mininal.
(i. e. When they introduced Linear Regression, they solved for the close form of the function of the parameters, instead of Gradient Descent. Although that still requires calculus, they didn't actually compute the gradient.)
Ninjaing this comment but I would also recommend elements of statistical learning as a follow-up to introduction to statistical learning.
@@ralphie123 Agreed! Tho ESL is slightly harder, as it shows PDF's of Continuous Distributions, and hence it uses integrals to get the probability. So might turn people off.
Thanks broo, Great work!
No worries! Enjoy.
Great list! Thank you for sharing. I would like to recommend "Python Machine Learning - Second Edition" by Sebastian Raschka and Vahid Mirjalili. This books really achieves a nice balance between practical implementation of ML models using SKLearn and the gory algorithmic/mathematical details.
Awesome and thanks for the video
Thanks for the inspiration
Always Ken!
Please make a updated vid on same topic , thanks
Thank you Daniel !!
You’re welcome Ravindra!
Excellent review
Thanks for the links!
You’re welcome Nathan!
Deep learning Yoshua Bengio, Aaaron Courville and Ian Goodfellow is a book for those who master all concepts of deep learning and have strong mathematical background (at least 2nd year of college math class) , and i don't recommend it if you want to start deep learning because it is for expert and it will threaten you 😄
I think that An Introduction To Statistical Learning is a great piece. Great for starters.
That's a great book but it wasn't a "starter" book for me. 😁 It showed right away that I had huge gaps in basic statistics skills, so I had to go back to an Introduction to Statistics course.
I agree with your recommendation though. It's a must read for any data scientist or machine learning engineer.
thanks
great stuff man thank you
Glad you enjoyed it Daniel! PS epic name
Great content, thanks Daniel.
Thank you Cauaveiga!
Grokking Machine Learning by Luis G. Serrano
How do you feel about using a kindle to read some of these books considering they can be graphic and color and text intensive?
Knowledge is power
Currency of the 21st century.
It's not about what ml is now, it's what they can become. DL bring the most likely culprit in my mind. I know dl is ml, but dl seems like the most likely culprit
Am a beginner in this field and your video is so helpful for me now so i would like to ask you which book should I read first and so on until the last one?
If you’re completely new, read them in order they appear. Otherwise, do your research on which suits your knowledge. I mentioned them in order of complexity.
Great recommendation.Would like to also get some tips on developing GRIT to Get through ML Journey
Good idea! I could make a video on what helps me
Great video again
Thank you Ankit!
Daniel awesome video again
Good one buoy 👍
Thank you Sahib!
Thanks for the recommendation on Trask's grokking DL book, I'm having fun with it (most of the others I already have :) )
No worries Lisa! Enjoy!
Hey Daniel, how did you get started with coding/maachine learning? college or self taught? Your videos are awesome!
Hey Kosi! Everything I’ve learned has been online. I’m self-taught but I’ve had lots of help from great teachers (from different courses) and helpful people in the forums and blog posts. I use my own masters degree to get started. If you Google, ‘Daniel Bourke AI masters degree’, it should come up! I studied food science and nutrition in college.
Daniel Bourke thanks for your detailed response!
Great video thanks for sharing! Recently got a few of these books myself :)
Thanks Samuel! Enjoy the learning gains!
Remake the video with new list of book?
thank you for good books, any tutorial or demo videos? or maybe favorite websites for data science beginner
Stay tuned! For the time being try out DataCamp, fast.ai or mlcourse.ai
The Towardsdatascience website seems helpful (and I can see that recently even Daniel has contributed with a few articles)
thanks for the video, can you suggest some books for deployment of machine learning model?? i am building a portfolio and i need to learn model deployment. thanks :)
Deployment is still an active area of development, I’m not sure there are many books on it yet. I’d check out something like Kubeflow or TF Serving in the meantime.
Hello Daniel. If you had to choose between these books or udacity and coursera courses which would you choose?
I’d choose both with my current resources. If money was limited, I’d choose books, read, implement and practice.
Which one is the perfect book for someone who has just started learning ML. Which clears the basic! Thx
They’re listed in order from least technical to most, I’d start with ML for humans to get an idea of the different fields. Then learn to work with data with something like Hands-On Machine Learning with Scikit-Learn and TensorFlow
@@mrdbourke Thank you so much! Surely imma do that way
Hi Daniel, can you please make a video that tell us about your desk? I see that the brand is varidesk.com, but I don't know which model. Thanks
Hi, I do love your books. I am totally beginner and now working on medical data ("big" data). Could you introduce me some books that fit to me?
You might want to look into Deep Medicine, not technical but does give good insights on the state of AI in the medical field
Hi, can you recommend a deep learning book for beginners?
All the books I recommend are here: www.mrdbourke.com/ml-resources/
How much time is needed to learn machine learning or deep learning from scratch?
@veseliy hakker is right, that’s a good amount of time to become a practitioner. But don’t be in a rush. Building valuable skills takes time.
Introduction to machine learning with python? Is it good book?
Good, love from Ukraine
Thank you! Love from Australia.
Wow!, you also got the sleep tracking ring with Siraj;
great video, I think I will get the Python for Data Analysis.
Sleep is one of the key pillars of health! Thank you for the kind words. Enjoy the book!
Hey, I was wandering for buying a GPU. I have some question that what sort of the GPU provider (MSI, ASUS, Gigabyte, EVGA, Zotac) is best suited for the ML model training. I have an MSI RX 570 card. How'll I set up my station for training?
Hey Ramstein! I have no experience with GPUs other than NVIDIA. And I don’t own one, I only rent them on the cloud. I’m not the best to recommend how to setup a local station.
Quick question: Is it possible to go deep into ml/ai without python? perhaps java and c++ are suitable too? Thanks for the vid regards from Greece
Yes, you definitely can. ML/AI can be done in any programming language with a numerical component. However I’m not sure about the landscape as I’ve only ever done Python.
Do you need to know "general" Python before going into machine learning?
Knowing some Python is very helpful, all machine learning projects I’ve worked on have been in Python. If you’re going to get into machine learning, you won’t go wrong learning Python.
Those who will study ML at university will likely also use Machine Learning - Mitchell =)
Hey Daniel. Do you remember me asking you 4 or 5 months ago that I've started a new course on machine learning basics with python? Well, I finished it and got my certificate. I participated in some kaggles competition (ranked at top 35% in the houses prices). But I am stuck now. I don't know where to continue and what should I do next and what to learn to advance in this field. Can you advice me?
what about you now ?
Hey Daniel,
Do you have the second edition or first edition for Python for Data Analysis?
Thanks
I have the 2nd edition!
Recommend some books regarding math for ml, data science
You can get plenty of math and data science out of these. But in the meantime, check out mml-book.com for machine learning math.
I have just finished python machine learning third edition. What should I read next.
Hands on ML 2
Excuse me bro would u like to tell me about the pc configuartion for ml or ai please i want to build a pc for ml ,ai focusing
As long as you’ve got a medium level laptop, you’ll be fine. Don’t get the smallest, least powerful one but you also don’t need the most expensive. I use a MacBook Pro for all of my work but when I need more compute power I go to the cloud. Anything with i7 should be enough (but look this up, because I don’t know PC specs very well)
with a mid-range budget, you can buy pc with the following parts
processor: ryzen 5 3600
motherboard: AsRock B450M PRO4
SSD: WD Blue 3D NAND 250 GB PC SSD
HDD: 2TB with 7200 RPM
RAM: 16 GB corsair vengeance LPX DDR4(3200MHz)
Graphics Card: RTX 2070 super
casing: any micro-ATX casing
PSU: Corsair CX Series 550 Watt 80 Plus
Its 2023....Is it worth reading these books?
Please give me some suggestions.....
I'm a complete beginner and don't know how\where to start.....😔
Yes, these books are still valid for 2023, start wherever you’re most curious! You’ve got this!
Any recommended book on ML/DL with FPGA implementation?
I’m not sure what FPGA is!
Is there any book that's akin to The Hundred-Page Machine Learning Book, but more on the topic of Data Science?
Not that I’ve read or can think of off the top of my head. Hands-On Machine Learning introduces a lot of the data concepts you’ll need.
What's your Github? We can code review your code.
My handle is @mrdbourke everywhere. I appreciate all the advice/feedback.
Is this book for beginners?
Sir, How can i start machine learning??? What are the things is necessary to start step by step..???? Thanks in advance
You can start by reading one of the books in this video!
@@mrdbourke thank you sir
Hi guy, I have a question. I am about to write a research paper with machine learning , but I am not so good at Math. Is it okay just to use machine learning approaches like SVM, KNN, Random forest..... ?
Without good understanding of Math, it’s hard for me to create a model .
I’m new to machine learning , guide me if I misunderstand about this? Thank you in advance.
I’ve never written a machine learning paper. But it will depend on what you need. I have a bias towards using approaches which work rather than inventing new ways of doing things. Only when existing approaches don’t work you’d look into inventing a new method.
@@mrdbourke I've read some research papers, they combined this and that. And I have no idea with it. It's a bit hard to find the gap and write one of my own.
are freecodecamp certifications worth it, for the new python courses that are out there?
Depends what you mean by worth it, certifications themselves aren’t really worth anything, skills are more important. If the freeCodeCamp certifications lead to you gaining valuable skills, is that not worth it?
@@mrdbourke yes, they definitely did help, skill-wise. thank you 😊
Nicep
Your books are in new condition, did you read it?
Yes, I take care of them. The Deep Learning Book is brand new though
Ray Gillette doppelganger! XD
Hahaha had to Google Ray Gilette and you’re right!
@@mrdbourke I´m glad you like it! Funny isn't?
@@mrdbourke Thank for the video, btw
Hey Daniel, if you don’t mind, can I send an email and ask you for some information about a career in DS and ml and how the job situation in Australia is
Yo Srinivas! You can always email me, however I don't know what the job situation is like in Australia right now, I haven't applied for a job here in 2-3 years. I do know the space is growing though. My advice around jobs in general is in this post: www.mrdbourke.com/how-can-a-beginner-data-scientist-like-me-gain-experience/
None of them are available in India.
Good video up to one point:
I think "no, I've read this book and know what machine learning actually is" is the wrong answer to "are robots going to take over the world".
Not only is "no", which implies a 100% certainty and is absolute in value, wrong in way too many cases (including this one) and "I've read this book and know.." sounds more like religion than science, but the question itself also didn't ask "with current ML techniques" or mentioned any restriction for that matter - the question was way too broad for a simple "no, I know it" answer.
The right answer should be way longer and shouldn't include a clear "no" at all, at best a "if, then not yet or anytime soon". Just because you can't model an AGI with current machine learning algorithms , or any Artificial Intelligence approach for that matter, doesn't mean it won't ever happen. If a "close to human intelligence brain" is nothing but the sum of it's parts, then it will be artificially created one day, at which point robots would indeed be capable of taking over - unless the AGI was designed properly. Which is hard as we don't even know how to define that "properly" yet.
So Instead of saying "no" I would've rather returned a question and asked about the time horizon of the question.
I know it was supposed to be an example (and a joke about the apparent fear about AI I guess), but I think it wasn't a good one.
Thank you for the feedback Dominik.
I have been subscripted your channel. thanks for all the books your advice.
Hands on Machine Learning is outdated as hell. Codes for Tensorflow will not work, a new version of this book is about to be released for Tensorflow 2.0 which will also include a few bits of Keras and it has Francois Cholette as a co-author.
Great share!
7:13 HAHAHAHAHHA 🤣🤣🤣🤣🤣🤣🤣
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The Element of statistical learning gang ?
Hello sir .. iam applying phd can I have your email please ? .. I want asking you about my topic thesis
The way you speak sometimes feel like Billy from strengerthings , or are u Australian..?
I’m Australian!
please can you put your email here