Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018)
ฝัง
- เผยแพร่เมื่อ 4 มิ.ย. 2024
- For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: stanford.io/ai
Listen to the first lecture in Andrew Ng's machine learning course. This course provides a broad introduction to machine learning and statistical pattern recognition. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. Explore recent applications of machine learning and design and develop algorithms for machines.
Andrew Ng is an Adjunct Professor of Computer Science at Stanford University. View more about Andrew on his website: www.andrewng.org/
To follow along with the course schedule and syllabus, visit:
cs229.stanford.edu/syllabus-au...
0:00 Introduction
05:21 Teaching team introductions
06:42 Goals for the course and the state of machine learning across research and industry
10:09 Prerequisites for the course
11:53 Homework, and a note about the Stanford honor code
16:57 Overview of the class project
25:57 Questions
#AndrewNg #machinelearning
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any non CS person watching this in 2024? I have a medical background and now trying to learn this shit. those who are trying too, all the best, cheers
Memememe
cheers man best luck
me and it's so hard for me man!
How r u keeping up with it? I also have a non tech background
mememe!!! same
Lecture starts from 36:20
Thanks
hero
Thank god
many thanks
Thanks
At times I feel so lucky being born in this age of internet, Otherwise it was no way possible for me to learn from this legend of machine learning for free, looking at this career across academia and industry , he is a pioneer . It like learning integral calculus from Newton himself.
you are not alone ...
We shall dub him sir andrew
Lebnitz
It would be like Newton, if it was Yoshua Bengio or Yann LeCun or Geoffrey Hinton
Yeah.. we're so privileged to have these opportunities.
It’s a real honor to be able to listen to this lecture at home.
Hey bro any previous knowledge is required for this course ?
@@ItihaasInsights1 no, he says he'll explain concepts along the way.
@@ItihaasInsights1 prerequisites 10:21
I am thankful for Stanford and Dr. Andrew for making these lectures available on TH-cam. I am from a third world country where I cannot access such quality education. I am so grateful. God bless everyone
@@26d8 bro what are you talking about
where u from?
@@la_rayyy 🤨
Sameeee
We are truly grateful to Stanford University for providing these valuable lectures free of charge, which holds significant importance for those who may not have the means to access this level of education.❤❤
What wonderful feedback, thanks for your comment!
@@stanfordonline it will be wonderful to get the assignments of this course
I just love how humble and friendly this prof is. Big fan
40:14 supervised learning
1:04:50 unsupervised learning
1:11:18 reinforcement learnin
I just love how humble and friendly this prof is.
the way this man teaches is something which doesn't get you tired. you will watch him teach with a beaming smile on your face.
thanks for this great class.
Thank you for uploading these videos even though it's 2023 today. U can always learn something from Andrew Ng, He's like Iron man's dad.
Andrew Ng is a legend. I took his course on Coursera and I wish the course never ended. I have taking a lot of Machine learning courses but none of them compare to Andrew's. He made complex concepts look trivial and established a theoretical background ('Intuition' as he would say).
Is it the same course on coursera ?
@@anand-arnaudpajaniradjane9722 not at all. This course is much more theoretical, the coursera one is focused on practice
@@JComprendsAuxMaths okay thanks a lot for your answer !
bro which should i complete first youtube or coursera
And how exactly did it help with your career? Were you able to apply this knowledge in any useful way?
Thank you for making this world class education from a premium institute like Stanford available to us at free of cost, which otherwise was unthinkable for me to get. Really loved the way Professor Andrew Ng taught this lecture. Looking forward to learning so much from him. Thank you 🙏
5 topics of this class
1) Supervised Learning
2) Machine Learning Strategy
3) Deep Learning
4) Unsupervised Learning
5) Reinforcement Learning
This is amazing.
Machine Learning, AI and Data Science are the fulcrum on which our world turns and will continue to turn.
Thank you Standford and Andrew Ng for this generosity of knowledge to our world.
I am grateful to Stanford University and wish these open courses to be up-to-date and continuous.
05/07/2022 Lecture 1: well-posed learning problem is said to learn from experience E with respect to some task T and some performance measure P, if the performance on T, as measured by P, improves with experience E. See, not that complex!
I am a 12th-grade student and really interested in machine learning. I did some basic math which is required in machine learning and am starting with this course today. I really liked the fact that the language which is used in this course is python. so excited to learn about this new branch. Will try my best to complete it. THANK YOU STANFORD FOR UPLOADING THESE LECTURES.
Grade 12 = senior year?
@@ideagood9253 Yep
Get a solid foundation in math out of the way. You will struggle less. Put effort into math early. You will be able to pick up everything else easier. Learn Calculus 1 Calculus 2, and Multivariable Calculus, Also Statistics and Linear Algebra
@@Atlas92936 yess!!!! For linear algebra, Prof Gilbert Strang lectures are great place to start. And yes, thank you for your advice.
i wanna ask u something . if u can help
Thank you for explaining the concepts, purposes, and practical applications so clearly
Congratulations for the great work you have done for the humanity
Huge respect and love to you sir🙏
This video on machine learning is an absolute gem! It beautifully captures the essence of this transformative field and showcases the incredible power and potential it holds. As someone fascinated by the intersection of data and technology, I found this video to be both informative and inspiring.
The video did an excellent job of explaining the fundamental concepts of machine learning in a clear and concise manner. From supervised and unsupervised learning to neural networks and deep learning, it covered a wide range of topics, making it accessible to both beginners and those with some background in the field.
The real-life examples and applications of machine learning were truly captivating. Seeing how it can be used to recognize patterns, make predictions, and provide valuable insights across various industries like healthcare, finance, and marketing was mind-blowing. It's amazing to witness how machine learning algorithms can analyze vast amounts of data and extract meaningful information to drive innovation and improve decision-making.
The video also touched upon the ethical considerations surrounding machine learning, emphasizing the importance of responsible and unbiased use of these algorithms. It's crucial to be aware of the potential biases that can exist in data and to strive for fairness and transparency in the development and deployment of machine learning models. This reminder of the ethical dimensions added depth and thoughtfulness to the video's content.
I truly appreciate the effort put into creating this video. The visuals, animations, and explanations were top-notch, making complex concepts easier to grasp. It's evident that the creator has a deep understanding of machine learning and a talent for effectively communicating it to a broader audience.
Whether you're a beginner seeking an introduction to machine learning or someone looking to deepen their knowledge, this video is a must-watch. It not only provides a solid foundation but also instills a sense of awe and excitement for the possibilities that machine learning holds. Hats off to the creator for producing such an outstanding and educational piece of content!
This lecture was amazing! Andrew Ng is such a great teacher and explains everything so clearly and intuitively. This video is the next best thing to being in the classroom with him! Thank you for sharing this valuable resource with us!
Yeah hes much better than the MIT guys.
What wonderful feedback, thanks for your comment!
you finished the entire playlist, also could you tell me how much does this course prepare you for getting job, like can you build ensemble models after this course?
lecture starts from 36:30
Every lecture need this type of cmt tnx
Saved me minutes ❤
Thanks
AmaOng teaching ,just blessed to get such content for free.
Thank you for the course. It's very helpful for me ❤
every time I watch it I learn more
This course is great for students who are learning Machine Learning or Deep Learning.
What amazing 75 minutes of my life!
oh my God, Stanford thank you so much..
Interesting and informative. Thank you Andrew.
Just loved the introduction . May I ask if we you-tube watchers can also get the hold of notes which sir Andrew is talking about ? btw thank you making this course and video available to us !!
thanks Andrew and Stanford this very interesting for me.
Thanks Stanford for this video
Very nice lecture
Excelente clase. Saludos desde Perú.
In today's time Andrew Ng is legend by his ML knowledge 🤓
That's amazing. Done watching
This is an amazing opportunity for students like me . Long last stanford University
Thanks for our comment, glad to hear you're enjoying these lectures!
thank prof, it's clear explained
Thanx for the lecture.
I am going to record my self building a product hopefully a life changing project for everyone thanks for this course.
Let's do this 💪
00:06 CS229 Machine Learning provides the tools to become experts in machine learning
06:13 This class aims to make you an expert in machine learning
18:33 Form small groups for class projects and prioritize teamwork.
24:04 Communication with teaching staff and important logistical information
35:47 Machine learning tools can help remake the world and make it a better place.
41:50 Supervised learning involves learning a mapping from inputs to labels using different models.
54:29 Supervised learning using neural networks for self-driving cars.
1:00:34 Debugging the learning algorithm is crucial for building effective learning systems.
1:11:51 Reinforcement learning can be used to train robots and optimize their behavior.
❤thank uuuu
We Students don't have Teachers like this
If we can have
I'm sure
We can make this world more and more and more Better.
Wow, what a lecture!! How can I download these videos? Prof Andrew is phenomenal
Very good lecture, I learned a lot, I hope I did watch this video way sooner.
thank you
are you a beginner from 0? if no, can you recommend me a yt tutorial?
Thank you!
좋은 강의 감사합니다.
Great course
this is so cool, its like im in class.
Easiest way of teaching ❤
It's very helpful for me ❤
Much better than my professor!
When I searched for ML I got this and saw Abdre Ng, I just cliked it instantly. I just now him in face and I knew when I was a 12th grader in 2018 G.C that he is a pretty recognized neural network scientist.
Since he mentioned about the group project I naturally started think about who to work with on my group project but after he asked "How many of you this is your very first class at Stanford? raise your hand" I have realized I do not have any group project at Stanford. Wish I could be in there xd
Very nice learning.
Where can we find the homework and projects for CS 229 ?
Thank you for this amazing course. It would be of great help if you can provide the problem statements as well. As in the links provided before requires making an account on Piazza so can you please provide them in some other format.
Thank you
I'd love to see the analytics for this video. The view count must've grown exponentially within the past 6months to 1 yr
great ending statement about Newsom
Does any one figured if there are recorded session of TA discussions, going in details over the prerequisite concepts..
Amazing. Also, #UnintentionalASMR
I love Stanford!
me too!!
@@hangkaiqian8067 me too from korea
Can someone kindly share the website on which i can get a copy of his book?thanks
As a beginner what do you need before start to watch this playlist ??
Is this lecture series cover all the basic concepts about ML??
Any way to get the problem sets? The links require log in credentials I guess linked to the course itself, and the first problem set seems to be missing at all..
Can anybody tell from where we can find note, they are not available on site which is given?
Are the recordings of the discussion sections available as well? I would like to review the prerequisites for this class!
the prerequisites are at 10:21
The cs229 course website no longer have class notes and other resources, please update it asap. It will be very helpful.
1:05:01
unsupervised learning
Hi. First of all thank you Stanford for uploading these lectures on youtube. I am currently doing Professor Andrew's coursera ML specialisation. Shall I listen to these lectures after I completed that specialisation?
Hi there, thanks for your comment! After you feel confident about the foundational knowledge you've learned in the specialization, you will then likely get the most of out these graduate level course lectures. Happy learning!
Hi, Could you guide me where i can find that course exactly? Is that the beginner course?
Starting the course today
After this lecture going to cover prerequisite so looking for friends reply
Can anyone give me a full list of prerequisites before I start? I am a Cloud guy and don't want to go for managed services. This will be awesome, thanks in advance!
SMOOTH DELIVERY
I completely lost it when I realized the helicopter was flying upside down
How can i get notes for these lectures?
Well I couldn’t get the book he mentioned from the site, any help
are the discussion sessions on youtube?
Thank you for this great lecture. if there are any Stanford students pls help me to get the tutorials.
10 mins could have been enough for that video. There is really nothing to miss here.
Hey question, what math should I exactly understand as a background before I start learning this course?
Hi there, thanks for your question! The prerequisites for this course include linear algebra, basic probability and statistics. You can view more about the course here: online.stanford.edu/courses/cs229-machine-learning
You may also be interested in checking out the first problem set: cs229.stanford.edu/materials/ps0.pdf
Hope this helps! :)
@@stanfordonline Hi, are all the problem sets available for me? If so, were can I find them?
@@stanfordonlinelinear algebra, that is harder than calculus 2 isn’t it?
@@lenderzconstableYes but not by much
Is it the full ml course ???
34:11 - the topic begins
I don't know if this video is being monitored but, is it possible to get the files for the handouts for the course?
Hi Robert thanks for your question! You can check out the course website here: cs229.stanford.edu/ and the recent syllabus with notes and slides: docs.google.com/spreadsheets/d/12ua10iRYLtxTWi05jBSAxEMM_104nTr8S4nC2cmN9BQ/edit?usp=sharing
anyone know if there is recordings for the dicussion sessions that go over probability, statistics and such (being a while and do need some reminder) Thx
did you find it bro?
No I didn't :(@@Quester2023-xp7rb
34:10 the course starts
Nice
Where can I get all the study materials of cs229 ?
The website name where we will find the book systematic engineering principle for machine learning by andrew ng is not clear
can anyone share the site ?
I need a partner to coordinate with practice projects by following this course, will you?
How can i get the book in 1:03:30.
How know the URL of the website
where are the discussion sections recording?
Will he be using tensor flow in his course?
till 34:00 for Stanford Student
From 34:00 for online student
Is there an opportunity for an international refugee student registered at UN who wants to study a bachelor’s degree in CS at Stanford? Are there scholarships or anything like that?
Where are the discussion recordings?
i am a beginner, should i learn from him , or should i go for some basics of ml first.
Hi there, you can learn the basics from Andrew in this 3 course specialization: www.coursera.org/specializations/machine-learning-introduction
was andrew ng's original ML playlist removed?? i cant find it anywhere
Hi Kavya, thanks for watching! Andrew Ng's full ML course can be found here: th-cam.com/play/PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU.html
I’ve been trying to complete this course for a decade 🤣
I give you 2 weeks. Report back in 14 days.
@@PlantProTips challlenge accepted!
Where to find the homework content?