Lecture 1 | Machine Learning (Stanford)

[@SILOETTE100page]

I just wanted to say that you guys have no idea how grateful we are for you guys, Stanford, for putting these lectures up. Thank you guys for sharing.

[@Kakerate2]

u rite

[@samsonsu1541]

You're grateful and I on the other hand have no idea what he's saying.

[@samsonsu1541]

Also, thought this was an Andrew YANG video lol

[@TheBala7123]

@@samsonsu1541me neither .. but still we are grateful :)

[@anandp7694]

@@Kakerate2 km nnbnm mmmmmxih

[@PannaKatarakta]

Having no course overview is annoying as hell, so I copied all descriptions. Hope that helps! 1 an overview of the course in this introductory meeting. 2 linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. 3 locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning. 4 Newton's method, exponential families, and generalized linear models and how they relate to machine learning. 5 generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning. 6 naive Bayes, neural networks, and support vector machine. 7 optimal margin classifiers, KKT conditions, and SUM duals. 8 support vector machines, including soft margin optimization and kernels. 9 learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities. 10 learning theory by discussing VC dimension and model selection. 11 Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms. 12 unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization. 13 expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression. 14 factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). 15 principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning. 16 reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration. 17 reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. 18 state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs. 19 debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning. 20 POMDPs, policy search, and Pegasus in the context of reinforcement learning.

[@sienna367]

thank u sooo much!!!!

[@83vbond]

Thank you

[@jonaqpetla_]

If this was Reddit, I'd give you gold. You, sir, are a hero.

[@vinodreddymedapati5935]

Thank you so much for your efforts.....

[@olgagorun3700]

Thank you!

[@aamirafzal3992]

This is the best example of Knowledge Sharing. And hats off to Standford University for putting up such useful lectures here. Thankyou loads

[@onthetop93]

that's the kind of lectures I love... The professor is not there just to teach something but even to tell you how to love it

[@GonzoTehGreat]

*Fast forward to 32 minutes to avoid the course admin...* _It's worth either pinning this comment so people see it before watching or (even better) adding this information in the video description._ Thanks

[@punkson]

Thanks

[@mhdnp1234]

Thanks, I felt to comment the same and found your comment prior to that. :)

[@jacobmackenziewebsdale3120]

TheShreester thanks

[@codingboy42]

thanks here is the link to the start kzbin.info/www/bejne/i6vbip-YgJeWbacm34s

[@ShresthaSujal]

thanks

[@Saitama-nu6jf]

I'm almost a decade late to this class. Goddamnit

[@muramasa7537]

Ikr ???

[@gzsingh1435]

saitama is goku stronger than you?

[@tomascanevaro4292]

It's ok, i'll only give you half a fault. Sit where you want :).

[@saijos9798]

You are better off....I am six months behind you..Late to the bus yet again..

[@toddmoore112]

i am just here to read trolls

[@bitsinmyblood]

skip the first 30 minutes to 31:30

[@egor.okhterov]

Christian Gentry better skip to 33 minutes and save 3 more minutes of your life :)

[@pierrealainsimon5190]

Охтеров Егор Thx guys ;-)

[@youmah25]

Christian Gentry thank you man

[@bitsinmyblood]

Have you guys checked out the latest nvidia videos? Pretty amazing how fast this is moving.

[@DarleisonRodrigues]

Christian Gentry thaaaaaaank you, but i wacthed 29 minutes haha

[@vigilhammer]

Mr. Andrew Ng - The man behind Google`s Brain!! These kids have him as a lecturer?? God damn.... what i wud`nt give to attend 1 lecture given by this man! He`s clearly one of the top 10 minds in AI and Machine learning on the planet living today!

[@mcgil8891]

vigilhammer really? I didn't know

[@eliasdargham]

He also was a Co founder in Baidu, China's Google I guess...

[@cshawn8011]

He´s not human!;)

[@jackburton8352]

Look at his face when he is talking he is absolutely loving it.

[@bariscan9267]

It's truly amazing.. even when I think of the lecturers in my country, I can dedicate myself in days and nights for this course because the approach and goal of the lecturer totally deserves it. Many thanks Stanford.

[@WhoForgot2Flush]

I would just like to point out to anyone who is watching this -- If you're here because you want to learn how to make your own neural network and start machine learning, this is not the place. This course is to prepare students to read and write research papers. ML researchers at places such a Google, Microsoft, Facebook etc. will be expected to write research papers at the academic level of Stanford or MIT. This course has extremely high Calculus, Discrete Mathematics and Linear Algebra prerequisites. If you're not interested in writing research papers and paving the way of machine learning research, this course is not for you. Check out Andrew's course on Coursera, it's very trimmed and the bare minimum, if you're just trying to get into ML start there!

[@maldoengineer]

00:00 Introduction 31:39 Machine Learning Definition 36:18 Supervised Learning

[@kparag01]

My age is 47 and I m not late . All young guys hang on , I m coming

[@RohitSingh-yo2yl]

What will you do with AI at this age?

[@Yahya_the_goat]

I’m 19 all older guys hang on, I’m coming

[@DcBooper]

Ssss#sss draw t8euufsysy9ày88y8s8y8sy8stss88888f8fsf8fss88ts88rssrsssssssyy8y88tsssdssttttttttt888888888888 try tttt[[[=tye dye=e|you 6seseeseeetddrrt8[do r9[you know [[tragus edr=ee6rrrrrrr[ztdtsst[you see everyone[eeeeeeeeeeeeeeeeeetett er r r eeeet e trade so far far darts stays ẞtd7a8da y777aay7aa77afffaa to fy] SA

[@notagain3732]

Keep learning, everyday is a chance to gain knowledge

[@thomasbates9189]

Way to go, Parag!

[@abdouazizdiop8279]

Im here in 2020 , and im going to watch all theses videos , i think Andrew is one of the best teacher about ML

[@sauragra]

THE best Machine Learning instructor in the world. Thank you, Stanford.

[@edadan]

Love this teacher! It’s incredibly helpful to first explain why you’re learning a particular thing and what it’s useful for. Excellent!

[@neeraj33negi]

Skip to- 32:30

[@ВоваНеСкажу-е1ч]

NEERAJ NEGI is

[@KN-ey3yf]

Thanks man

[@itech40]

THANK YOU! You saved me almost 30 mins.

[@kaituo1803]

I wish I had taken this course 10 years ago

[@wahidarf6423]

@@sandipandas8272 ط(£٠£^€

[@behrad9712]

I love this men and his class! He makes ML easy & fun, you motivates to pursue it forever because it's science,engineering and money what we need else!?...

[@vishualee]

Thanks to youtube, i can go back in time, and witness these priceless moments

[@gauravbhokare]

In 2008 he says "I am studying it for 15 years " now in 2019 I wonder should I really start this course -_-

[@Интернетобразование-о1и]

i started

[@SAAARC]

everyone starts somewhere

[@cliftonwilson3163]

It is so amazing that this platform exsist and is obtainable to anyone who wants to learn and pay it forward as we learn thanks to Pi & community!

[@notagain3732]

Im so happy i can watch this here and i dont have to travel to another country , yet i would love to be in that classroom and interact with staff as well with students later on getting to know people so remote learning has its pros and cons, maybe there is a discord server or a subreddit with machine learning enthusiats yet still meeting someone in real life face to face with similar interests is still rare yet preferable...then there is zoom also a way to make connections i guess . Many methods for learning out there

[@drancisdrake]

I have searched for "Where to start learning about machine learning?" A whole bunch of people seem to think that Andrew Ng's course here is the place to start. Another good tip I got was to continue with reading some book on machine learning, picking a problem there that seemed fun, and create a machine learning algorithm.

[@juleswombat5309]

Yep its pretty heavy stuff unless you are really up to speed with linear algebra and probabilistic theory. So useful to mix it up alongside simple introductory ML books, practice with running algorithms against sample sets in R, Python or Weka. Slowly it starts to sink in?

[@borgestheborg]

Depends on if you want to learn ML at a practical level or at an academic level. If you plan on getting good enough at ML to get a job in the field then you'll want to take the practical route, but if you want to learn ML just for the sake of understanding it or doing research then you'll want to go the academic route. Andrew's lectures are highly academic and focus a lot on the mathematical and statistical aspects of ML so unless you have a solid foundation of statistics and linear algebra basics you'll struggle to keep up. Additionally, if you follow his lectures on Coursera then you'll want to learn MATLAB as well as that's the programming language used there. MATLAB is great for prototyping when coming up with your own ML scenarios or algorithms but it's not all that practical for field work (i.e developing an ML application, gathering and managing massive amounts of data, etc.). But the good thing about Andrew's lectures is that he's incredibly well versed in the subject and could explain its concepts much better than almost anyone else I've found online. If you get through one of his courses you'll have a thorough understanding of how most ML algorithms work and that'll form a solid foundation for practical application. However, if you want to study ML for the sake of deploying applications or getting a job then there are other available courses online which focus heavily on getting you to apply ML to real world scenarios as soon as possible. One such series is Google's own newly started Intro to Machine Learning (goo.gl/lIuJb2). They use Python to get you up and running coding your first ML program asap. However, the series is not complete and they are still adding to it at the time of writing. Another great source, in my opinion, is Sentdex's tutorials (goo.gl/RteOHz). He has a large number of tutorial series focused on implementing ML using Python which are aimed at solving practical, everyday problems like stock market predictions. These tutorials mainly focus on using already-available Python libraries (such as Sklearn and MatPlotLib) to implement ML algorithms instead of trying to build them from scratch. Mastering Sklearn and similar libraries will enable you to land an ML job much more quickly, but if you don't follow through with the academic side then you'll be left implementing a bunch of pre-written algorithms that you don't quite understand. "Another good tip I got was to continue with reading some book on machine learning" Good tip indeed, you may want to find an ML book which uses the language of your choice, though if you are going to enter the fields of ML and data science Python and R are outright expected of you. One suggestion I can make is _Machine Learning for Hackers by Drew Conway and John Myles White_ which uses R, if you want Python there's _Programming Collective Intelligence by Toby Segaran_ , It'll help you get started but I've heard that the second book has become outdated because it uses outdated API's for pulling data from various sites. "picking a problem there that seemed fun" Almost any problem becomes fun with ML but the real challenge, which is constant across all problems, is the pains of acquiring data, labelling data, formatting data, normalizing data, etc so that your algorithm can properly parse it. "create a machine learning algorithm" Wouldn't recommend you start implementing your own ML algorithms at this early stage, instead get to know the existing ML techniques and understand how they work, take them apart and see what each part, each variable does. Pretty much all problems you'll encounter in the real world will fall into an already defined category such as Clustering, Labelling etc, each having its own set of ML algorithms that best suite the occasion. (Here is a nifty little chart which can help you pick an appropriate algorithm: goo.gl/yMKQt6). These algorithms are highly optimized and quick, much better than anything we could implement manually. However, if you do encounter a problem where the existing algorithms fail to deliver a satisfactory result then you can look into forming your own ML algorithms, but this is more or less heavily research oriented. Hope this long post was of some help to you and good luck :)

[@arpit23021991]

this is the best comment I have seen till now.

[@letme4u]

Thanks. Much appreciated.

[@nawabsonu]

Thanks for heads up. No doubt Andrew's course stands out of all ML video lectures but the perspective of learning you have shown is what matters. I am looking forward to Google and Sendex lectures. Thanks.

[@dv9124]

Imaging people dropping this vastly amazing pool of knowledge just because the lecturer says 'um' from times to times. Imaging these people being the talent recruiter, ggwp.

[@amraja7]

Andrew NG is the father of modern Machine Learning 🤝

[@GlennMascarenhas]

Anyone exploring these lectures during the 2020 COVID-19 near-worldwide lockdown?

[@deadclassic9241]

Yes

[@andrevargasaguilar2723]

if it wasnt worldwide 3 months ago, it is worldwide now.....

[@ivanllopis5882]

Thank you very much Andrew, thank you very much Stanford for uploading these wonderful lectures

[@calendar]

I love how they are doing this in the tech world. Stanford seems to be leading this.

[@abramswee]

thanks, andrew. due to being asperger's disorder, i can never get the proper grades to attend a proper university in my home land. this avenue of online education did open doors of ideas to me.

[@top5s733]

Turn Subtitles On and Pause at 36:04 . That will make your Day

[@offchan]

hahahahhaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa

[@TheMorning_Son]

? dont get it

[@offchan]

The subtitle system changed. Before it was something very funny. I forget what it was now.

[@DharokWretched]

it's still funny now, "the checklist program plays checkers"

[@engineerhealthyself]

what's funny is that the subtitle system is also some implementation of machine learning

[@ansrhl9448]

Thank you Stanford for putting these lectures up on youtube. I feel so fucking lucky to watch these. #KnowledgeISPower

[@YangPaulYang_YaoNien]

One of my favorite class of ML... Great lecture content and presentation!!!

[@aakarshmalhotra343]

Skip to 32:38 : the place where the teaching actually begins (after course logistics)

[@methadonmanfred2787]

thanks

[@larry3317]

I'm in high school and very interested in this, thank you so much! My dream school is MIT

[@rustin3255]

Probably not the wisest thing to say in a Stanford video's comment section lol

[@abebuenodemesquita8111]

@@rustin3255 i mean most cs people's dream schoolis mit its prob not that bad. if they had said harvard or UChicago it would be different

[@varunnayyar3138]

Not many people know that when Andrew NG started teaching he got negative reviews from his students. With time he improved his pedagogy and now we know what his level his.

[@prasannarajaram]

Watching this after 10 years after this video has been uploaded. The concept is still relevant and easily understandbale

[@nawabsonu]

Thanks to Mr. Andrew and Stanford for making this incredible awesome tutorials available on the internet.

[@egogo5675]

Is there anyone here . Who would know this course could be the most demand course all arround the world. ANDREW NG the best teacher :))))))

[@khaldoon2300]

Thanks a lot for offering the course on KZbin. I really really appreciate it. It seems very useful and it will give me an opportunity learning something valuable for free!!

[@daweifunstuff]

real contents begin at 33'

[@johnsonkoshy777]

Thank you!

[@armaanmohammed8184]

Thanks a lot

[@mohit_talniya]

u saved half hr of my lyf. Thanks

[@mcgil8891]

Dawei LIU thank you so much

[@tr0p]

According to the course website there is no required textbook for the course, but supplementary texts are recommended: Christopher Bishop, Pattern Recognition and Machine Learning. Springer, 2006. Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001. Tom Mitchell, Machine Learning. McGraw-Hill, 1997. Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction. MIT Press, 1998

[@825_mohit8]

People from this will not know that how much famous he has become in the world of ai and deep learning

[@thomasbates9189]

Thank you for posting this course!

[@zhehou2360]

is the subtitles created using machine-learning technology? ;)

[@annaxavier1775]

+Zhe Hou Are they ?

[@zhehou2360]

+ANNA KAVALAM I don't know. But looks like. And I believe ML can do this kind of work very well.

[@Maxander2001]

+Zhe Hou There are no subtitles in the video, so ... surely.

[@magik603]

+Maxander2001 there are

[@akshatb]

Yes they are.

[@ABZein]

Lecture begins at 32:40

[@1230986666]

That prof looks like such a nice guy

[@mcgil8891]

Nicolas Bouliane ikr

[@long8398]

is it me or does he looks like andrew yang?

[@sitongye3601]

@@long8398 Andrew Ng

[@ai.simplified..]

If he is not good guy, maybe he was into his start up mot teaching others. He loves his job,it is obvious he is enjoying his current job

[@annaz1652]

in the cocktail party problem around the 1 hr mark, do u need to as many microphones as people's voices that you want to differentiate?

[@notagain3732]

I love this on so many levels

[@vaibhavshukla2043]

Thank you sir... One of the best classes I ever attended

[@christianlira1259]

A great introductory video covering multiple ML facets and segments. Thank you.

[@chaeunlee7398]

The books recommended on the CS229 site are , I think, a little bit old version. So, I suppose you refer the following books. They will be helpful. Fundamentals and review for the lectures 1. Pattern classification and machine learning Covers recent trends and fundamentals 2. Deep Learning, MIT press Mathematically rigorous 3. Understanding machine learning theory algorithms, Cambridge Univ. Press I, also, cannot see these books perfectly, but I convince that they will be good references. Thank you, Prof Andrew Ng.

[@brandomiranda6703]

Machine learning is the most exciting field in all human endeavors. I got into ML before transformers were cool! ;) Starting around this time in 2013 before the ImageNet paper ;)

[@zhenzheng3369]

Four types of machine learning topics: 1. Supervised learning a. regression b. classfication 2. learning theory 3. Unsupervised learning a. no label b. cluster c. application: 3D model from a single 2D image 4. reinforcement learning a. like training dogs

[@yasamanderiszadeh902]

What a wonderful professor. Thank you for posting

[@waedjradi]

Nice. Ng has information, for sure.

[@goketesh]

Maestro!! Mis respetos! Que honor tener una clase del profesor Andrew!

[@aa4mad]

1 an overview of the course in this introductory meeting. 2 linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. 3 locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning. 4 Newton's method, exponential families, and generalized linear models and how they relate to machine learning. 5 generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning. 6 naive Bayes, neural networks, and support vector machine. 7 optimal margin classifiers, KKT conditions, and SUM duals. 8 support vector machines, including soft margin optimization and kernels. 9 learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities. 10 learning theory by discussing VC dimension and model selection. 11 Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms. 12 unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization. 13 expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression. 14 factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). 15 principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning. 16 reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration. 17 reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. 18 state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs. 19 debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning. 20 POMDPs, policy search, and Pegasus in the context of reinforcement learning.

[@istiakahmed4621]

Thank you, sir I wanted to study Stanford University 🇺🇲🇺🇲🇺🇲

[@rodrigopinto27]

The teacher has a nice voice

[@jeet027]

+Rodrigo Pinto I don't think so ....

[@allthebestfails898]

Just wanted to point out how he says that matlab is better than R. Its 2017 now, and R is arguably better than matlab on so many levels.... Of course no one can see the future, not even the smartest person. These lectures are awesome, and we live in a time where we can find all the human knowledge, even thousands of years old, just in few seconds anywhere on earth. how amazing is that!

[@vilasjagtap6165]

Great initiative. Glad to learn ML basics online. Wonderful experience. Thanks.

[@SambitTripathy]

Enjoyed the way contents were presented and I have not repeated the video at any point. Great.

[@jonathanl7204]

This teacher is good

[@Nimyzz]

Totally awesome, explained the different concepts very well with lots of examples! Looking forward to watching the rest of these lectures!

[@EasonZhang-x9j]

I'm almost a decade and one year late to this class. Goddamnit

[@sandysandeep7227]

Is this course same to the one which is available on coursera? In coursera, he teaches in front of a pc in a room.

[@narayananshanker6066]

Nice learning experience for me. Thanks for putting it in my mail

[@SBARTSTV]

I'm going to watch all these videos. Nice job.

[@erlinharyani638]

Pada k Kembalikan ke yuotub

[@kumaramit228]

Where to get the practice problems and lecture notes?? Anyone have any idea then please post the link.thanku

[@PranavReddytv]

+Amit Kumar cs229.stanford.edu/materials.html

[@abhaytiwari1615]

Pranav Reddy i m grateful😊😊

[@ashubabbar3322]

Pranav Reddy do u HV another link,the link upon opening reads the content has been removed😢

[@dominggus]

www.coursera.org/learn/machine-learning/

[@pramilabajoria171]

Thank you so much Stanford for putting up these lectures

[@twahirabasi9765]

Thank you Stanford!, Thank you professor Andrew Ng

[@squeezeme9820]

I listened to the entire lecture and it wasn't a waste to me. Quite the contrary.

[@사랑아화이팅]

THIS truly is learning from a machine

[@mrnettek]

Professor Andrew Ng, great seminar.

[@vrutin123]

Machine learning content starts at 32:55

[@mcgil8891]

Vrutin Tarunchandra thank you so much

[@fahimhassanblog]

This is such an awesome video! Loved it!

[@ThisIsNotMyHandle]

Actual lession starts at: 32:35

[@elevenz367]

Thanks man

[@samuelcheng7150]

At 38:18, the curve that Professor Ng draws should be described as "logarithmic," not "quadratic," right?

[@harveyclark4923]

I believe so yes

[@asimpleenigma]

It could be quadratic, but if it were extrapolated to the right, the computer would guess wrong.

[@i_youtube_]

They teach in since 2008 and we learn it now.

[@someonefromsomewere1]

@Compact3 Not exactly, AI is just the computer following certain instructions based on predefined circumstances, but machine learning is when the machine starts to learn from its mistakes and don't make them a second time. (or something like that )

[@aeryes2806]

I love the part at @38:22 when he makes a quirky joke and no one laughs and he just chuckles merrily to himself ! If I was in that class I would have too many questions to ask !!!

[@ajaydublin]

Brilliant video ! Great work! Looking forward to see all the 20 video! Cheers

[@azamstat]

Thanks a lot Professor Andrew Ng, thanks Stanford.

[@ottoreichert]

Great thanks for Stanford for the GREAT COURSE!!!

[@manish1golu]

thanks to the university............... nd also to the prof.... who give his important time to the student like me...

[@life42theuniverse]

14:00 no the volume was fine... turning it up created static! A bit late.

[@easyTree77]

19:38 - did you use Machine Learning to spot honour code violations? :D

[@tear728]

Awesome they used "The Logical Song" by Supertramp in the demonstration. Get's you thinking about the philosophical implications of machine learning.

[@ojoozerubabelogom8498]

Good and practical presentation for all sorts of students

[@armanrainy]

Thanks Professor Andrew Ng. Thanks Stanford. I can not leave my desk. One Suggestion for Stanford: Subtitle for the videos will be beneficial for nonnative English speakers. Sweden, Halmstad,Embedded and Intelligent Systems Student,

[@prateekbanga3074]

Ok, wow. I'm gonna do this course now . That Infinite dimension concept sounds intriguing.

[@YoSoyAro]

Thank you so much! i exit the full screen to look at something like this at coments, and here it is

[@focker0000]

Hats off to stanford for those who are laughing at the professor's 'ummm', do you know who this guy is?

[@toddmoore112]

No. who is he ?

[@hty96]

@@toddmoore112 may be the most important human being in the field of machine learning and ai

[@talk2thoran]

@@hty96 Do you think he might be able to reprogram himself to speak more clearly?

[@maxajames]

He is Andrew Ng. You can look him up on the internet.

[@danielcahyo288]

@@talk2thoran lmao

[@vishnu_bhatt]

Starting 4th time this time will definitely complete it . :)

[@vikramrajput76]

Definitely this time i am with you.

[@jobandeepsingh1929]

Is this course still relevant in 2020? Before starting this course

[@TommyCarstensen]

I'm sure that any machine learning method will find, that the 38 individuals disliking this video don't have the pre-requisites to follow this course :) This guy is super cool!

[@hasanmomen5738]

@seahawks78 you can see the course schedule on stanford (dot) edu (slash) class (slash) cs229 (slash) schedule (dot) html

[@tajiknomi]

Switch to 33:10 , be4 this, its just Wastage of time...

[@hongren99]

Thank you for saving my time

[@mcgil8891]

Thank you so much

[@me_ganeshkadam]

Thanks Mate! :)

[@RelatedGiraffe]

For those of you who think this is a great class, you can now (since three weeks ago) take it for free at Coursera: www.coursera.org/course/ml But the course material will very likely be available even after the course has ended.