11. Introduction to Machine Learning

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Information 11. Introduction to Machine Learning

Title	:	11. Introduction to Machine Learning
Lasting	:	51.31
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Any india here 🧐brye modi ji ka chachera bhatija lg rha 😂
Comment from : @ashutoshkunal291

I’m thankful that I know English, and I can understand what this man is talking about!
Comment from : @367kkk

Sir, I would like to ask you a question Are you going to teach supervised, reinforcement, and unsupervised learnings ?
Comment from : @MelikBaykul

In my opinion, this video about first week in a term I can't share my machine learning knowledge here He is right, I think, he wants to say that " if you want to learn machine learning from mit, first of all, you should get an acceptance, after that, you have to attend the courses when you study hard, you can pass the exam" brBest,
Comment from : @MelikBaykul

👍
Comment from : @sandhyasahu5785

anyone watching this in 2024?
Comment from : @shreya3244

u guys rock!
Comment from : @Poosboy

29:45 For those curious out there, a chicken is indeed a reptile In fact, they're a dinosaur Even better, all birds are dinosaurs and reptiles
Comment from : @HackionSTx

I wanted to express my gratitude for the detailed and engaging content Thank you for breaking down complex concepts into digestible parts and providing clear examples This lecture has significantly deepened my understanding of machine learning principles and practices
Comment from : @jameswharton5259

Now I feel confident, I can learn Machine Learning Thank you Sir
Comment from : @jonathanmartinez-y6l

Great tips
Comment from : @HajiVarsani

commenting for algo
Comment from : @ADRIFAYIN

what a wonderful lecture
Comment from : @mrncomputer

Think more about binary options because easy money is great🐒
Comment from : @SaberAyoobi

Neuron reception
Comment from : @mohokhachai

I'm glad I take such an insightful lecture It was much easier to understand than the ones I've taken at my university It covered the basic and fundamental concepts of machine learning in a way that beginners can grasp Thank you for uploading
Comment from : @安保亮-l9h

This is the best explanation I've ever seen about this topic
Comment from : @AlexAcostaB

Can some send me link of his others vedios?
Comment from : @BrilliantPkF

excellent explanation
Comment from : @FlipHackingRealEstate

I learned so much from watching this video
Comment from : @mindblowingfactscompilatio6903

Excellent professor! Thanks for sharing this video
Comment from : @pilipinay594

Thank you, Professor Grimson I appreciate the MIT OCW and I am doing a pseudo MIT challenge for myself
Comment from : @carsongutierrez7072

stay on the screen 99 of the timebrGive the audience something to think aboutbrWhy show the speaker?
Comment from : @minhsp3

Show the screen rather than this guy
Comment from : @minhsp3

20:40 Isn't it a better idea to draw a linear line through the data, and the perpendicular to that line would be the "dividing line" ?
Comment from : @waynelast1685

ML is great but Netflix still can not tell what I really like maybe because I do not like anything so it can not tell anything about me?
Comment from : @waynelast1685

What’s the book for his lecture?
Comment from : @daisydiaz5831

I may seem more like an Arts guy
Comment from : @ChineduPaulOnuegbu

Excellent course, thank you
Comment from : @dav0625

A phenomenal professor who also makes learning fun This is lecture number 11, which means that there are more lectures under this course, but I couldn't find the entire playlist on the channel
Comment from : @zAbdullahKhan

cut the crap,,,has code the ML or not to start with,,,therefore crap,,,nothing happens in computers with no code
Comment from : @georgeageorgopoulos

What if we make a machine learning learn about machine learning is it gonna be the end of human beings?
Comment from : @iiSnely

Feels rushed at the end, kinda boggles my mind, but great lecture otherwise !
Comment from : @newbie8051

22:44
Comment from : @paulordmr

Really insightful
Comment from : @kingsleyokoli1

The only reason I'm interested in machine learning is Astrology: a subject that doesn't require anyone's "beliefs" based on severely lacking information and awareness At least computers are open to learning new things
Comment from : @aidanjohnwalsh2129

Thé concept is understandable but how to make the right algorithm and set the data I’m new to this Anyone cares enough to guide me through this ?
Comment from : @FAYZs

I want to study at MIT! But the costs are insane…
Comment from : @Youtuber_YusukeFromGermany

need more lecctures
Comment from : @sapandeepsandhu4410

Great explanation
Comment from : @adipurnomo5683

Mathematical Modeling to Physical Systems - Part-4 brSolution generated by Dr Abhinav Srivastava ( Phd- Robotics Engineering) under the aegis of SKAP ROBOTICS Online classroom programbrbrKindly follow & subscribe to SKAP ROBOTICS on YouTubebrbrVideo lecture link attached:brbr youtube/E0D0XMyjRnY
Comment from : @drabhinavsrivastava5221

The way u teach is ❤❤
Comment from : @abusaeed2051

Great presentation; but next time the camera operator needs to focus more on the slides rather than the presenter This way we can visually relate to what the presenter is saying Anyways thanks to the prof for the great presentation!
Comment from : @ucheogbede

Thank you #cambridge
Comment from : @antwanwimberly1729

Am I perceiving this correct, MIT has 45min lectures 0o brSo cool, I always feel like 1,5h are too long and too exhausting
Comment from : @schnauzebauze

interesting!
Comment from : @reawardintel

Sounds related to how we developed probability and statistics sums and formulas
Comment from : @Speed001

Always interesting, Great job! Thank you for sharing
Comment from : @55maxtube

Anyone know what book he asked them to read the chapter ?
Comment from : @EM-do1yi

10:35
Comment from : @aaron10k

last testing example confused me b/w FP and FN
Comment from : @syedsaqlainahmad637

But: behind a source code there is a human mind Behind the machine code there is a human mind Machines can learn, but nothing beyond they are taught to learn And that's a restriction Behind all of this is God Humans, atheistic or believers will never cross that line Keep that in mind boys and girls
Comment from : @hannukoistinen5329

Very well explained!
Comment from : @harpyeagle99-e7x

Lppl))
Comment from : @sameeryt5736

This was great thanks for sharing
Comment from : @walkerrichardson

Thank you
Comment from : @jongcheulkim7284

Snakes aren't poisonous! The can be venomous though
Comment from : @robbiesmith79

Hi Op, my question is regarding the challenge of dealing with datasets in my project The feature set contains about 40 columns from merging10 csv files to train my model But the test dataset contains just 2 columns including the target column So each time I try to make classification on the target column I get a failure notice telling me that my training and test shape are not the same brbrbrHow can I make my model to make prediction based on the test dataset given to me? Or how can I adapt my test dataset to conform to the training dataset in shape in order to run my program?
Comment from : @aiziko9072

No wonder an MIT professor!! He lives up to his title Good job!!
Comment from : @cmc634

good algo👍
Comment from : @mehmetcemunal

This is such an awesome video! Btw I'm not a pats fan (go cowboys! lol) but I love how at one point prof points out Gronk and Bennett in his example xD <3
Comment from : @NicolasFioriniA

Very useful lecture I think the axis titles are not in the right order
Comment from : @rawiaelrashidy2139

This is insightful, thanks for sharing 👍
Comment from : @temitopemamukuyomi

Thank you !
Comment from : @user-re4px1ez4i

Wow! Thank You
Comment from : @Przemek1o9

This is so exceptional and simplified, am so grateful for this
Comment from : @olutobajoel125

Awesome Thank you, Sir
Comment from : @fidelca3679

Thanks Modi ji , it was a great lecture !
Comment from : @user-qj4zr1pj9y

Learning father must live The Father
Comment from : @patrick-8068

Thank you MIT
Comment from : @sachinpatil7005

Beautiful lecture I was hooked the whole time
Comment from : @computersciencestuff3405

I:join pleas
Comment from : @fabianusmonepatimonepati6721

Brilliant teachers in MIT I envy the students in this department
Comment from : @kwubegharitony2044

For those that read this, hope you have a nice day!
Comment from : @greendot9106

i need a lecture notecan you share it
Comment from : @norfarahazreenjunaidi2947

Thankyu so much 👍🏻
Comment from : @Forever_curious

Amazingggggg MIT
Comment from : @dhruvbhoi803

Professor Grimson is the best!
Comment from : @retrofutur1st

Wow!
Comment from : @hellokuantum

I have been studying data science and machine learning from past few months from various online sources I have built few projects also by using some github file as reference But i failed to understand the explicit use of probabilitiy in it Can anyone help me understand this with example or provide some good source to learn this? Thank you
Comment from : @chiragpalan9780