All Machine Learning algorithms explained in 17 min

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Information All Machine Learning algorithms explained in 17 min

Title	:	All Machine Learning algorithms explained in 17 min
Lasting	:	16.30
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Views	:	594 rb

Thanks for the video This just explains a lit of things in an good and crystal clearly way in just a short amount of time(just 15min)!! This explained my really clearly and Im always greatful to you 🎉🎉🎉
Comment from : @allthings5757

You should make part-2 of it
Comment from : @preparing10101

Hi, I know near zero about ML, algorithms etc brOnly thing that worries me, is that IF the humans that makes algorithms and provide datasets, have mental heath issues, we are going to produce a crazy machine that learns to be even more crazy
Comment from : @lucianogiudice8569

you make me thrill mate thanks
Comment from : @adikurniawan7072

Amazing Pplz
Comment from : @aracoixo3288

Thanks
Comment from : @Sir-e7p

Explain dsa in like 19 minutes
Comment from : @dhaulagiribgl434

Fantastic work !
Comment from : @TMM-rk4gm

You're awesome, I really needed this high level overview, I'm a visual learner so If I don't see things, I don't usually understand well Thanks for the video
Comment from : @yourlinuxguy

When I think of ML, my mind just goes to Neural Networks
Comment from : @friedoysterskins2942

one of the best 15 minutes you can spend on utube! Thanks
Comment from : @leeamraa

This video is gold Thank you very much for this brilliant content
Comment from : @martinsaravia

In 45 minutes I got through only 6 minutes of it 16:29 is just a timestamp This content is gold Jerry, gold! brListen in 075 or 05 and really pause, write, reflect I learned 3 hrs of Andrew Ng lectures in 6 mins timestamp with 075 speed which was 45 mins of my time Ah! the relativity of time in learning brbrThank you so much for this!
Comment from : @sanjogrijal

Feb'12, 2025 11:07 ambr(Switch 1)
Comment from : @googleit2490

Super amazing👍👍👍Brief and precise
Comment from : @gideonnketiah-v5z

hot dog / not hot dog
Comment from : @mai1100ai

Good lesson
Comment from : @leopardtiger1022

Dont we fit logit function for logistic regression, sigmoid is for neural networks right?
Comment from : @Gauurab

Viagra!
Comment from : @cryptoresearch-i4j

This is so helpful omg
Comment from : @Moonney1

I will share your channel as mush as I can
Comment from : @ABHIJEETSINGH-gm6te

Great explanation! The breakdown of different machine learning algorithms was super helpful Which algorithm do you find works best for most real-world problems you’ve encountered?
Comment from : @AILabHQ

My friends are taking coding classes at Moonpreneur before enrolling in AI courses Can I pursue AI courses without prior coding experience? Please clarify my doubts
Comment from : @NilimaPradhan-us9se

thank you very much!
Comment from : @Sofi-ps5zc

Okay how many people said hi Tim on the intro lol
Comment from : @IrradiatedOne

Terrific video on ML within 16 mintues
Comment from : @abmonsur148

This is really amazing thank you so much
Comment from : @datasciencews

This is one of my favorite videos on youtube
Comment from : @salgadosp

8:13 ayooooooooooooooooooo
Comment from : @chizitereigwe4544

Naive bias classifier should be FYP
Comment from : @gamingfreaks7108

8 semesters in 1527 min
Comment from : @devkumar9889

Great Video🙏👌
Comment from : @ariamehrmaleki8964

Amazing!!
Comment from : @melisacevik3821

Can we have similar video for Deep learning as well
Comment from : @zub3rahmed76

this was so overwhelming
Comment from : @MohdYaqoob-s3k

Cooles Video! Danke dir, btw dein english ist sehr gut
Comment from : @stockbrot1416

This is great video! Nice and clear Thank you 👏🏻
Comment from : @KordTaylor

Really impressive! Thanks!
Comment from :

Is it possible for the machine to predict oncoming results from algorithms and abort going as algorithms ask?
Comment from : @Sebastiaan_Y

Loved it Quick and easy to understand ❤
Comment from : @yamiplays428

interesting example at 8:15
Comment from : @Turtle09

Your video makes me want to run to the library right away No shit Last semester I wasn't satisfied with the grade that I got in stat's I thought I like math but not stats, but maybe that isn't true
Comment from : @cathylee1533

I have to do a ML housing price prediction for my thesis in a week and i don't have anything yet Any advice?
Comment from : @tigraiembeba4447

thank you very much well explained
Comment from : @SAKSHI-o4m

Please Please make moew videos on machine learningbrI find this as the best resource to learn all the concepts
Comment from : @tanishqkhandar7016

Thank you for this well explained video
Comment from : @mertile1dakika

Thank you, I have an final exam in about 14 hours and needed a good refresher on the material!
Comment from : @amlenk

Really well done - thanks for sharing
Comment from : @levon9

Best short-course! However, do you have the same for REinforcement learning, specifically inverse, and how constraint satisfaction associates with ML?
Comment from : @DrHeinzHolzapfel

This man explain exceptionally
Comment from : @abdulwahabchudhary6269

Great video, thanks
Comment from : @BorisAuaro

bro best video on youtube so far, thank you so much for this video
Comment from : @architagarwal007

I'm in confusion between Germany and Ireland for ms in data analytics/science brWhich is best
Comment from : @pavansrikar7491

In 12:56, it shows Association under unsupervised learning How does that task differ from clustering and dimension reduction?
Comment from : @benedicttiu2919

I do not know whether this is a person or not This is the best explanation
Comment from : @sengnawawnghkyeng9179

Great content! But I'd love to see a series of videos exploring each of these algorithms step by step, with real life examples and with proper time for understanding it Throwing it all at once is hard to follow
Comment from : @MarcosTrazzini

nice video but not giving credits for used images is not cool
Comment from : @blynnozaur

Yeah, man You know what? You're some sort of Didactics Super SayanbrThanks for the video Instant subscribe
Comment from : @viniciusmoura9105

Can you give me the file for this presentation?
Comment from : @neutronforever3875

This was awesome!
Comment from : @alexanderpeca7080

Informative
Comment from : @ConnoisseurOfExistence

Wow! I've taken many machine learning courses to date, but his breakdown is spot on! So concise! 🎉👍 Great job Do you have more?!
Comment from : @adrielomalley

Valuable overvidw but the speed of ghe speech seems to be boosted like 3xplease talk slower
Comment from : @ANV1832

What algorithm do you use when the features are tokens and the predicted object is a category?
Comment from : @J3SIM-38

Which algorithm is incremental and continuous?
Comment from : @J3SIM-38

Dude, you just made my concepts so clear in just 17 minutes Now I know what to use for my application Thank you very much! You are Amazing!!!
Comment from : @vigneshpandi3013

This is just awesome, I was trying to learn ml models since 2-3 months but getting confused, this one video made me understand each with clarity in just 1 hour😮, this is awesome ❤
Comment from : @Royalmewati

AI algorithms breed and die faster than bacteria only thing that is faster is AI-Experts and people loosing too much money believing too much in two letters
Comment from : @Heisenberg2097

8:18 Lmao perfect example
Comment from : @FrickinCCDeVileV

this is a great summary for ML learners
Comment from : @bongkem2723

8:15 great example
Comment from : @HarshitPayal-v7l

Awesome sir Many thanks - Nepali from USA
Comment from : @8848nepalyt

I would add the distinction between relationship based and data driven models brbrRelationship-based statistical models rely on predefined hypotheses and the relationships between variables Once a hypothesis is confirmed, additional data is not necessary for validation brOn the other hand, data-driven machine learning models continuously learn and improve from the data, identifying patterns without the need for predefined hypotheses
Comment from : @adamleeperelman

top video! make a part 2 with more advanced algorithms like sarimax etc
Comment from : @Νικόλαος-β3σ

Great video, thanks for making this At the end however, I’m unable to see the last two slides due to cards covering it
Comment from : @mihir777

successfully observation to learn them
Comment from : @Marc-005-c3b

The best video among others on the subject I've been passing through Thank you
Comment from : @irinabelenkaya5156

Youtube recommend this channel as No Fluff channel,
Comment from : @KeithAdams-p8z

Great explained and good to remember some algorithms in the future
Comment from : @alejandroilamo3177

Thank you❤
Comment from : @xMichi7

Ironic that we have a Decision Tree to decide which model to use
Comment from : @matthijsperabo7282

SOME AI BULLCRAP VIDEO
Comment from : @timtim4664

Awesome video, thanks
Comment from : @RolandoLopezNieto

Thank you; it was super helpful for me to understand the big picture of ML!
Comment from : @jainjanechoi

Good video but try to speak a little slowly next time!
Comment from : @FauzanTahir

Self-organizing maps?
Comment from : @Flodis

Why are you not including reinforcement learning i the video? You are at 0:50 saying machine learning is divided into 2 subfields, supervised and unsupervised learning, when in reality there are 3 Reinforcement learning is a very important part of machine learning as well and is very well knows as a 3rd subfield of it I also noticed you "simplified?" some things in the video almost to the point of becoming lies which I can't say I like very much Lying to make a video simpler is not the right way to go
Comment from : @hickek6607