CinemaGuess

3 ай бұрын

All Types of Infinities Explained in 3 Minutes

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@jllakshminarayanna77403 күн бұрын

00:01 Linear regression models the relationship between continuous target variables and independent variables 01:48 SVM is effective in high-dimensional cases but may have training time issues. Naive Bayes is fast but less accurate due to its independence assumption. Logistic regression is simple yet effective for binary classification tasks. 03:40 Logistic regression uses the sigmoid function for binary classification. 05:30 KNN is simple and easy to interpret but becomes slow with high data points and is sensitive to outliers. 07:10 Random Forest is an ensemble of decision trees with high accuracy and reduced risk of overfitting. 08:53 Boosting and K-means clustering explained 10:40 K-means clustering and DBSCAN are key clustering algorithms. 12:25 DBSCAN algorithm and its features Crafted by Merlin AI.

@co597204 күн бұрын

Hey bro I heard you like a high level overview about your high-level overviews about your high-level overviews❤ I don't know which direction to go in this rabbit hole but I do know which thing to push against and which thing to pull near❤ Now don't do like everyone else does and drill down keep panning back and give us a high level overview of the high-level overview of the high-level overview it is a fractal Universe after all❤Subbed. 😊

@DanielUdoh-ej9nh6 күн бұрын

I have read through a couple of encouraging comments, deservedly so, but I believe this video can be better, more engaging and entertaining. Learning is and should be fun, it’ll be helpful for you and your viewers if you reflected that more. Use simple words, more engaging animations, include jokes and comics. Cheers, To Growth. 🥂

@DanielUdoh-ej9nh6 күн бұрын

Also incorporate more enthusiasm in your voice. I commend you on your efforts thus far, the first steps can be incredibly hard, and you took them, well done.

@enchance7 күн бұрын

Why can't all ML online classes start this way? You're the man!

@aman_the_one10 күн бұрын

Just realised I have gone through mathematics of all this algos(and more) in deep during my Undergrad. How did I survived it?

@googooboyy11 күн бұрын

Either I've been playing Chess wrong or this is the best variant yet!

@xxsamperrinxx399312 күн бұрын

Bro said "knave"

@user-fg2qw3mc8y15 күн бұрын

I'm new to machine learning and I don't really know what do you mean by all, are this algos the only existing algorithss in ML or what ?

@jaybrodnax15 күн бұрын

Didn’t even include back propagation what

@jaybrodnax15 күн бұрын

“Summarized as quickly as possible “ is not “explained “

@AryanPatel-wb5tp8 күн бұрын

time stamp ?

@johanlofilelo535915 күн бұрын

7:30 nah i lost

@faridsaud656717 күн бұрын

Great video!! Just one thing, k means is not built on the EM algorithm...

@gat0tsu19 күн бұрын

solid

@zgoaq21 күн бұрын

ну видно что чубз не из профессуры. читает то шо сам не знает

@shivampradhan610124 күн бұрын

great introduction for anyone new to ML

@tmanley198524 күн бұрын

When learning anything new, it's nice to get a lay of the land before you start or else you just end up in rabbit holes with no sense of where you're going. This is a great overview!

@co597204 күн бұрын

I'm steeling your quote! Really excellent phrasing!

@amandac090325 күн бұрын

Pleaseeee do more videos on machine learning u summed this shit up so good

@janneskleinau633225 күн бұрын

4:30 Isn't kNN an unsupervised Learning algorithm?

@faridsaud656717 күн бұрын

It is normally used for classification or regression, and these are supervised tasks, as you need labels. I haven't heard of it being used in an unsupervised fashion, but who knows at this point lol

@keenshibe752916 күн бұрын

@faridsaud6567 it explicitly requires labelled data to make predictions so no

@joseivan233712 күн бұрын

KNN is supervised, it's the K-means clustering that is unsupervised

@rubyrose-san957Ай бұрын

thx, my teacher will like my assignment bc of your vid.

@enasmagedАй бұрын

Thanks

@Moiez101Ай бұрын

dang, 14 min eh, beast mode! Let's goooo

@ElBondin1701Ай бұрын

I think i have been playing the wrong chees this whole time

@TravisssssАй бұрын

IM HERE BEFORE THIS PAGES BLOWS UP KEEP GOING BRO !

@LubulaAfritechАй бұрын

This is amazing, thank you. Like button hit

@HackingBinaries-dt2fhАй бұрын

I love Linear Regression, SVMs, Logistic Regression, Random Forest and Gradient Boosting

@AnEasyGuy22Ай бұрын

Where neutral networks at?

@misraimburgos7461Ай бұрын

Thats Deep Learning. This video it's just some ML algorithms

@geevarjos7054Ай бұрын

Thanks for this video!

@girishghadge8460Ай бұрын

Wow very crisp no left right just on target I think this should be considered as an algorithm of an impactful concept video great work keep it up thanks 👍

@matthewgalitz8028Ай бұрын

Isn't the sigmoid function outdated? I thought learning algorithms use LRU now.

@cinemaguess200Ай бұрын

Bro to be honest I just looked all of these up on google lmao. But I do remember hearing about sigmoid years ago so you’re probably right

@SharodWilliams8Ай бұрын

Great explanation!

@VedranationАй бұрын

Glad to have found your channel. Super great and informative videos! Just please change or slow down background, its really distracting

@VedranationАй бұрын

awesome content

@MAYANK-mn8irАй бұрын

Hi, is anyone currently enrolled in Masters with major in ML in Canada/US? How is the Job market there?

@alihaiderkhan25Ай бұрын

Could you plz Start a Series to teach each algorithm in details.

@justlikeit417Ай бұрын

Great job, however there are still many left, LDA, Gaussian Mixture Model, Canopy Clustering, all of Deep Learning...

@not_a_human_beingАй бұрын

amazing stuff! (except, where are NNs? kek)

@r0cketRacoonАй бұрын

I dont understand the point of using bootstrapping method in random forest. Could someone explain easily for me?

@faridsaud656717 күн бұрын

Bootstrapping allows for more diverse subsets of data, which in a way prevents overfitting. It also makes the trees more diverse, which helps with generalization.

@TobiMetalsFabАй бұрын

Absolute banger of a video.

@user-xb4wt2el9sАй бұрын

It's useful :)

@haraldurkarlsson1147Ай бұрын

Nice overview.

@jeanpeuplu3862Ай бұрын

This is so underrated! Thank you so much :)

@mohamadcheaito9088Ай бұрын

Hi, your channel looks promising and the way all the algorithms are explained in a simple way is great. As a favor can you give me the music played in the background ??

@mein.c.tut.wАй бұрын

Hey very nice video! Some genres i missed... no breakcore no noize no doomcore (tekkno) no tekk (differenciation between a tekkno subgenre (Tekk) to its parent genre (Techno) wich exists in austria and some other eastern and southern european countries but not in germany and other western European countries) 😢

@breathemath4757Ай бұрын

Nice video but why so confidently claiming all learning algorithms when not even close?

@cinemaguess200Ай бұрын

Because “Some Learning Algorithms” is a terrible title lmao

@Logic_Bum11 күн бұрын

@@cinemaguess200Lying to people is worse.

@yanovoyair5129Ай бұрын

dont stop making videos pls, it's a pleasure you are simplying it, for people that want or not want to go to mathematic's realm, thank you..

@atharvabaviskar1129Ай бұрын

It was not 14 min video rather it take 1 hr to digest the knowledge but good one

@redfang3718Ай бұрын

thank you

@VladKochetovАй бұрын

0:22 linear regression 0:51 SVM 2:18 Naive Bayes 3:15 logistic regression 4:28 KNN 5:55 decision tree 7:21 random forest 8:42 Gradient Boosting (trees) 9:50 K-Means 11:47 DBSCAN 13:14 PCA

@shadowskullGАй бұрын

8:42 is not typing all of that

@Harry_065628 күн бұрын

😮

@s8x.2 ай бұрын

thank you for this. u just taught an entire machine learning course in 14 minutes. gods work

@djangoworldwide7925Ай бұрын

Umm.. no he didn't, and if your entire machine learning course doesn't extend beyond the scope of this nice video, you should leave and ask for your money back. This video is nearly a glance into the wonder world of ML (no deep learning even), But it does not provide you with any practical skills. Well, duh, it's only 14 mins.