All Learning Algorithms Explained in 14 Minutes

comment your favourite algorithm below
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

[@VladKochetov]

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

[@shadowskullG]

8:42 is not typing all of that

[@Harry_0656]

😮

[@tmanley1985]

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!

[@s8x.]

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

[@djangoworldwide7925]

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.

[@cate9541]

Are u fr bruh

[@_rd_kocaman]

All of these are outdated now

[@AnEasyGuy22]

@@_rd_kocaman why? These algorithms are still being used

[@jeanpeuplu3862]

This is so underrated! Thank you so much :)

[@jamieyoung3770]

There's a typo in the slides that I think was just put in to test if I was paying attention. In the voiceover it says "a point is a border point if it is unreachable" but in the slide it is written"a point is a border point if it is reachable". May I suggest you change both the written and spoken portion and instead have it say and read "the most delicious pizza topping combinations are figs, prosciutto and goat cheese."

[@Laszer271]

I see you also have achieved your self-conciousness

[@TobiMetalsFab]

Absolute banger of a video.

[@ibrahimahmed3434]

I love this type of videos thanks for summarizing

[@girishghadge8460]

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 👍

[@mohamadcheaito9088]

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 ??

[@SharodWilliams8]

Great explanation!

[@geevarjos7054]

Thanks for this video!

[@alihaiderkhan25]

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

[@paulochikwekwe7862]

This is amazing, thank you. Like button hit

[@haraldurkarlsson1147]

Nice overview.

[@justlikeit417]

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

[@redfang3718]

thank you

[@shivampradhan6101]

great introduction for anyone new to ML

[@amandac0903]

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

[@Moiez101]

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

[@HackingBinaries-dt2fh]

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

[@enasmaged]

Thanks

[@not_a_human_being]

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

[@user-xb4wt2el9s]

It's useful :)

[@janneskleinau6332]

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

[@user-ui3kf2fr3y]

Finally a quick gist.

[@atharvabaviskar1129]

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

[@MAYANK-mn8ir]

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

[@otheanh5306]

How about Gaussian Mixture Model and EM algorithm..

[@matthewgalitz8028]

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

[@cinemaguess200]

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

[@r0cketRacoon]

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

[@AnEasyGuy22]

Where neutral networks at?

[@misraimburgos7461]

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

[@tiny3607]

Naive is pronounced "nigh-eve"

[@voncolborn9437]

I noticed that he started out pronouncing it incorrectly then 'magically' started saying it correctly. My guess is that the narration is AI generated. When used as part of a compound word it was pronounced incorrectly but when used alone it was usually correct.

[@tiny3607]

@@voncolborn9437 It appears as if the fool is actually me.

[@canbeexplained]

haha you actually think it's AI@@voncolborn9437

[@breathemath4757]

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

[@cinemaguess200]

Because “Some Learning Algorithms” is a terrible title lmao

[@icebluscorpion]

So... Using all of them and fitting them in the right way then you will get a good AGI? I mean humans have this process in a way too... Otherwise humans wouldn't be NGI right 🤔

[@dennisestenson7820]

Our intelligence (entirely oversimplified) is mostly baysian and implemented on networks of interconnected neural networks.

[@vrclckd-zz3pv]

The video title lied. This isn't all ML algorithms. I think he just went over all ML algorithms in the SciKit library for Python.

[@icebluscorpion]

@@vrclckd-zz3pv i agree with you.

[@icebluscorpion]

@@dennisestenson7820 thats what I want to say. Did you ever heart about Memristors? They do all those simulated neural connection stuff nowadays with those components in a chip. Those memristors have similar behavior like neurons. Which drastically decreases power consumption for "Calculations?"

[@prathamjain1310]

These are ML algorithms not sorting algorithms tho 😅

[@cinemaguess200]

lmao good point

[@KHe3CaspianXI]

timestamps please, no time to watch

[@dennisestenson7820]

Better time management maybe?

[@KHe3CaspianXI]

@@dennisestenson7820 full busy in procrastination

[@PixAlphere]

dude it's 14 min and you have 24 hours in a day

[@lupino652]

😂

[@notsojharedtroll23]

​@@KHe3CaspianXI bruh