All Learning Algorithms Explained in 14 Minutes
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- čas přidán 25. 02. 2024
- 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
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
8:42 is not typing all of that
😮
This is so underrated! Thank you so much :)
thank you for this. u just taught an entire machine learning course in 14 minutes. gods work
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.
Are u fr bruh
All of these are outdated now
@@_rd_kocaman why? These algorithms are still being used
Absolute banger of a video.
I love this type of videos thanks for summarizing
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 👍
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."
I see you also have achieved your self-conciousness
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 ??
Great explanation!
Thanks for this video!
This is amazing, thank you. Like button hit
Nice overview.
Could you plz Start a Series to teach each algorithm in details.
Great job, however there are still many left, LDA, Gaussian Mixture Model, Canopy Clustering, all of Deep Learning...
I love Linear Regression, SVMs, Logistic Regression, Random Forest and Gradient Boosting
dang, 14 min eh, beast mode! Let's goooo
thank you
Thanks
amazing stuff! (except, where are NNs? kek)
Finally a quick gist.
It's useful :)
It was not 14 min video rather it take 1 hr to digest the knowledge but good one
How about Gaussian Mixture Model and EM algorithm..
I dont understand the point of using bootstrapping method in random forest.
Could someone explain easily for me?
Hi, is anyone currently enrolled in Masters with major in ML in
Canada/US?
How is the Job market there?
Naive is pronounced "nigh-eve"
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.
@@voncolborn9437 It appears as if the fool is actually me.
Isn't the sigmoid function outdated? I thought learning algorithms use LRU now.
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
Where neutral networks at?
Thats Deep Learning. This video it's just some ML algorithms
These are ML algorithms not sorting algorithms tho 😅
lmao good point
Nice video but why so confidently claiming all learning algorithms when not even close?
Because “Some Learning Algorithms” is a terrible title lmao
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 🤔
Our intelligence (entirely oversimplified) is mostly baysian and implemented on networks of interconnected neural networks.
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.
@@vrclckd-zz3pv i agree with you.
@@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?"
timestamps please, no time to watch
Better time management maybe?
@@dennisestenson7820 full busy in procrastination
dude it's 14 min and you have 24 hours in a day
😂
@@KHe3CaspianXI bruh