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

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!

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

This is so underrated! Thank you so much :)

Absolute banger of a video.

great introduction for anyone new to ML

I love this type of videos thanks for summarizing

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."

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 👍

Thanks for this video!

Great explanation!

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 ??

This is amazing, thank you. Like button hit

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

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...

Nice overview.

thank you

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

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

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

Thanks

It's useful :)

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

How about Gaussian Mixture Model and EM algorithm..

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

Finally a quick gist.

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

Isn't the sigmoid function outdated?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

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

Where neutral networks at?

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

Naive is pronounced "nigh-eve"

These are ML algorithms not sorting algorithms tho 😅

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 🤔

timestamps please, no time to watch