All Machine Learning Models Explained in 5 Minutes | Types of ML Models Basics

[@katiecallaghan5005]

Learned more in this 5 minute video than I have in the past two months of watching other videos. Thanks so much for your help

[@learnwithwhiteboard]

Hi Katie, thanks for your love, support and appreciation ❤️👍 I’ll try my best and share more such quality videos with you!

[@philipp4361]

please half the volume of the background music in future videos! good content though, thumbs up!

[@learnwithwhiteboard]

Yes, for sure, thanks for the feedback, means a lot!

[@retiredteacher6289]

No. Get rid of the music entirely. It is very distracting. There is no need for music if you have something worth saying.

[@NeoKailthas]

Music is good just lower is better. Really good reference video by the way

[@dr.ranabasu517]

Good

[@dr.ranabasu517]

Good

[@isaachernandez3094]

I'm taking a machine learning course and this is what I needed to completely understand each method. Great video, concise and easy to follow. Thanks!

[@learnwithwhiteboard]

Glad it was helpful! All the best with your course :)

[@SanjeevKumar-nc2rt]

From which course you are learning. I also want to learn machine learning but not able to find which course is best for me. Cam you suggest me which course i should take?

[@kenconcepts]

What can you do once you know machine learning? I'm still clueless but I'm into A.I and automotive

[@learnwithwhiteboard]

@@kenconcepts Hello my friend, see, AI & ML are efficiency-based concepts, meaning they let a business remove repetitive business tasks/processes and automate them (to some extent) with the help of technology. This, in long term, is not just efficient, but also cost effective. But still, after so much development in AI & machine learning field, there are certain areas that they CANNOT add much value to, I'll recommend you to watch this video: kzbin.info/www/bejne/eIu4eaarepeFpNU Hope this was helpful!

[@DanielTobi00]

Same here

[@LiorLeiba]

Been trying to fully figure it all out for a while and here you were concisely explaining the key pointers in exactly 5 mins. Thanks for your help & keep on with the good job mate !

[@learnwithwhiteboard]

Glad it helped you, my friend!

[@PalataoArmy]

Only few thousands sees this video and I found this is the most brief and easiest to understand the basics of ml model. Thank you.

[@learnwithwhiteboard]

Glad it was helpful! :)

[@cavidanhacili7723]

1.1 M views)

[@anandnarvane8755]

He has expainened all branches of machine learning ......that is good for biginers like me. To understand ml..

[@KennTollens]

Thank you, I really needed an overview. Now I have a container to put the information in as I learn it.

[@learnwithwhiteboard]

Happy to help Kenn! 👍😄

[@christopherwright361]

I always find it interesting that the statement "logistic regression is a classification method" is repeated so often, despite 'regression' being right there in the name. Like decision trees (CART), random forest and other similar methods, there are both regression and classification versions of the method. In a binary outcome problem, we model the process as flipping a coin that has a probability p of coming up heads (1) and 1-p of coming up tails (0). We can either predict p, a continuous value between 0 and 1, or we can predict the outcome, itself, which is in {0,1}. The former is regression; the latter is classification. Logistic regression predicts that probability p and logistic classification takes that probability and uses a cutoff (sometimes, but not always, 0.5) to predict the outcome. Many times, the probability is more important (for example, lots of the same type of customer -> you do not want to assume all of them are 1's, when p is estimated to be 0.6) and sometimes the outcome is more important (for example, making a decision to approve an application for credit.)

[@devaredmi]

Thanks for the clarification/correction.

[@dhruvbala4093]

Good point. This was one of my qualms with exams like Microsoft's AI-900 certification -- the answer choices fail to account for nuance

[@jacobkanyi5046]

Thank you, i had trouble understanding why Logistic regression is under classification

[@Amar-hl6pu]

You are correct and the video misinforms about Decision Trees, Random Forest , SVM, and even Neural Networks - all of which have a regression and classification usecase.

[@WHATuWATCHisWHATuGET]

thanks for the beautiful explanation!

[@CC-ed7jr]

Thank you, my class was s bit overwhelming and this helps to distinguish the stuff more

[@learnwithwhiteboard]

I'm happy that it was helpful to you, I'll try my best to upload more good videos to help you understand many more concepts!

[@maxturgeon89]

It may surprise some people, but logistic REGRESSION is in fact a regression model. The output is continuous: it's a probability. Of course you can dichotomize the output and create a classifier. Just like you can dichotomize any score function or regression model output. So the distinction between regression and classification is not so clear cut.

[@arpithashe]

This is what Azure AI 900 course articles says - Note Despite its name, in machine learning logistic regression is used for classification, not regression. The important point is the logistic nature of the function it produces, which describes an S-shaped curve between a lower and upper value (0.0 and 1.0 when used for binary classification).

[@Sarah-qo2ut]

I was passed out just by thinking about my ML exam and this video boosted up my confidence I mean at least now I’ve an idea about that subject. Thanks to you

[@learnwithwhiteboard]

Happy to help! 😄👍❤️

[@CAGonRiv]

Exam? Grad school?

[@pauloszewierenko6978]

Great ! It gives an overview of the subject and acts as a roadmap to dive deeper into the study of ML

[@learnwithwhiteboard]

I want to sincerely thank you for your kind words and appreciation 👍❤️

[@bunnyistaken]

It was an amazing refresher for me.. Now I know where I need to concentrate more and learn more.

[@learnwithwhiteboard]

Glad it was helpful!

[@luiscastellanos422]

I love how the explanation is made to everyone with experience or not in this issue. I’m in the second group and now I have a reference to start. Thank you ☺️

[@learnwithwhiteboard]

Glad it was helpful! Thank you for your love, support and appreciation 👍❤️

[@suhasiniambare762]

The summarising ML model is really good. It is very helpful.Thank you sir.

[@learnwithwhiteboard]

Thanks for your love, support and appreciation ❤️👍 I’ll try my best and share more such quality videos with you!

[@thej1091]

great video. Highly underrated channel!

[@learnwithwhiteboard]

Thanks for your Kind words Kiran! 👍😄

[@brilliantlights]

Great compilation. Very well presented. Clear explanation. One of the very few videos on YT that sums it all so well. It would have taken a lot of effort but worth it, benefiting so many, thanks!

[@learnwithwhiteboard]

Glad you liked it! ❤️👍

[@levon9]

Really nice summary, thanks for putting this together.

[@learnwithwhiteboard]

I'm happy that it is helpful ☺️👍!!

[@Dwarf_Software]

Very well edited man and the explanation was great!

[@learnwithwhiteboard]

Glad it was helpful, buddy!

[@kolesplace]

This would be a good video for the person who already understands the vocabulary of machine learning and the different aspects of machine learning. I didn't really gain any knowledge from this video since I'm starting from the beginning of ML.

[@MrShubi444]

i needed this straight forward video for a very long time thank you so much :)

[@learnwithwhiteboard]

A pleasure to be of help Shubham, would try my best to upload more such quality videos! 👍

[@JohnJonesJMJAtlanta]

Just the info I needed. Thanks!

[@learnwithwhiteboard]

Glad it was helpful!

[@TheRealDouglasJOlson]

I find that you glossed over the details of the more difficult models. But provided examples and more depth for the simpler models. For individuals learning ML for the first time, there is a need for people to understand the more complex models in depth. I suggest adding deep dives into each specific model.

[@Asce123]

Thank you this saved my life

[@learnwithwhiteboard]

Glad it was helpful Kexin! ❤️👍

[@adcommunication4]

🎯 Key points for quick navigation: 00:00 *🤖 Introduction to Machine Learning Models* - Supervised learning models map inputs to outputs based on example pairs, such as predicting shoe size based on age. - There are two subcategories of supervised learning: regression and classification. - Regression models find a target value based on independent predictors, like the relationship between age and shoe size. 00:57 *📈 Regression Models* - Regression models have a continuous output and are used to find relationships between dependent and independent variables. - Common types of regression models include linear regression, multiple linear regression, and polynomial regression. - Linear regression finds a line that fits the data, while multiple linear regression finds a plane of best fit and polynomial regression finds a curve of best fit. 01:01:24 *🌲 Decision Trees and Random Forests* - Decision trees are models with nodes, and the more nodes, the more accurate the tree. - Random forests are ensemble learning techniques that build off of decision trees. - Random forests create multiple decision trees using bootstrap data sets and randomly selecting subsets of variables, reducing the risk of error. 02:00:06 *🧠 Neural Networks* - Neural networks are multi-layered models inspired by the human brain. - They consist of nodes (neurons) and connections between them. - Neural networks can be used for various tasks, including image recognition and natural language processing. 02:06 *🧠 Neural Networks and Classification Models* - Neural networks are multi-layered models inspired by the human brain, with input, hidden, and output layers. - Classification models have a discrete output and are used to categorize data into distinct classes. - Common types of classification models include logistic regression, support vector machines, and Naive Bayes. 03:35 *unsupervised learning ❄️ Unsupervised Learning* - Unsupervised learning draws inferences and finds patterns from input data without labeled outcomes. - Two main methods in unsupervised learning are clustering and dimensionality reduction. - Clustering groups data points and is used for customer segmentation, fraud detection, and document classification. 04:02 *📊 Clustering and Dimensionality Reduction* - Common clustering techniques include k-means clustering, hierarchical clustering, mean-shift clustering, and density-based clustering. - Dimensionality reduction reduces the number of features in a dataset. - Principal component analysis (PCA) is a popular dimensionality reduction method. Made with HARPA AI

[@CromaCampusOfficial]

Thanks for explaining all the learning modes of machine learning.

[@learnwithwhiteboard]

A pleasure indeed to be of help 🙂

[@wlchao5852]

nice video, straightforward and comprehensive

[@learnwithwhiteboard]

Thanks for your love, support and appreciation, my friend 👍❤️

[@gracegitome2625]

The explanation is simple and easy to understand. Thankyou.

[@learnwithwhiteboard]

Glad it was helpful!

[@whatsurb]

this video is very helpful for interview purposes.

[@learnwithwhiteboard]

Thank you Surabhi for your appreciation ❤️👍

[@thomasbates9189]

Very helpful. Thank you for making this!

[@learnwithwhiteboard]

A real pleasure Thomas, and thank you for your appreciation my friend! ❤️👍

[@thomasbates9189]

For sure thanks for making quality educational content!

[@jonathanmardini4498]

great high-level framework for how to bucket different ML methods

[@learnwithwhiteboard]

Glad it helped!

[@9zhivago]

Fantastic and concise summary.

[@learnwithwhiteboard]

I'm happy that you liked my work ❤️😃

[@edwardalabi5654]

Amazing content, straight to the point whilst still being detailed

[@MrFischvogel]

Thanks for making it so concise ! Top.

[@learnwithwhiteboard]

Thank you for your love, support and appreciation. I'll try my best and share more such quality videos with you. Cheers! ❤️👍

[@petermorgan5645]

Really liked this video. Thanks very much!

[@learnwithwhiteboard]

Glad it was helpful, buddy!

[@YoutubeWatcher3566]

Thanks bro this helps alot, will be watching it multiple times until my brain can sponge it in.

[@DavidMortaz]

Great primer on ML Models! I would suggest slowing down when you talk and also do a FINAL slide showing all the subcategories you covered under each Supervised and unsupervised models. I would also use real world examples of where each model is being used and applied today.

[@learnwithwhiteboard]

Done David, thanks for the feedback, I'll account this when creating videos from now on

[@thakurcolin]

Nice overview. I think you were super excited. The speed of your chat shows your deep (no pun intended) understanding of the material!

[@eldwinc1546]

no need to slow down, just change video speed. that's what technology is for

[@vigneshmamidi9774]

Thank you u made it simple to understand.

[@learnwithwhiteboard]

Thank you for your love, support and appreciation. I'll try my best and share more such quality videos with you. Cheers! ❤️👍

[@sanjanabhattacharjee4682]

It was very helpful. Thank you.

[@learnwithwhiteboard]

Glad it was helpful, buddy!

[@mihiretuabj145]

I like the explanation of MLDL, thanks

[@learnwithwhiteboard]

You are welcome, my friend!

[@EasyAIForAll]

Interesting how many people in the comments call Logistic Regression a regression model... Logistic Regression outputs a category, not a continuous value. To understand deeper: Logistic Regression = Linear Regression + Sigmoid (or SoftMax for multi-class) + Unit Step function Thus, for the binary case, imagine the prediction of the linear regression (a continuous value from -infinity to +infinity) that is than squeezed into the range of 0 to 1, which is not a straightforward probability, but we can interpret it that way. Lastly, a unit step function is applied (the threshold), which makes the prediction categorical. So, while part of it outputs continuous values, the final outputs is categorical and Logistic Regression is always used as a classification model in the literature.

[@neerajshrivastava5600]

Awesome!!! Truly Outstanding content, Thank you.

[@learnwithwhiteboard]

Glad it was helpful! I'll try my best and publish more such quality videos.

[@juicyburgi1944]

Ty, this helped me alot in my report!

[@learnwithwhiteboard]

Glad it was helpful! I'll try and share more such valuable videos with you.

[@udaykumarchittala7375]

After seeing this video i got a clear idea about all the models thank you😀

[@learnwithwhiteboard]

Happy to help! 😄

[@manjeetkaur-eu3jl]

Thanks for explaining machine learning models with such precision, can’t believe it was so short... I can now decide and lead my team on best ml model to select!

[@learnwithwhiteboard]

Happy to help! I would try to serve the same precision in my future videos. :)

[@venkatsusheelg9658]

lol

[@out_aloud]

@@venkatsusheelg9658 was here to make the same comment lol

[@rzbruno]

That was a really nice summary!

[@learnwithwhiteboard]

Glad to hear it! I'll try to publish more insightful videos like this one. Thanks for love & appreciation :)

[@DenisDuvauchelledev]

Great overview. Thank you very much.

[@learnwithwhiteboard]

It's a pleasure Denis Glad to be of help ☺️

[@Rock4temps]

Super refresher, well done

[@learnwithwhiteboard]

Thanks Julien for your kind support, love and appreciation ❤️👍

[@petermathes2061]

This is awesome. Package your stuff into a Udemy teaching course that rewards you for all this shared knowledge and wisdom about ML!

[@learnwithwhiteboard]

Wow, thanks for kind words! I'm trying to create a course Peter, trying even harder to launch it soon. Once I have it live, I'll make sure to keep you posted. Thanks again for the appreciation. 😊👍

[@alexlangevin8340]

No

[@ricodsanchez6792]

@@learnwithwhiteboard Hi there, have you created a course yet? i would like to buy one from you. Kind regards

[@blueb06]

Liked !Subscribed ! Shared ! Request more clearcut videos like this !

[@learnwithwhiteboard]

Sure Bhavna, will try to upload more quality videos like this and help people like to learn better & learn faster... Thanks for your support!

[@blueb06]

@@learnwithwhiteboard :)

[@udaykumar4B3]

Excellent job 👏 Very helpful to see overall view of all the models

[@learnwithwhiteboard]

Thanks Uday for your love, support and appreciation. I’ll try my best and share more such quality videos! ❤️👍

[@faaizaziz6430]

thank you. Very easily explained. Brief and simple.

[@learnwithwhiteboard]

Glad you liked it Faaiz :)

[@khaledzoheir7566]

Wow, excellent and to the point. Great work

[@learnwithwhiteboard]

Thanks Khaled for your love, support and appreciation ❤️👍

[@saipri]

Very well explained! look forward to more such videos...

[@learnwithwhiteboard]

Hi Priya, thanks for your kind words & appreciation ❤️👍, will try my best and produce more such quality videos. Have a great day!

[@thinkwrite869]

Bundle of thanks for this

[@learnwithwhiteboard]

Glad it was helpful Kexin! ❤️👍

[@에세이임수필]

Thank you for the summary of the machine learning. Hope i meet more usage and example video which comes from Machine Learning.

[@learnwithwhiteboard]

Thanks for your love and appreciation. I will try my best to deliver more value centric videos! 👍❤️

[@rodrigomericq5296]

great explanation, congratulations

[@learnwithwhiteboard]

Thanks Rodrigo for your kind words and appreciation ❤️👍

[@kannangoamakonde880]

Very informative to starters

[@learnwithwhiteboard]

Glad it was helpful! ❤️ 👍

[@theaieducator1595]

Thank you for some useful videos

[@learnwithwhiteboard]

Glad you like them! I'll try and share more such valuable videos with you. Thanks for all your love, support, and appreciation ❤️👍

[@byrospyro4432]

You can also get multi-class classification where the output is more then 2, also you can use neural networks for classification also.

[@Kurtmind]

This is a very informative video! Well done !

[@learnwithwhiteboard]

Glad it helped! Thanks for your love, support, and appreciation! 👍❤️

[@Saou8794]

thank you it's really good explaining

[@learnwithwhiteboard]

Glad it was helpful!

[@FabianBarajas]

Thanks for posting this video. Your explanations make it really easy to get a high-level understanding

[@learnwithwhiteboard]

Glad it was helpful! 🙏❤️

[@LucasChomusuke]

I wish you the best luck!

[@learnwithwhiteboard]

Thank you Lucas 🙏❤️

[@PerfectlyNormalBeast]

Great summary As a visual person, it would have helped if you zoomed out occasionally when jumping topic to topic, so I could see the tree

[@learnwithwhiteboard]

Hello my friend, Thanks for the feedback, I'll incorporate this in my future uploads. Have a good day ☺️👍❤️

[@krishanudutta2943]

Thanks a lot for this awesome video❤️❤️

[@learnwithwhiteboard]

My pleasure Krishanu 😊 I'll try and share more valuable videos like this. Thanks for you support, love and appreciation! ❤️

[@jmp0207]

Great video! Keep up!

[@learnwithwhiteboard]

Thanks, will do! ❤️👍

[@umakantapradhan1424]

very good, excellent description

[@alternbits]

Thank you. it was awesome

[@learnwithwhiteboard]

Glad you liked it!

[@md.alamgir2209]

Short but excellent presentation

[@ighsight]

Good job. Smart devs should never forget about the newbs.

[@hr2079]

Good overview. Thank you for sharing

[@ernesto68a]

Muchas gracias. Video muy ilustrativo, concreto y preciso para tener un panorama de los modelos de aprendizaje automático (ML)

[@learnwithwhiteboard]

El placer es mío señor 👍😊❤️

[@vidyasankpal8507]

Found short and sweet video 🥰

[@learnwithwhiteboard]

Glad you found it helpful Vidya ❤️👍

[@Randomuser11231]

Great intro for the beginners.

[@autaj]

Wow, this is so so good

[@learnwithwhiteboard]

Thanks Jonathan for your love, support and appreciation … I’ll try my best and continue to share more such quality videos ❤️👍

[@user-saint]

Well done. Thank you.🙏

[@learnwithwhiteboard]

Its a pleasure my friend! I'll try my best and share more such quality videos 😊

[@TheBuzoTechie]

Great video, very useful for the machine learning library I plan on making :)

[@xaverpfk]

Great overview. Thanks

[@rose8968]

Good summary. Very helpful !

[@learnwithwhiteboard]

Thanks Emma for your love and appreciation ❤️👍

[@dharam35]

Great video, thank you !

[@learnwithwhiteboard]

Glad you liked it!

[@harshpimpalkar5948]

Great work 👍👍

[@learnwithwhiteboard]

Thanks Harsh for your support and appreciation ❤️👍

[@quratraja7391]

Very well explained!

[@sketchytv1321]

good job done i learned quite well

[@learnwithwhiteboard]

Thanks Hasan for your love, support and appreciation ❤️👍

[@rahulupadhyay3570]

Best video on this topic

[@kumarnityanand4731]

Appreciated 👏👏👍👍👍👌

[@learnwithwhiteboard]

Thanks for liking 👍❤️

[@marcfruchtman9473]

Great stuff. Thanks for the overview video. Background music is much too loud tho.

[@johnlaw3276]

this is really good, thank you

[@MyDeadDude]

Great content!!! brilliant explaination & please get rid off the telemarketing music

[@learnwithwhiteboard]

Thanks for the appreciation, I have corrected this mistake in my future uploads.

[@li-pingho1441]

this video save my life

[@vd7851]

Thank you for your video

[@learnwithwhiteboard]

Its a pleasure to be of help to you ❤️👍

[@PavelPospisil1]

Can you please provide the last summary shot of the video, with all methods on one page? Advertising new other videos are actually hindering the view of it. Excellent explanations, by the way, thank you.

[@499shaikfarooq6]

thanks man appreciate it!

[@rayerdyne]

Well, one can expect to get a deeper understanding about guitar than machine learning seeing this :P Imo, a complete explaination of these would require one hour per type of model

[@adityajha4373]

Thanks sir... Much appreciated ❤

[@learnwithwhiteboard]

You're welcome 😊

[@nawid21]

The moment i heard the indian voice i just knew it would be an awesome video :)

[@learnwithwhiteboard]

Thanks for your sweet words and appreciation, my friend ❤️👍

[@jmc3179]

Awesome video!

[@learnwithwhiteboard]

Thanks for your love and appreciation 👍❤️

[@priyankagautam9470]

Well explained and precise video👍

[@learnwithwhiteboard]

Glad it was helpful! :)

[@anirbanaws143]

Subscribed and liked!

[@learnwithwhiteboard]

Thanks a lot Anirban, love your support!

[@pratyushmondalofficial]

Boom content 💥

[@learnwithwhiteboard]

Glad it was helpful, buddy!