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

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

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

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

Very useful video Thank you Comment from : Chun Lichess

please decrease background soundit disturbs us Comment from : Hero Mishra

Bro wideup my entire career in just 5 min 😂 Comment from : Vishal Shevale

Just the info I needed Thanks! Comment from : John Jones

Thank you it was awesome Comment from : Altern

Bro this video is "THE ONE" Comment from : batanai chimuka

Start with examples Comment from : K Kim

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 ! Comment from : Lior Leiba

learn how to speak with this accent: just say D instead of T sound Also keep your tongue as close to the roof of your mouth as possible Comment from : Max Kosh

Thanks for posting this video Your explanations make it really easy to get a high-level understanding Comment from : Fabian B

great explanation, congratulations Comment from : rodrigo mericq

Technical video , why so loud music background Terrible Comment from : MadhuKiran Vaddi

Well done Comment from : Ch Fin

I wish you the best luck! Comment from : Lucas

Content was good, but please improve your audio Random guitar music just was not it Comment from : Ruben Castaing

Al is a game changer, already revolutionised many industries, mind blowing 🥷🥷🥷 Comment from : The Tech Wave

✨✨👌👌 Comment from : dhyan chand babu

blog link is not working! Comment from : SPS Tech

Please make more videos on machine learning Comment from : Saman Khan

Classification or regression which one has ordered data and unordered data? Comment from : Saman Khan

The music is covering your voice Its annoying trying to focus on what you are saying Comment from : AQ

I just talked with the AI Chat GPT: It's amazingbrIt's like a true encyclopedia & saves up a lot of time searching If we are going to be substituted by this & latter techs, I'm good, eat me Comment from : Flipper Time

you should speak in spoken English rather than in text book English ffs Comment from : Apollo

We can use PCA( Dimensionality reduction) in Supervised learning too !!! Isn't it ? Comment from : Coding Pathshala

I quite dislike that you say it's all models - this is terribly misleading Also, the Naive Bayes you described was not the Naive version Comment from : Vaclav Remes

Great stuff Thanks for the overview video Background music is much too loud tho Comment from : Marc Fruchtman

In one sense, AI is like a cartoon in that a series of stationary cells appear to be moving AI iterations happen so quickly, they appear "natural" AI would not be possible without extreme computer power Comment from : Rich Bro

#Dev’s SAP MM/WM Coaching Comment from : Devdutt Rath

5 mins is short 15mins will be good Comment from : Derek L

mtlb mere engineering in data science ke 4 saal joke the XD Comment from : Gitansh Saharan

Well done Thank you🙏 Comment from : 冠霖0

The explanation is simple and easy to understand Thankyou Comment from : Njeri Gitome

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 Comment from : Doug Olson

Very helpful Thank you for making this! Comment from : Thomas Bates

Cool 👍 Comment from : ksi2ilmi

Excellent job 👏 brVery helpful to see overall view of all the models Comment from : Uday

Ty, this helped me alot in my report! Comment from : juicy burgi

PLEASE!!!!!!!!!!!!!!! I give you 6 minutes but please, speak slower! and leave the music away THANKS!!!!!!!!!!!!! Comment from : Rolling Stone

That random forest explanation went way over my head Comment from : George

I have a question, regarding wanting to go into the industry to apply my knowledge of statistics it is possible to translate my knowledge into a job coding machine learning methods? here is my background: I am a neuroethologist with a large background (+15 years) in all of these (statistical) methods EXCEPT that I use them to study behavioural (animal communication, acoustics, locomotion, associative learning) and neuro-cognitive (drug/neuromodulators effects on behaviour; electrophysiology) and even ecological-hydrological datanow at the end of my PhD in neuroscience and after 4 publications in peer-reviewed scientific journals I am thinking in using my skills into the industryand finding a real job I also know some coding with R and Phyton I would appreciate any guidance Comment from : Os

Found short and sweet video 🥰 Comment from : vidya Sankpal

Are these models also referred as techniques? Comment from : Mahathir Islam

Please do not put background music on your videos It is very enjoying otherwise well done! Comment from : Milad Hafezi

thank you it's really good explaining Comment from : Gh saoussen

we don't need the music to like your videobrThe music is disturbing Comment from : karen

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 Comment from : Katie Callaghan

The summarising ML model is really good It is very helpfulThank you sir Comment from : Suhasini Ambare

Wow! Comment from : Uday Radhe

youtube/7JT92Ly9Llsbr#NurserytoVarsity Comment from : Dr Sunil Kumar Jangir

i wish there was no distracting music is the background, the video is good indeed Comment from : kineticx

Thank you this saved my life Comment from : Kexin Guan

Bundle of thanks for this Comment from : Think Write

Wow, this is so so good Comment from : Joe

hellobrwhat about predictive models for soccer betting?brDo you think that this can be done? Comment from : free spirit

great high-level framework for how to bucket different ML methods Comment from : Jonathan Mardini

The video is uploaded 2 years back and it only explained supervised learning Please help me to find the exact next video after this Comment from : Shreya Komal

Tum merey researchers karwavo jo duniya ka nature pata chal jayega ki kitna naturally universe ki efficiency hain Comment from : Anil kumar Sharma

I don't get it In Regression, sections iII DT/i and iIII Random Forests,/i the figures demonstrates that the outputs generated are clearly discrete, not continuous The concept of sorting algorithms into the categories of Regression and Classification fails Comment from : Rursus

sir, what is the different between ML and data mining? Comment from : CKeong

reduce background noise Comment from : Puneet Hardaha

You can keep this a lot simpler Comment from : Uday Shivamurthy

Sir advance legal v explain karte to Acha rgega Comment from : abid iqbal

Music is too distracting Comment from : Open University

Good overview Thank you for sharing Comment from : H R

this video is very helpful for interview purposes Comment from : @radio_tingles

Great intro for the beginners Comment from : Jake

Thank you for your video Comment from : V D

Amazing content, straight to the point whilst still being detailed Comment from : Tania Afroz

He has expainened all branches of machine learning that is good for biginers like me To understand ml Comment from : anand narvane

Even with subtitles on this is gibberish Comment from : Aaron Jennings

Very basic and unhelpful to build anything Comment from : Mahmoud Mahdy

Best video on this topic Comment from : Rahul Upadhyay

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 Comment from : Pavel Pospisil

Nice song Comment from : SAMIHOUSSEMEDDINE BABOUCHE

nice video, straightforward and comprehensive Comment from : WL Chao

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 ☺️ Comment from : Luis Castellanos

Awesome!!! Truly Outstanding content, Thank you Comment from : Neeraj Shrivastava

Why indian accent use too much tongue?!!! It's so annoying Comment from : BlackThreader

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, 05) 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 06) and sometimes the outcome is more important (for example, making a decision to approve an application for credit) Comment from : Christopher Wright

Great video But I would include reinforcement learning on the same level as supervised and unsupervised It's a whole different thing And you are also missing recommendation systems btw Comment from : Parlez-vous IA ?

Amazing content, straight to the point whilst still being detailed Comment from : edward alabi

this is really good, thank you Comment from : John Law

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

Great video, but crucially missing RNN Comment from : ArgumentumAdHominem

okay but you have nothing we can interact with you, like an app or somwthing so we can do this Comment from : freitas209

Thank you u made it simple to understand Comment from : Vignesh Mamidi

Good explanation How about NLP? Comment from : Jeremy Heng

Thanks for making it so concise ! Top Comment from : MrFischvogel

sir which software you use to make video Comment from : Rahul Bediya

hi, well i tested , forecast exact number doesnt exist , its a very big differents Comment from : Philippe I

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

Very good overview Comment from : travel

00:01 - categorias do ML feito, modos os quais o robo aprendeu, nomes titulos rotulos de categoria- Comment from : Requiem and τέχνη Foley

Reinforced learning? Comment from : ritesh bhatt

this video save my life Comment from : LI-PING HO

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 Comment from : Max Turgeon

Good to know list of ML algos but not so helpful in clearly explaining each of those in easy to understand language Comment from : Sameer K

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