10 ML algorithms in 45 minutes | machine learning algorithms for data science | machine learning

10 ML algorithms in 45 minutes | machine learning algorithms for data science | machine learning
#machinelearning #datascience
Hello ,
My name is Aman and I am a Data Scientist.
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Please find link for all algorithms in detail:
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Logistic Regression : • Understanding Basics o...
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Topics for the video:
10 ML algorithms in 45 minutes
machine learning algorithms for data science
machine learning algorithm interview question and answers
machine learning algorithm in hindi
machine learning algorithm mathematics
machine learning all topics
machine learning algorithm telugu
machine learning algorithm projects
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Book recommendation for Data Science:
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The Elements of Statistical Learning by Trevor Hastie - amzn.to/37wMo9H
Python Data Science Handbook - amzn.to/31UCScm
Business Statistics By Ken Black - amzn.to/2LObAA5
Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow by Aurelien Geron - amzn.to/3gV8sO9
Ctaegory 2 - Overall Data Science:
The Art of Data Science By Roger D. Peng - amzn.to/2KD75aD
Predictive Analytics By By Eric Siegel - amzn.to/3nsQftV
Data Science for Business By Foster Provost - amzn.to/3ajN8QZ
Category 3 - Statistics and Mathematics:
Naked Statistics By Charles Wheelan - amzn.to/3gXLdmp
Practical Statistics for Data Scientist By Peter Bruce - amzn.to/37wL9Y5
Category 4 - Machine Learning:
Introduction to machine learning by Andreas C Muller - amzn.to/3oZ3X7T
The Hundred Page Machine Learning Book by Andriy Burkov - amzn.to/3pdqCxJ
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[@hgowda11]

This is an excellent and time-efficient video with a great explanation.

[@naveenarunkumar95]

So Easy to Understand all the concepts of ML Thank you for this

[@RakiatHaruna-cx8jh]

Thank you for the beautiful presentation. Could you please give an example using spatial data.

[@katamreddiprasad418]

Very informative. Thank u...

[@muditmathur465]

Thanks, this came really handy 1 day before interview 😁👍

[@UnfoldDataScience]

That is the purpose of this video,🙂

[@tadhailu]

Very important, I need to watch it again and again.

[@swethamadhavarapu3018]

Great video, simple easy to understand explanation for beginners. Thank you!

[@VinayAggarwal]

This is super helpful. Thanks for putting this together. ❤
Can these all work on more then 2D data ?

[@UnfoldDataScience]

Yes Vinay. Thanks

[@atiqrehman8435]

True that

[@dheenadhayalan423]

All the prerequisites I was hoping for was covered and explained clearly. Thank You sir !

[@UnfoldDataScience]

Thanks a lot

[@Srinivascheekati681]

Hi ,This Ch Srinivas ( EX Faculty in ACE academy and currently working in MADE EASY IES, I would appreciate your teaching process . Thanks for sharing your knowledge. GOD bless you. I am planning to do PhD in Data Science please give me your valuable suggestions. Thanks

[@UnfoldDataScience]

Thanks Srini, welcome to channel

[@jayalekshmik3706]

Yes I too would like to know what entails in a ML path

[@buttterflygirl]

Need your help understanding a scenario where the OA and kappa coefficient are more or less similar on test and validation datasets when using only one independent variable. Here, the validation dataset meaning completely a new dataset in time and space. Train and Test belong to same time and space. Can you explain to me why this is? I appreciate your help on this. When run with a few more variables, this issue is not showing up.
For more understanding, Train and Test are from same day satellite image for city A. Validation dataset is from different day satellite image for City B.

[@satyagarapati3611]

Great Aman!!
Wonderful explanation ❤

[@kamaleshwaran06]

Best Video for a quick introduction/refresher on ML Algorithms. Kudos!

[@UnfoldDataScience]

Glad it was helpful!

[@sukumarane2302]

Good presentation . Thanks 👍

[@explorewithskp1237]

Thank U Sir . Clearly got an idea on all algorithms in very short time ☺️

[@UnfoldDataScience]

Most welcome 😊

[@VikasVerma-xf6hb]

Thank you. Very nicely explained. Kudos to you. Keep-up the good work.

[@UnfoldDataScience]

Thanks Vikas. Apne friends group me bhi share kar dijie.

[@sircosm]

Nicely explained! Very helpful.

[@UnfoldDataScience]

Thanks for watching. Keep learning

[@surajitchanda1975]

A very good lecture to refresh my knowledge my name is Surajit Chanda i am an instrumentation engineer and also a Software Engineer

[@SethiGaneswar]

Great presentation and i think this is one of the best videos on simply making understandable to the concepts. thanks for the video

[@UnfoldDataScience]

Glad it was helpful!

[@NithishKumar-ng7dp]

Good Explanation Sir

[@Latif127]

It looked good to me, thank you.

[@sureshkumar-cn5jr]

Useful content Aman!
Thanks for your efforts to teach complicated but important concepts in M L

[@user-lq3op3rd2e]

very pretty and clear explanation .stay tuned and thanks very much buddy

[@UnfoldDataScience]

Welcome.

[@pritus9537]

Very simple and effective method of teaching all algorithms

[@hinduputra9623]

Very Informative video, thank you

[@UnfoldDataScience]

Thanks a lot.

[@anishvarughese9517]

Explained well

[@ajaykumargupta5862]

Great session and well explained. Thank you sir. Please create more videos to explore more.

[@UnfoldDataScience]

Thank you, I will

[@DataScienceWallah]

That's very well explained highly appreciate the content ❤❤❤

[@UnfoldDataScience]

Thanks again, please share with friends as well.

[@sachinmore8938]

Great informative video. Thank you for sharing your knowledge.

[@UnfoldDataScience]

Glad it was helpful!

[@chandrasekhar_m]

best video for quick revision !! tq ..Aman '

[@UnfoldDataScience]

Thanks Chandra.

[@dvabhinav31]

Helped with understanding logistic regression!

[@ishanagpurkar]

This is a very good video for revision of ml models.

[@UnfoldDataScience]

Thanks Isha. Please share with friends as well

[@pawankumarjm]

This is the best explanation till I saw..😊

[@UnfoldDataScience]

Thanks Pawan. Please share with friends as well :)

[@bushraaijaz2125]

Helpful tutorial (y)

[@VISHNUPRASADSAKHAMURI]

Hi
This video is very informative. thanks you so much..
Can you suggest which algorithm is best suited for below use case
"scan the kuberbetes pods for application exceptions and feed the algorithm.. let the model store this info along with impact assessment, to raise the alerts only for critical exception"

[@UnfoldDataScience]

Thanks For watching.yoy can research on isolation forest or random cut forest

[@Marklodhi420]

Exceptional stuff.

[@UnfoldDataScience]

Thank you. Pls share with friends as well.

[@lakshmanthota8902]

Very handy for a quick recall

[@mahendrachauhan63]

Nice, super Duper, you are awesome boss

[@UnfoldDataScience]

Thanks Mahendra

[@vasutke1187]

Sir, Ultimate Teaching Style, Sequence of arranging Topics are highly help full to us. Great

[@UnfoldDataScience]

Thanks a lot. Please share with friends also.

[@shivagupta2052]

Great session . Can you sir make a video regarding project where you apply all ml algorithm and also do model development and same for deep learning

[@UnfoldDataScience]

Noted

[@pradeeppaladi2852]

It was indeed a great session, thanks

[@UnfoldDataScience]

Thank you Pradeep. Pls share with friends.

[@pradeeppaladi2852]

@@UnfoldDataScience Already did

[@Kiranmai30]

Thank u so much brother
I am new subscriber of u r channel
After seeing ur videos, i thought that i got some support in Learning of ML
Ur videos are in very simple English
Thank you brother

[@UnfoldDataScience]

Thanks a lot.

[@Er.Sunil.Pedgaonkar]

Good -- Er. Sunil Pedgaonkar, Consulting Engineer (IT)

[@leonine291]

زبردست ❤

[@karanalang1573]

thanks for this very helpful video !

[@UnfoldDataScience]

Glad it was helpful!

[@kennethstephani692]

Great video!!

[@UnfoldDataScience]

Thanks for the visit

[@kanoriopurity4374]

very well detailed great content

[@UnfoldDataScience]

Much appreciated! your comments motivate me.

[@dd1278]

Thanks for this..quite a critical video for everyone who's having interview (s) lined up.

[@UnfoldDataScience]

Thanks Deb.

[@spicytuna08]

wow. awesome summary,

[@UnfoldDataScience]

Glad you liked it!

[@SuperMoorthy]

Good, i am first time watching, very understandable.

[@satishb9975]

Thank you 🎉❤ excellent 👍

[@UnfoldDataScience]

Welcome 😊

[@masoudmirzakhanloo8059]

awesome 👌

[@happylearning-gp]

Excellent, Thank you very much

[@UnfoldDataScience]

Thank you

[@vamsivegi]

Wish this kind of tutorial 5 years ago. But it’s not too late. Simply one the best.

[@UnfoldDataScience]

Thanks Vamsi.

[@user-vo9kh4zd3f]

this is very helpful video those who want to gain basic knowledge in ML algos
but uh did a mistake in Gradient boost calculation in 23:44 .
once check it

[@UnfoldDataScience]

Thanks a lot for watching and feedback.

[@achunaryan3418]

It's 122

[@Learn_IT_with_Azizul]

Great. please keep up with e-commerce projects in ML practices. Ty

[@UnfoldDataScience]

Sure , many thanks for appreciation and suggestion.

[@kishorrawat8083]

Great lecture.... 👌👍

[@UnfoldDataScience]

Thanks a lot

[@Jordi5720]

super useful

[@talentzunlimited1398]

Very helpful !

[@UnfoldDataScience]

Glad it was helpful!

[@khanyijiyane1585]

Really big thank you❤

[@UnfoldDataScience]

You're welcome 😊

[@rafibasha4145]

Thanks for the video ,pls cover Naive bayes ,XGboost catboost dbscan hierarchical clustering in one hour video and all stats in 2 to 3 videos also dl nlp imp concepts in 1 hour length video s

[@UnfoldDataScience]

Noted

[@rafibasha4145]

@@UnfoldDataScience thanks

[@user-oo4ml5rn7y]

Very good Video. As a beginner i understood the basics well. Definitely will recommend to my students. Thankyou for the effort you put into the Presentation.

[@UnfoldDataScience]

So nice of you. Please share with friends as well. Welcome to Unfold data science family :)

[@rajpandim]

Excellent explanation

[@UnfoldDataScience]

Glad it was helpful!

[@omkarbelpatre5]

Thank you so much sir

[@UnfoldDataScience]

Most welcome

[@Dropella]

this is best I have seen ever

[@UnfoldDataScience]

Thanks deelip. Pls share with friends.

[@ganeshsubramanian6217]

Very good explanation Aman🎉

[@UnfoldDataScience]

My pleasure

[@smegala3815]

Thank you sir

[@UnfoldDataScience]

Welcome

[@Griffindor21]

Great video!
Decision Tree can also do classification as well, right?

[@UnfoldDataScience]

Yes it can. Thank you

[@user-jh4wo6ok4s]

Thanks a lot for this. Very helpful! I was a bit lost at a few points such as Ada Boost & Log Regression. But that's efficient for a starter. 👍👍👍

[@UnfoldDataScience]

Thanks for watching

[@bushraaijaz2125]

Seven ML Classifiers with python using colab: ruclips.net/video/1c8Pi0rh-oQ/видео.html

[@storiesshubham4145]

Liked it even before watching

[@UnfoldDataScience]

Thanks Shubham.

[@itsme1674]

Machine learning is nothing but learning pattern from a data using an algorithm. An algorithm is set of steps that are executed in an order to reach final solution.

[@UnfoldDataScience]

Yes, nicely said

[@13soulmate13]

​@@UnfoldDataSciencebrother resume shortlist hi nhi hora what can i do i am fresher

[@UnfoldDataScience]

put good projects and keywords based on JD

[@geethakiran8122]

R u data scientist

[@comandozz123]

​@@13soulmate13I went w

[@lifeisbeautiful1111]

please explain base model in adaBoost . It sounds similar to M1 model. is it different from M1 model. if it is so, what is the difference. Kindly explain. But great explanation.Keep up the good work sir. God bless

[@UnfoldDataScience]

Sure thank you

[@raghuyadav1406]

Hi, do you have implementation examples for all these, i think decision tree, random forest available but others not, also you cover support vector, k nearest etc..

[@UnfoldDataScience]

Definitely, thanks for suggesting, will do.

[@ratheeshmsuresh7368]

Brother, Please help to get clarity for the Below Questions,
First Question :
check whether The average monthly hours of a employee having 2 years experience is 167.
What will be the Null and Alternative Hypothesis that I should Consider?

[@UnfoldDataScience]

Can be framed in multiple ways

[@uditi_]

null can be “…it is 167” and alternative can be it is not, then you can prove or disprove null hypothesis

[@thelife1919]

Thank you

[@UnfoldDataScience]

You are welcome

[@robotdream8355]

Really its amazing. Do you have any udemy course?

[@UnfoldDataScience]

Thanks Robert, please check here www.unfolddatascience.com

[@pramethicine9328]

Amazing video will let you know if I pass the interview 😂🙏🏼

[@UnfoldDataScience]

Cheers, good luck

[@atulkumarjoshi4773]

nice one

[@UnfoldDataScience]

Thanks Atul.

[@sanjaybt1475]

Can u make the videos regarding outliers and scaling, missing values affects on the different algorithms.

[@UnfoldDataScience]

Sure. please check this video meanwhile
ruclips.net/video/-uC79UTOye8/видео.html

[@NurserytoVarsity]

You're making education engaging and accessible for everyone. #NurserytoVarsity

[@UnfoldDataScience]

So nice of you. Please share with friends.

[@pankajjoshi8292]

Thank you sir , cannu pls tell how to implement these in python

[@UnfoldDataScience]

HI Pankaj, if you go to playlist section, you will find all the implementation as part of different playlists :)

[@TeluguAbbai802]

do you have full video links for Machine Learning

[@UnfoldDataScience]

Yes - please go to playlist and you will find separate playlist for all areas of ML

[@CodeWonders_]

At Starting you said wrong because random Forest and decision tree can be used for both

[@UnfoldDataScience]

Not sure which part of the video I said it. Both can be used for classification and regression scenarios.

[@joachimguth6226]

Are 9 and 10 not classification problems as well?

[@UnfoldDataScience]

we can debate

[@azhrhasan]

Decision tree seems like a moving average. How is it different from moving average?

[@UnfoldDataScience]

Decision tree is not moving average, it's about finding best split.

[@houshym]

Do you have PPT slide?

[@UnfoldDataScience]

Thanks a lot.

[@naineshkhanjire]

what is beta in logistic regression
?

[@UnfoldDataScience]

coefiicients

[@AshisRaj]

Aman bhaiya I am too from CEB bhubaneswar. I hope you remember

[@UnfoldDataScience]

Hi Ashis, good that you messaged, yes I do. Please mail me at [e-mail]

[@AshisRaj]

@@UnfoldDataScience bhaiya "please" KAHE bol rahe hai. Acha lga apka growth dekh kar😀

[@omkarbelpatre5]

Sir eatna Ml sufficient he kya data science ke liy sir

[@UnfoldDataScience]

No, this is just for quick revision. please see description links to go into complete knowledge

[@omkarbelpatre5]

@@UnfoldDataScience ok thank you so much

[@AsifAli-ro2vo]

Can you suggest some Hindi data science and machine learning channel

[@UnfoldDataScience]

www.unfolddatascience.com
hindi courrse available

[@surajitchanda1975]

hi good morning

[@aidanthompson5053]

7:59

[@ashwinipatil1231]

I didint heard ABT ada boost algorithm in ML

[@UnfoldDataScience]

:)

[@remrem6681]

Tomorrow I hav interview, so I m here

[@UnfoldDataScience]

Wish you all the best

[@user-rc2uc1kv6w]

bagging boosting kis mein hota hai? kya hota hai?

[@UnfoldDataScience]

Ensemble learning

[@I_Anupam_Pandey]

can you please share the notes in the description of this video, hit like if you guys also want notes

[@UnfoldDataScience]

I can save notes and share if many people want it.

[@ani1nonly]

In your vid u explaining what is ML But u r using terms which no body know like regression/classification/usv

[@UnfoldDataScience]

Thanks for feedback Manoj. will take care

[@mdfaizanshakeel5591]

ML: L1st

[@UnfoldDataScience]

:)

[@vivekanandpandey4114]

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Hare Rama Hare Rama Rama Rama Hare Hare ❤❤
Raadhe Raadhe ❤❤
Jai Shree Ram ❤❤❤