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:
Linear regression : • When To Use Regression...
Logistic Regression : • Understanding Basics o...
Ensemble models : • Introduction to Ensemb...
SVM : • Support vector machine...
Kmeans : • K Means Clustering in ...
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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
About Unfold Data science: This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well.
Book recommendation for Data Science:
Category 1 - Must Read For Every Data Scientist:
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
Category 5 - Programming:
The Pragmatic Programmer by David Thomas - amzn.to/2WqWXVj
Clean Code by Robert C. Martin - amzn.to/3oYOdlt
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Watch Introduction to Data Science full playlist here : • Data Science In 15 Min...
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[@naveenarunkumar95]

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

[@tadhailu]

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

[@RakiatHaruna-cx8jh]

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

[@swethamadhavarapu3018]

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

[@katamreddiprasad418]

Very informative. Thank u...

[@kamaleshwaran06]

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

[@UnfoldDataScience]

Glad it was helpful!

[@muditmathur465]

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

[@UnfoldDataScience]

That is the purpose of this video,🙂

[@dheenadhayalan423]

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

[@UnfoldDataScience]

Thanks a lot

[@pritus9537]

Very simple and effective method of teaching all algorithms

[@Er.Sunil.Pedgaonkar]

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

[@satyagarapati3611]

Great Aman!!
Wonderful explanation ❤

[@explorewithskp1237]

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

[@UnfoldDataScience]

Most welcome 😊

[@sukumarane2302]

Good presentation . Thanks 👍

[@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.

[@surajitchanda1975]

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

[@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

[@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

[@dvabhinav31]

Helped with understanding logistic regression!

[@lakshmanthota8902]

Very handy for a quick recall

[@ajaykumargupta5862]

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

[@UnfoldDataScience]

Thank you, I will

[@leonine291]

زبردست ❤

[@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.

[@vamsivegi]

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

[@UnfoldDataScience]

Thanks Vamsi.

[@sachinmore8938]

Great informative video. Thank you for sharing your knowledge.

[@UnfoldDataScience]

Glad it was helpful!

[@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

[@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

[@NithishKumar-ng7dp]

Good Explanation Sir

[@user-lq3op3rd2e]

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

[@UnfoldDataScience]

Welcome.

[@chandrasekhar_m]

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

[@UnfoldDataScience]

Thanks Chandra.

[@gunanidhibg1273]

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!

[@anishvarughese9517]

Explained well

[@sircosm]

Nicely explained! Very helpful.

[@UnfoldDataScience]

Thanks for watching. Keep learning

[@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.

[@dd1278]

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

[@UnfoldDataScience]

Thanks Deb.

[@ishanagpurkar]

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

[@UnfoldDataScience]

Thanks Isha. Please share with friends as well

[@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

[@hinduputra9623]

Very Informative video, thank you

[@UnfoldDataScience]

Thanks a lot.

[@pradeeppaladi2852]

It was indeed a great session, thanks

[@UnfoldDataScience]

Thank you Pradeep. Pls share with friends.

[@pradeeppaladi2852]

@@UnfoldDataScience Already did

[@DataScienceWallah]

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

[@UnfoldDataScience]

Thanks again, please share with friends as well.

[@bushraaijaz2125]

Helpful tutorial (y)

[@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.

[@pawankumarjm]

This is the best explanation till I saw..😊

[@UnfoldDataScience]

Thanks Pawan. Please share with friends as well :)

[@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

[@SuperMoorthy]

Good, i am first time watching, very understandable.

[@Marklodhi420]

Exceptional stuff.

[@UnfoldDataScience]

Thank you. Pls share with friends as well.

[@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 :)

[@kanoriopurity4374]

very well detailed great content

[@UnfoldDataScience]

Much appreciated! your comments motivate me.

[@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

[@mahendrachauhan63]

Nice, super Duper, you are awesome boss

[@UnfoldDataScience]

Thanks Mahendra

[@masoudmirzakhanloo8059]

awesome 👌

[@Learn_IT_with_Azizul]

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

[@UnfoldDataScience]

Sure , many thanks for appreciation and suggestion.

[@karanalang1573]

thanks for this very helpful video !

[@UnfoldDataScience]

Glad it was helpful!

[@satishb9975]

Thank you 🎉❤ excellent 👍

[@UnfoldDataScience]

Welcome 😊

[@Jordi5720]

super useful

[@spicytuna08]

wow. awesome summary,

[@UnfoldDataScience]

Glad you liked it!

[@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

[@kishorrawat8083]

Great lecture.... 👌👍

[@UnfoldDataScience]

Thanks a lot

[@happylearning-gp]

Excellent, Thank you very much

[@UnfoldDataScience]

Thank you

[@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

[@kennethstephani692]

Great video!!

[@UnfoldDataScience]

Thanks for the visit

[@Dropella]

this is best I have seen ever

[@UnfoldDataScience]

Thanks deelip. Pls share with friends.

[@talentzunlimited1398]

Very helpful !

[@UnfoldDataScience]

Glad it was helpful!

[@khanyijiyane1585]

Really big thank you❤

[@UnfoldDataScience]

You're welcome 😊

[@ganeshsubramanian6217]

Very good explanation Aman🎉

[@UnfoldDataScience]

My pleasure

[@rajpandim]

Excellent explanation

[@UnfoldDataScience]

Glad it was helpful!

[@omkarbelpatre5]

Thank you so much sir

[@UnfoldDataScience]

Most welcome

[@smegala3815]

Thank you sir

[@UnfoldDataScience]

Welcome

[@Griffindor21]

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

[@UnfoldDataScience]

Yes it can. Thank you

[@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

[@bushraaijaz2125]

Seven ML Classifiers with python using colab: czcams.com/video/1c8Pi0rh-oQ/video.html

[@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

[@storiesshubham4145]

Liked it even before watching

[@UnfoldDataScience]

Thanks Shubham.

[@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.

[@thelife1919]

Thank you

[@UnfoldDataScience]

You are welcome

[@pramethicine9328]

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

[@UnfoldDataScience]

Cheers, good luck

[@sanjaybt1475]

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

[@UnfoldDataScience]

Sure. please check this video meanwhile
czcams.com/video/-uC79UTOye8/video.html

[@robotdream8355]

Really its amazing. Do you have any udemy course?

[@UnfoldDataScience]

Thanks Robert, please check here www.unfolddatascience.com

[@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 :)

[@atulkumarjoshi4773]

nice one

[@UnfoldDataScience]

Thanks Atul.

[@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😀

[@NurserytoVarsity]

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

[@UnfoldDataScience]

So nice of you. Please share with friends.

[@naineshkhanjire]

what is beta in logistic regression
?

[@UnfoldDataScience]

coefiicients

[@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

[@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.

[@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.

[@houshym]

Do you have PPT slide?

[@UnfoldDataScience]

Thanks a lot.

[@joachimguth6226]

Are 9 and 10 not classification problems as well?

[@UnfoldDataScience]

we can debate

[@surajitchanda1975]

hi good morning

[@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

[@ashwinipatil1231]

I didint heard ABT ada boost algorithm in ML

[@UnfoldDataScience]

:)

[@aidanthompson5053]

7:59

[@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

[@ani1nonly]

Terms I stated knows by only professionals already knows about what u mame

[@UnfoldDataScience]

Thanks for watching.

[@shushmakothapalli9657]

Try to tell in this code also

[@UnfoldDataScience]

You will find all codes in machine learning playlist.

[@jenny7533]

Base prediction here 80,how came,??

[@UnfoldDataScience]

Which part?