Live Day 6- Discussing KMeans,Hierarchical And DBScan Clustering Algorithms 

ek number session ... in easy terms ... BIAS is the inability of ML algorithm to capture the 100 percent or exact relationship. To understand bias one must think why do we need a ML in first place. In mathematics or physics we have absolute relationship or formula between dependent and independent variables like s=ut+1/2 at2 (std 7 Physics) or SI = P*R*T so for computing cases like we have absolute formula we don't need any ML algo. ML try to do the same i.e. estimate a formula, let say I want to calculate the purchasing power (P) so I train a model with different variables like income,age, family income and m model fetches a formula P = wo+ b1*income+b2*age + b3* family income..... So this formula is not absolute or universal as its derived by a specific ML algo for specific data but let say by miracle we derive a formula that exactly calculates the purchasing power with 100 percent accuracy so for that model bias is 0 as the model accurately captures the relationship..... Variance ---- Talking about variance, in short way the difference in fits between data set is called variance , imagine we used that same miracle formula in test data and data fits 100 percent as in we get 100 percent accuracy(for different test set) then we can say that the variance is 0 which means the ML formula is perfect or let say when use the same miracle formula in test set we get 50% accuracy which means the bias was low but variance is high as formula didnt work well with unseen (test) data... SO in an imaginary world if bias is 0 and variance is also 0 then my friend you have discovered a formula not an estimation .... In a practical world we aim for a model with low bias and low variance..... Subscribe Krish Channel if this helped

Good morning krish.. You have really made my foundation very strong before that I was null in statistic and machine learning since from non technical background.. Now I can read very high level books and could really understand.. You are really great value addition to my learning path..

Hello sir I started every morning with a new session of machine learning. And last 6 days teach me a lot about machine learning algorithms. Thank you very much for this playlist.

You are the best teacher that I have in my life in this domain,thanks a lot to share this kind of knowledge...

Excellent and knowledge gaining session and every second spend was gain. Thanks alot 😊 keeping helping and sharing the knowledge & concepts 💐💐💐

Good Evening Krish. Your contents is absolutely a gold mine. Please arrange Deep Learning sessions next :)

Thanks for this great Tutorial.

you are one of the best teachers any student can have..❤

Excellent, just excellent. Thanks

Super Explanation as always. hats off

Thank You

Amazing explanation thank you sir

A humble request to you @Krish, make next live streams on Deep Learning.

Hello @Krish, thank you for the explanations. Please do an extensive depth in EDA sessions next. I appreciate your efforts very much, thanks again.

I got placed at tiger analytics Credit goes to u krish Your videos helped me to crack the interview

Thanks

Thank you for the lecture

Thank you so much sir❤️

First thing First ! Great session 👏 👌 👍

This video is incredible, and very well explained . But if we have more than one feature in our dataset, should we make the feature selection first and then perform the elbow test?

thanks! really want know about exact definition of bias & var great teaching

Is the silhouette score applicable to hierarchical clustering? as some clusters are within other clusters. How do we differentiate a(i) from b(i) then?

Thank your sir Krish

Please cover XGboost'GBM and catboost in live videos so we can understamd learn better

Hello Krish! How it is possible to have 3 centroids when k=2 is specified as you told at 32:00 while introducing kmeans plus?

superb.....!!

Yes DEEP LEARNING NEXT!

1.75 speed is he best way to watch and lot of information covered in less time

Hello sir. Do you, by any chance, know about the assumptions of k means cluster analysis in the case of large variance?

finished watching

Beautiful sir....

You are just amazing Sir. 😊

Hello sir, you are doing great job. do you have any video related to OPTIC clustering?

In k means clustering, is there an assumption in numbers of observations and variables? Would having variables greater than observation affect the results of clustering and make it less accurate?

The video was very good. But how to calculate the feature importance after k-means clustering?

Hi krish sir its learning from you. Can you please detailed video of Principle components analysis

Please start mock interview sessions as well

Keep it up.

Krish Naik Sir is Awesome

A humble request to you @Krish,make next live session streams on Machine learning practice and practicals

Please let me know on which kind of data like linear ,non linear etc which algorithm works better

Depends on the data points

What are the type of Biases can there be in a dataset? how to answer this question ?

Hi Sir, At 1:11:00, I think you had mistakenly spoken the wrong terms for High Bias & low Bias. It should be like for High Bias-> Not perform well, Low Bias-> Perform well. We use Low Bias & low variance for the Generalized Model as it performs well. Correct me if I am wrong.

at 1:11:31 , I guess its wrong if the model captures the good relationship(between dependent and independent variable) in data then it has low bias not high bias. Low bias means that model output the formula is flexible (low bias) to capture the relationship , high bias means that the accuracy is low and model is unable to capture the actual data points .. please verify guys

Hello sir take care of your health

Hi Krish, Are you planning to take ML (Deep Learning) session?

Please make some videos on soft clustering algorithm (ex. Fuzzy C Means)

Mil gya bhai ml padhna ka channel ekdum maja aagya sir

sir can you make an urgent lecture on cluster labeling problem ?? document cluster labeling thing ? and what if we enhance this issue as hierarchical cluster labeling thing ?

@KRISHNAIK SIR, KINDLY PROVIDE THE DBSCAN VIDEO LINK

will you do deep learning series?

sir pls make video on homogeneity, completeness, V-measure and Davies-Bouldin Index

anyone knows where I can get data science/ml internships? I am in third yr of comp eng

Could someone share github link which is being referenced at 51:51?

I didnt find the githuub link sir

10/10 rating

Where is the Github link for this?

K means clustering is not mathematically clear. The line you're drawing connecting the two centroids is ok, but how does that perpendicular line drawn. means how is that perpendicular line decided? Also for any new point, will that line be used to classify for k nearest neighbours is to be used?

how to find eps and impis in dbsan

can i know the matrial link ?

10 out of 10

10/10

Sir can you please provide the github link?

Can I've the git hub link here please 😵‍💫

I don't understand after knowing the clusters we draw the histogram in hierarchical clustering and you are showing we need to draw a parallel like and the number of vertical lines it intersects will be number of clusters?? I mean we already drawing the histogram based on the clusters. Doesn't make sense what you told.

Yes Deep learning course

Sir, if low bias - high variance is overfitting and high bias - high variance is underfitting , then what is high bias - low variance ?

east or west naik sir is suppper duper best

Quick qq. High bias meaning better accuracy. ??

silhouette Code is dam tough to understand Sir 😞

51:27 k means cant do cluster like this , kmeans created convex pattern in data

Silhouette score

Andrew NG of INDIA==Krish Naik Sir

Sir pls make a video ON pea

silhoit score