aman sir, please make a video only containing the different topics of different subjects to be prepared to become a machine learning engineer and data scientist, include every topic from a to z of different subjects like mathematics, programming, database language to learn, etc etc and also different framework and different packages etc... i mean please include everything... that will be very helpful to everyone, thank you sir
Sir please explain in detail the math behind algorithms because that is the most crucial in understanding than the python implementation. And it is mentioned in sklearn documentation for LDA that it used Bayes probablitic rule, which yiu have not mentioned. Sir i request you to create videos with full information.
Thank you so much Aman for another insightful video. All your videos are really good. I have seen almost all your videos. All the concepts get clear when I learn from your channel. Thanks for your great help . And In this series can you please make videos on SVD and TSNE as well.. Though I search from channels you give very clear explanation. Will be waiting for more videos
Shape of the constraint in lasso is diamond, when the optimization touches corner, it becomes zero. Please read little about it, it's not difficult to understand.
LDA (Linear Discriminant Analysis) and LDA (Latent Dirichlet Allocation) are two different techniques used in different areas of data analysis. LDA (Linear Discriminant Analysis) is a supervised learning technique used for dimensionality reduction and classification. It aims to find a linear combination of features that maximizes the separation between classes in a dataset. In other words, it tries to project the data onto a lower-dimensional space such that the classes are well-separated. LDA is commonly used in pattern recognition, machine learning, and data analysis. LDA (Latent Dirichlet Allocation), on the other hand, is an unsupervised learning technique used in natural language processing and text mining. It is a probabilistic model that is used to discover the latent topics that underlie a collection of documents. LDA assumes that each document is a mixture of several topics, and each topic is a distribution over words. The goal of LDA is to learn the topic distribution for each document and the word distribution for each topic. So, while both techniques are referred to as "LDA," they are used for different purposes in different areas of data analysis.
@@UnfoldDataScience 3:58 decision boundary is wrong or the data symbols used are incorrect. 7:35 purpose is laughable. 13:13 I dont see any separation boundary. You dont talk about how it works with data with more than 2 variables / the general cost function. Maximizing the cost function is just to differentiate. Anyone can import sklearn and use it. Nothing useful there as well. Hope you know it is used in classification as well not just dimension reduction, but you never even mention it in the explanation part. But you show the classification example in your code. How is this vid useful to anyone?
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aman sir, please make a video only containing the different topics of different subjects to be prepared to become a machine learning engineer and data scientist,
include every topic from a to z of different subjects like mathematics, programming, database language to learn, etc etc
and also different framework and different packages etc...
i mean please include everything...
that will be very helpful to everyone,
thank you sir
I think another difference between PCA and LDA is,- PCA is unsupervised ML technique whereas LDA is supervised.
PCA use for both supervised , unsupervised
By ignoring target feature
You are brilliant, man! Great teacher, explainer.
Sir please explain in detail the math behind algorithms because that is the most crucial in understanding than the python implementation.
And it is mentioned in sklearn documentation for LDA that it used Bayes probablitic rule, which yiu have not mentioned. Sir i request you to create videos with full information.
Thank you so much Aman for another insightful video. All your videos are really good. I have seen almost all your videos. All the concepts get clear when I learn from your channel. Thanks for your great help . And In this series can you please make videos on SVD and TSNE as well.. Though I search from channels you give very clear explanation. Will be waiting for more videos
Thanks Anu. Sure.
Great video Aman 💯💯
Thanks a lot.
Excellent explanation!
Very good Aman
I learned alot from your videos,it really help in aiml course
Great video. Can we have a similar one on t-SNE?
Your simplicity appreciated
Thanks Sachin.
thank you bro , you are a Great teacher , so please make a video on SVM
Thank you so much sir , all videos are very informative.🙏🙏
How to identify which feature extraction method we going to apply?
I mean when we see a dataset?
Thankyou so much sir 😇🙏
Welcome Hariom.
What great trainings you have!!! I hope you have a tutorial video on Genetic Algorithm.
Great suggestion! Thank you
good video.
Thank you Aman 😊
I have given interview with one of product based company.. they asked why lasso regression peanlize to zero..? It would be great if you explain this..
Shape of the constraint in lasso is diamond, when the optimization touches corner, it becomes zero. Please read little about it, it's not difficult to understand.
Very good explanation. Could you make a video on differences between LDA VS QDA?
Yes, sure. Thanks for suggesting
Aman would you explain what is Eigen Value Decomposition ,Eigen Space and Eigen Face in Detail 🙏🙏
Ok let me take that as one topic in list to come.
That means how kernel trick is different from LDA sir
Data is projected on new axis in LDA.
Kernel Trick Use is SVM with different types of kernels like rbf ,linear etc
In SVM we don't reduce dimensionality
You didn't say one Difference that PCA is Unsupervised learning and LDA is Supervised Learning
Thanks for suggesting. Yes may be i missed. I love these when you interact through comments. Keep rocking
How to decide if we should use lda, pca or svd?
Cant decide in advance based on what is the objective and how the data distribution is we need to take call
i am really confused! what is difference between LDA (Linear discriminant analysis) and LDA (latent dirichlet allocation)?
LDA (Linear Discriminant Analysis) and LDA (Latent Dirichlet Allocation) are two different techniques used in different areas of data analysis.
LDA (Linear Discriminant Analysis) is a supervised learning technique used for dimensionality reduction and classification. It aims to find a linear combination of features that maximizes the separation between classes in a dataset. In other words, it tries to project the data onto a lower-dimensional space such that the classes are well-separated. LDA is commonly used in pattern recognition, machine learning, and data analysis.
LDA (Latent Dirichlet Allocation), on the other hand, is an unsupervised learning technique used in natural language processing and text mining. It is a probabilistic model that is used to discover the latent topics that underlie a collection of documents. LDA assumes that each document is a mixture of several topics, and each topic is a distribution over words. The goal of LDA is to learn the topic distribution for each document and the word distribution for each topic.
So, while both techniques are referred to as "LDA," they are used for different purposes in different areas of data analysis.
@@muhammadzubair440 Thanks a lot for the explanation!
Plz explain tsne
Moore Ronald Thompson Thomas Williams Barbara
Please say in hindi.
Very poor explanation.
Thanks for your feedback. Most people found it useful though
@@UnfoldDataScience 3:58 decision boundary is wrong or the data symbols used are incorrect. 7:35 purpose is laughable. 13:13 I dont see any separation boundary. You dont talk about how it works with data with more than 2 variables / the general cost function. Maximizing the cost function is just to differentiate. Anyone can import sklearn and use it. Nothing useful there as well. Hope you know it is used in classification as well not just dimension reduction, but you never even mention it in the explanation part. But you show the classification example in your code. How is this vid useful to anyone?
Clark Carol Lewis Amy Taylor Lisa
Very nice Aman