This is one of the best clear example setting videos with step-by-step architecture than that of any "Edtech platforms". Yes, this is what we want as an explanation. It's that simple rather than something more to it. I am surely subscribing to your channel for more explanations in near future. "Beautifully broken down and explained"
This is exactly what I was looking for. I learned how to build a binary classifier (e.g. ham, spam), but needed to learn how to build a model that would predict an outcome for more than two categories. Thank you for the tutorial!
You have a got a very detailed explanation of concept. This is a very big asset for my project implementation. Thank you so much and please make more videos with more machine learning algorithms.
Hi , very nicely explained. Basically I need to understand what will be the output of this model like if I want to return the text as result of text analysis so how can we display the output in excel sheet again in further classification like neutral, positive,negative ..thanks
hi , i would like to ask you something. what techniques should i use to find some keyword in my csv file and then if match with the keyword, i want to assign it to another keyword. the output something like this, column A Keyword DUMPBLT:TESTING FAILED: OPERATOR PUSHED STOP BUTTON SYSTEM FAILED if i found keyword of 'DUMPBLT' and 'PUSHED STOP BUTTON' in column A, i want to assign it to "SYSTEM FAILED" and put to other column. can you help me about this ?
The concepts are very well explained. But the classifier isn't able to distinguish between alt.atheism and soc.religion.christian. How would you fine tune the model?
Out of all the categories, we restricted ourselves to 4 categories while importing data. [By passing categories list while importing] Now news_train["target"] shows the same 4 categories. Note: "target_names" is a key in the dictionary of news_train containing only our 4 categories. Hope this helps.
Yeah, I will try to, Just for a reference, you can use word2vec skip gram model or continuous bag of words for predicting next word... Stay Tuned.. #CodeWrestling
when I run the accuracy at 20:07 is get the following error: ValueError: Found input variables with inconsistent numbers of samples: [1502, 2] do you know how to solve this?
i'm using python django and my datasets were stored in django db how can I use that dataset as the training datasets aside from creating a folder put the documents there
I want to label Quora question of dataset around 30k rows in CSV . How would you test this train model of 20 newsgroup on such a dataset . Much needed help / any vedio on this will be very helpful .
can you make a video on one example where we can learn how to use text classification multivariate problem...like i have 10 features with categorical output. out of 10 variables couple of them are text....can you please share one example how to use all 10 features applying text mining on 2 and finally use classification model to predict results...
Hello! Once the accuracy of Mutlinomial Naive Bayes is calculated, could you tell me how to predict the class of unseen data/test data using same classifier Mutlinomial Naive Bayes?
load the new dataset, use the pipeline to classify the data in the same way as I have mentioned the video. Also, refer the code, a link is given in the description of the video
hi brother i love ur explanation. but i have one question. When i tried to increase the size of target names to 6 from 4, it produces error which says Number of classes, 6, does not match size of target_names, 4. Try specifying the labels parameter how to solve these????
Bro, by default fetch20newsgroup will take all the categories, but I want to work on only 4 categories, so I have created a list containing only those, you can give any name to this list variable. And finally assign to the fetch20 newsgroup categories. I hope, I was able to solve your query.
Try to get the result without using count vectorizer, you might understand then. Anyway in the end we have used something else to make the code shorter
Hi..Amazing video! If I have new file, how to predict the category of that file. Can you please provide the step after the following step? predicted =clf.predict(X_test_tfidf)
Question, can I use the result of MultinomialNB as an input to another machine learning? I'm doing a classification model that requires other features for prediction. By the way, great job for this video. Learned a lot. Appreciate it!
Determining which algorithm to use, totally depends on what kind of problem it is. Sometimes other algorithms works better. So maybe first step is to understand that which kind of algorithm you should use for a particular type of dataset and then use the appropriate algorithm. Thanks for the appreciation.
although you might have noticed now that according to the formula of micro avg there is no need of showing it under all the three. And it has been named to accuracy now which shows 0.83
Your way of explanation is too good
This is one of the best clear example setting videos with step-by-step architecture than that of any "Edtech platforms". Yes, this is what we want as an explanation. It's that simple rather than something more to it. I am surely subscribing to your channel for more explanations in near future. "Beautifully broken down and explained"
Glad it was helpful!
You are awesome. You are better than my professor.. thank you
Everyone who want to start text classification research should watch this video...
Really well explained
Thank you so much!! It means a lot
Perfect Explanation on countvectorizer, tfidfvectorizer and all the metrics with good examples.
This is exactly what I was looking for. I learned how to build a binary classifier (e.g. ham, spam), but needed to learn how to build a model that would predict an outcome for more than two categories. Thank you for the tutorial!
i know it is quite randomly asking but do anyone know of a good place to watch newly released tv shows online ?
is this program (Web Document Classification Using Naïve Bayes in advanced data mining)? i need to know please
better than a few videos i have seen trying to understand the basics of NB+python
Firstly i would like to thank for your video.
Finally found something for text classification with proper explanation
You have a got a very detailed explanation of concept. This is a very big asset for my project implementation.
Thank you so much and please make more videos with more machine learning algorithms.
soon we will make 😄 #codewrestling
THANKYOUUUU 1 LIFE SAVED 🙏🙌😁
very nice superb explanation would like to see more of data science and text mining videos
Your explanation is excellent. Keep up with such good teaching. Thanks !
Keep watching
Good explanation keep it up champ
Very helpful. Totally recommend seeing this.
Congrats from Brazil!
Thank You!!
Amazing so easy to understand. Thank You
Glad it was helpful!
Well explained and properly coded implementation. Thank you for explaining in this manner.
Great narrative and explaination. Keep up the good work brother !
thanks
Awesome explanation. Keep uploading. Thanks a lot :)
Thank you🙏very well explained.please make a video🎥on other algorithms too.please.or else suggest some best channel for it
good explanation and very useful for beginners.
Hi , very nicely explained. Basically I need to understand what will be the output of this model like if I want to return the text as result of text analysis so how can we display the output in excel sheet again in further classification like neutral, positive,negative ..thanks
Superb explanation .. keep it up
Thanks a lot
Excellent explanation clearly and succinctly - Very well done..
Thanks and Stay Tuned with us!! :-)
Awesome brother continue the good work 🙂
wow good job go ahead!
Nice and clear explanation
what if i want to test for single document and want to predict its target_names ?
Can you tell me if there's a way I can do the same classifier with Excel or CSV data sheet?
hi , i would like to ask you something. what techniques should i use to find some keyword in my csv file and then if match with the keyword, i want to assign it to another keyword. the output something like this,
column A Keyword
DUMPBLT:TESTING FAILED: OPERATOR PUSHED STOP BUTTON SYSTEM FAILED
if i found keyword of 'DUMPBLT' and 'PUSHED STOP BUTTON' in column A, i want to assign it to "SYSTEM FAILED" and put to other column. can you help me about this ?
Thanks, dude, nice explanation!
Thanks a lot. Stay Tuned #CodeWrestling
Great explanation really helpful thanks
Thanks sir. is this program (Web Document Classification Using Naïve Bayes )?
Thank you, it's a very nice explanation, great helpful
Hi, I want to build text to intent classification program. Can you suggest which algorithm should be used and how to achieve the same.
can you explain please, what should i do if i want to classify twitter dataset from csv file ?
thanks
Did you find how to do that ?
@@guelibbouchra1115 yes, i'm using pandas dataframe and preprocessing and classifiy
@@rianasmaraputra can you help me with how to classify twitter data from a text file
@@avinashprasad4181 yeah, find me on twitter @rianasmara_p
can you help me please Rian Asmara Putra ?
The concepts are very well explained. But the classifier isn't able to distinguish between alt.atheism and soc.religion.christian. How would you fine tune the model?
Can we classify someother pdf files as datasets using this implementation?? Please Answer bro, I am in a similar kind of project
Why are we not removing the punctuations and stopwords?
Nicely explained magician :)
Good explanation bro..
Thank you 🙂
can someone tell me what is news_train["target"] for? same with the testing set
THANKSSS
Out of all the categories, we restricted ourselves to 4 categories while importing data. [By passing categories list while importing]
Now news_train["target"] shows the same 4 categories.
Note: "target_names" is a key in the dictionary of news_train containing only our 4 categories.
Hope this helps.
@@manthanadmane7812 THANK YOU SOO MUUUUCHHHH =) GOD BLESS YOU :)
@@GelsYT Ah! No worries bud, happy learning :)
Excellent & very impressive !
Thanks for appreciating.
#CodeWrestling
My dataset is not categorical and i want to detect the novelty from news archive.Can any one help me to catch the right approach?i want to use SVM.
This was really great. Can you make a whole video on Next word prediction .......This will help us a lot bro..
Yeah, I will try to, Just for a reference, you can use word2vec skip gram model or continuous bag of words for predicting next word... Stay Tuned.. #CodeWrestling
when I run the accuracy at 20:07 is get the following error:
ValueError: Found input variables with inconsistent numbers of samples: [1502, 2]
do you know how to solve this?
Can you please elaborate a little more?
Greate Content and Nicely explained
Hi, really appreciated your video. Nice explanations.
Thanks! #codewrestling
class_prior parameters in naive bayes what mean? i dont understand in documentation
Very nice video. Question, besides writing less code, what do I gain using the pipeline method? Do I gain computational time?
Nice one! Well done!!
i'm using python django and my datasets were stored in django db how can I use that dataset as the training datasets aside from creating a folder put the documents there
I am also looking for such activity
How to use the model( pickle file) in a separate module to predict a new set of data. How to transform the new data to be predicted?
Hi, I have same doubt. can you help me with this? How to predict new dataset?
hello could you also show an implementation of a binary neural network always with this dataset?
how they are preparing text file as a dataset inside training & test data.what us the format.
Very helpful, thanks and God bless
Thanks a lot. Stay Tuned. #CodeWrestling
I want to label Quora question of dataset around 30k rows in CSV .
How would you test this train model of 20 newsgroup on such a dataset .
Much needed help / any vedio on this will be very helpful .
can you make a video on one example where we can learn how to use text classification multivariate problem...like i have 10 features with categorical output. out of 10 variables couple of them are text....can you please share one example how to use all 10 features applying text mining on 2 and finally use classification model to predict results...
i am working Urdu news text can u guide me. i am working in python with pycharm.
Are these training dataset are .txt files?
Can I use emotion dataset of having attributes I'd, text, emotions
Well explained
Hello! Once the accuracy of Mutlinomial Naive Bayes is calculated, could you tell me how to predict the class of unseen data/test data using same classifier Mutlinomial Naive Bayes?
load the new dataset, use the pipeline to classify the data in the same way as I have mentioned the video. Also, refer the code, a link is given in the description of the video
I have lots of text file for model training how to train model for classifiction?
Can you please make a video on text similarity measurements using cosine similarity ?
10:20
Feature Selection
12:30
Term Frequency
please do a video on decision tree on iris dataset without using sklearn
The video is on the way.. Stay Tuned
Hey bro.. after finding the result of precision recall and F1 score. How we'll write this in text?? Will u help me out from this?
hi brother i love ur explanation. but i have one question. When i tried to increase the size of target names to 6 from 4, it produces error which says Number of classes, 6, does not match size of target_names, 4. Try specifying the labels parameter
how to solve these????
Is there any datasets for feedback classification?
Hello Bro, what is the use of using argument categories = categories while load_files()?
Bro, by default fetch20newsgroup will take all the categories, but I want to work on only 4 categories, so I have created a list containing only those, you can give any name to this list variable. And finally assign to the fetch20 newsgroup categories. I hope, I was able to solve your query.
Hi. did anyone know how's to create a transliteration machine learning that can solved homograph disambiguation using python?
Great video!!! Can we get the github code for the process which is not the magic one?
why you are using count vectorizer and tfidif both in your implementation ? isn't tfidf enough for both of the tasks (counting and transforming)?
Try to get the result without using count vectorizer, you might understand then. Anyway in the end we have used something else to make the code shorter
why sci.med is not included??
It was just for explanation. Not any specific reason behind that.
hi can we have a word regarding the system specifications for machine learning
Very thanks
superb
Very helpful, thank you. I have one question though, will the process be the same if some document had more than one categorie?
Yes it will
Can make a playlist on Mathematics of machine learning , like probability , Linear Algebra , differentiation
how do u train ur data to the model
Use google colab to train the data.
Hi..Amazing video! If I have new file, how to predict the category of that file.
Can you please provide the step after the following step?
predicted =clf.predict(X_test_tfidf)
Question, can I use the result of MultinomialNB as an input to another machine learning? I'm doing a classification model that requires other features for prediction.
By the way, great job for this video. Learned a lot. Appreciate it!
Determining which algorithm to use, totally depends on what kind of problem it is. Sometimes other algorithms works better. So maybe first step is to understand that which kind of algorithm you should use for a particular type of dataset and then use the appropriate algorithm.
Thanks for the appreciation.
Very good explanation. Do you have videos for KNN,Decision tree,SVN models?
yes we have lots of videos in queue coming soon stay tuned #codewrestling
superb bro
Thanks 🤗
I can not find the dataset. by the way thank u for your video
can u give explanation on different algorithms like knn algorithm, decession trees with the same data set
Working on it.
@@CodeWrestling I am doing a project on that data set. It will be helpful to me
Can I use this same code in windows....???
Yeah.
Super bro
I am not getting micro avg row in the output pls help
although you might have noticed now that according to the formula of micro avg there is no need of showing it under all the three. And it has been named to accuracy now which shows 0.83
hello sir i am not getting that data set on internet can u help me for this
you can find the dataset on the following link:
qwone.com/~jason/20Newsgroups/20news-bydate.tar.gz
#codewrestling
It is an amazing video. Am working my thesis on Amharic language classification. could u guide me on doing so?
Nice Video
is very nice vedio.But what about the csv dataset.
thanks a lot
How would I get another dataset?
kaggel
Thanks, bro
can u pls upload video for KMeans method in python
Sure its on the way..
Hi CodeWrestler I want to get in touch with you regarding some doubts based on a project. Could you please get back to me. Thank you
Hi mate,
Can you made a Naive Bayes algorithm in Machine learning Program but with Document Classification?
Thx.
Sure, I will definitely look into it #codewrestling