NLP Demystified 8: Text Classification With Naive Bayes (+ precision and recall)

[@futuremojo]

Timestamps: 00:00:00 Naive Bayes 00:00:25 Classification as a core task in NLP 00:01:11 Revisiting conditional probability 00:03:26 Deriving Bayes' Theorem 00:04:12 The parts of Bayes' Theorem 00:05:43 A spatial example using Bayes' Theorem 00:07:33 Bayes' Theorem applied to text classification 00:08:30 The "naive" in Naive Bayes 00:09:34 The need to work in log space 00:10:05 Naive Bayes training and usage 00:13:27 How the "accuracy" metric can go wrong 00:14:10 Precision, Recall, and Confusion Matrix 00:17:47 DEMO: Training and using a Naive Bayes classifier 00:36:28 Naive Bayes recap and other classification models

[@gauravmalik3911]

This is one of the best video I enjoyed ever while learning machine learning. Explaining conditional probability to naive Bayes demo in detailed and still in concise way is art. Wow, this is excellent playlist.

[@CharlesOkwuagwu]

Thanks so much for your clear and concise explanations and steps

[@jett_royce]

Thanks for the feedback! I'm glad you're finding it useful.

[@CharlesOkwuagwu]

@@jett_royce pease what time frame for the completion, and are you open to working on private tutorship or projects?

[@futuremojo]

@@CharlesOkwuagwu My aim is to publish the remaining modules before the end of summer (a new module was published today). Unfortunately, I don't have time for private tutorship but you can email me and I'll try to point you in the right direction.

[@MangaMania24]

Thanks for making it enjoyable and exciting by explaining clearly!

[@samuelcortinhas4877]

This video made a lot of things "click" for me, thank you!

[@futuremojo]

Love to hear it!

[@user-ie5gj9bh4p]

Your explanations are simply amazing! Congrats!!!

[@basiaostaszewska7775]

Thank you it was very well done!

[@patndedi7955]

Very well done

[@johnhandley1870]

"My toilet is haunted" at 10:30 had me cracking up 😂

[@vipulmaheshwari2321]

I think When we are using Naive Bayes for text classification, it calculates probabilities at the token level rather than for intact words. For example, at 10:52 when considering the word "vaccine", the model is actually looking at the likelihood of the token "vaccine" rather than the exact word. This is an important distinction, because tokenizing the input texts allows the model to handle different word forms, capture useful n-gram contexts, and focus on predictive tokens. So the probabilities are calculated for tokens P(token|class) rather than unique words P(word|class). Tokenization before applying Naive Bayes is a key step, as it allows the model to better capture patterns and meaning from the input texts. Correct me if I am wrong

[@Bibhukalyan_iitkgp_21]

hey man can you please share the slides

[@Ankit-zv7lz]

sir is it possible that you can provide the link to the slides? Please! I would be really appreciate it.