Building a Supervised Text Classification Model

Presented by WWCode Data Science
👩‍💻 Speaker: Rishika Singh, Jayeeta Putatunda
✅ Topics: Intro to Machine Learning, Text Mining, Text Analysis, Text Classification
Considering the vast amount of unstructured text data that is constantly generated (eg, social media channels like Facebook, Twitter, forums like Quora, Reddit, various blogs, news platforms), NLP has become the crux to process, analyze and understand this large amount of text data. Almost all technology we use daily has underlying NLP use-cases like speech-to-text, machine translation, image captioning and so on. This 6-part technical series (led by Jayeeta Putatunda, Rishika Singh, Candice Yun Chen, Ruchika Singh) introducing Natural Language Processing focuses on how it can be used in solving various industry problems! Register for upcoming sessions of this series at bit.ly/introton...
In this session, Rishika Singh introduces us to machine learning and its types. She jumps into supervised learning, its types and methodologies to evaluate supervised models. She then shows us the important distinction between Text Analysis and Text Classification. In the second part of the session, Jayeeta Putatunda shows us how to build a supervised model to detect fake news using a open-source Kaggle dataset with nltk, sklearn, pandas in Google Colab.
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[@Dami_San]

Hi, is it possible to get a link to the notebooks? Thank You

[@steven.pineda]

Extremely useful and a wide variety of techniques. Thanks!

[@selinakarac6792]

Very helpful, detailed and clear descriptions.

[@superfreiheit1]

code area is to small cant see

[@kkonline1980]

Great presentation. Thank you, you just saved my bacon ;)

[@kiaraintheforest]

Thank you for sharing this video.

[@maYYidtS]

is it necessary to remove stop words and apply lemmatization for all kinds of tasks?... if not when to use them...example, please.

[@kiaraintheforest]

Hi. As far as I know, it's not necessary, it's up to the programmer whether they want to include or exclude stop words. Depends on what you want to achieve.