Build Natural Language Processing (NLP) model with Python| TF-IDF, N-gram, Text processing

Published: 02 July 2021
on channel: When Maths Meet Coding

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A complete Guide to Build and Deploy NLP Model with Python, A to Z (NLP) Machine Learning Model Building, and Deployment with streamlit to a web app. A complete explanation of TF-IDF, N-gram, and Text processing word vectorization techniques.

https://github.com/jakkcoder/Language...

link for the dataset I have used in the current tutorial

https://www.kaggle.com/basilb2s/langu...

0:00 Intro — NLP Language Detection Project (End-to-End)
0:41 What You’ll Learn + Full Pipeline Overview (TF-IDF, Pickle, Web App)
1:31 Environment Setup + GitHub Links (Env + Dataset)
2:30 Dataset Overview (Language Detection CSV)
3:28 Libraries + Start in Jupyter Notebook
5:14 Text Cleaning (Lowercase + Remove Punctuation Function)
10:20 Train-Test Split (Prepare X and Y)
13:02 Why Vectorization is Needed (Text → Numbers)
13:33 Unigram vs Bigram vs Trigram (Concept)
18:20 TF-IDF Explained (Term Frequency + Inverse Document Frequency)
21:20 TF-IDF Vectorization (sklearn) + Pipeline Setup
24:30 Train Logistic Regression Model
25:44 Model Evaluation (Accuracy + Confusion Matrix)
27:24 Testing with Custom Sentences (Live Predictions)
28:50 Save Model as Pickle (.pkl)
31:12 Streamlit Web App (app.py Explanation)
33:14 Run Streamlit App (streamlit run app.py)
34:33 Wrap-up + Next Steps (Packaging/Docker/Deploy)

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