Curriculum
- 2 Sections
- 22 Lessons
- 10 Days
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- Getting Started and Preprocessing using NLP12
- 1.1Project Overview [Video]12 Minutes
- 1.2Steps to be followed in the Project6 Minutes
- 1.3About the Input Data [Video]10 Minutes
- 1.4Tweet Cleaning Steps10 Minutes
- 1.5Noise in Text Data10 Minutes
- 1.6Use of NLP (Natural Language Processing) Techniques in the Project15 Minutes
- 1.7Stemming10 Minutes
- 1.8Lemmatization10 Minutes
- 1.9Stop Words Removal10 Minutes
- 1.10EDA Concepts [Video]15 Minutes
- 1.11EDA and Preprocessing Coding Explanation [Video]20 Minutes
- 1.12Text Tokenization [Video]25 Minutes
- Create Text Embeddings and Sentiment Modelling10
- 2.1Bag of Words and TF-IDF Concepts [Video]15 Minutes
- 2.2Bag of Words (BoW) Representation / Embeddings [Video]20 Minutes
- 2.3TF-IDF (Term Frequency-Inverse Document Frequency) Representation / Embeddings [Video]12 Minutes
- 2.4Difference between BoW and TF-IDF10 Minutes
- 2.5Logistic Regression Algorithm [Video]20 Minutes
- 2.6Classification Errors and Metrics [Video]10 Minutes
- 2.7How does Logistic Regression fit / train on the Data + Coding [Video]20 Minutes
- 2.8Classification Metrics10 Minutes
- 2.9Streamlit UI [Video]10 Minutes
- 2.10Python Code for Project