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Applied Text Mining and Sentiment Analysis with Python
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[TutsNode.com] - Applied Text Mining and Sentiment Analysis with Python (Size: 961.03 MB) (Files: 145)
[TutsNode.com] - Applied Text Mining and Sentiment Analysis with Python
1. Course Preview
1. Preview.mp4
1. Preview.srt
3. Text Normalization
4. (Python Practice) Cleaning Twitter Features.srt
6. (Python Practice) Cleaning General Features.srt
4. (Python Practice) Cleaning Twitter Features.mp4
15.1 Colab_Notebook_Section_2_completed.ipynb
7. Tokenization.srt
3. Text Cleaning (12) - Twitter Features.srt
14. (Python Practice) Applied Lemmatization.srt
1. Section Overview.srt
2. What is Text Normalization.srt
5. Text Cleaning (22) - General Features.srt
10. (Python Practice) Applied Tokenization (33).srt
12. (Python Practice) Applied Stemming.srt
8. (Python Practice) Applied Tokenization (13).srt
11. Stemming.srt
9. (Python Practice) Applied Tokenization (23).srt
15. (Python Pratice) Tweet Pre-Processing.srt
13. Lemmatization.srt
6. (Python Practice) Cleaning General Features.mp4
7. Tokenization.mp4
3. Text Cleaning (12) - Twitter Features.mp4
2. What is Text Normalization.mp4
12. (Python Practice) Applied Stemming.mp4
5. Text Cleaning (22) - General Features.mp4
14. (Python Practice) Applied Lemmatization.mp4
1. Section Overview.mp4
10. (Python Practice) Applied Tokenization (33).mp4
11. Stemming.mp4
13. Lemmatization.mp4
8. (Python Practice) Applied Tokenization (13).mp4
9. (Python Practice) Applied Tokenization (23).mp4
15. (Python Pratice) Tweet Pre-Processing.mp4
2.1 Section 2 - Theory Deck.pdf
5. Sentiment Analysis
3. Logistic Regression.srt
7. Model Performance Measures.srt
6. (Python Practice) ML Model Fitting.srt
8.1 Colab_Notebook_Section_4_completed.ipynb
4. ML Model Training.srt
8. (Python Practice) Applied Performance Measures.srt
3. Logistic Regression.mp4
5. (Python Practice) TrainTest split.srt
4. ML Model Training.mp4
9. (Python Practice) Prediction Pipeline.srt
2. Why a model.srt
1. Section Overview.srt
7. Model Performance Measures.mp4
6. (Python Practice) ML Model Fitting.mp4
8. (Python Practice) Applied Performance Measures.mp4
1. Section Overview.mp4
5. (Python Practice) TrainTest split.mp4
9. (Python Practice) Prediction Pipeline.mp4
2. Why a model.mp4
2.1 Section 4 - Theory Deck.pdf
2. Introduction to Text Mining
1. Section Overview.srt
4. Text Mining and NLP.srt
5. Sentiment Analysis.srt
6. Roadmap.srt
10.1 Colab_Notebook_Section_1_completed.ipynb
7.1 Colab_Notebook.ipynb
9. (Python Practice) Dataset Overview.srt
8. (Python Practice) Dataset Connection.srt
10. (Python Practice) Dataset Visualization.srt
2. What is Text.srt
7. (Python Practice) Google Colab.srt
3. What is Text Mining.srt
1. Section Overview.mp4
10. (Python Practice) Dataset Visualization.mp4
8. (Python Practice) Dataset Connection.mp4
2. What is Text.mp4
3. What is Text Mining.mp4
5. Sentiment Analysis.mp4
9. (Python Practice) Dataset Overview.mp4
6. Roadmap.mp4
4. Text Mining and NLP.mp4
7. (Python Practice) Google Colab.mp4
2.1 Section 1 - Theory Deck.pdf
8.1 tweet_data.csv
4. Text Vectorization
8.1 Colab_Notebook_Section_3_completed.ipynb
6. (Python Practice) Applied Bag-of-Words.srt
7. TF-IDF.srt
3. PositiveNegative Word Frequencies.srt
1. Section Overview.srt
4. (Python Practice) Applied PositiveNegative Frequencies.srt
5. Bag-of-Words.srt
8. (Python Practice) Applied TF-IDF.srt
2. Why Representing Text.srt
6. (Python Practice) Applied Bag-of-Words.mp4
7. TF-IDF.mp4
3. PositiveNegative Word Frequencies.mp4
1. Section Overview.mp4
4. (Python Practice) Applied PositiveNegative Frequencies.mp4
5. Bag-of-Words.mp4
8. (Python Practice) Applied TF-IDF.mp4
2. Why Representing Text.mp4
2.1 Section 3 - Theory Deck.pdf
TutsNode.com.txt
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