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Checked by:
Category:
Language:
English
Total Size:
961.0 MB
Info Hash:
2470BAC3D740EBA4ECD833D965F28EC0B0CA1C17
Added By:
Added:
June 1, 2023, 11:46 p.m.
Stats:
|
(Last updated: May 17, 2025, 2:30 a.m.)
| File | Size |
|---|---|
| 1. Preview.mp4 | 70.0 MB |
| TutsNode.com.txt | 63 bytes |
| [TGx]Downloaded from torrentgalaxy.to .txt | 585 bytes |
| 4. (Python Practice) Cleaning Twitter Features.srt | 8.0 KB |
| 3. Logistic Regression.srt | 7.7 KB |
| 1. Section Overview.srt | 2.0 KB |
| 7. Model Performance Measures.srt | 7.1 KB |
| 6. (Python Practice) Cleaning General Features.srt | 6.6 KB |
| 6. (Python Practice) ML Model Fitting.srt | 6.0 KB |
| 0 | 606 bytes |
| 4. (Python Practice) Cleaning Twitter Features.mp4 | 38.0 MB |
| 8.1 Colab_Notebook_Section_4_completed.ipynb | 85.3 KB |
| 4. Text Mining and NLP.srt | 2.4 KB |
| 8.1 Colab_Notebook_Section_3_completed.ipynb | 83.7 KB |
| 5. Sentiment Analysis.srt | 2.7 KB |
| 15.1 Colab_Notebook_Section_2_completed.ipynb | 82.0 KB |
| 6. Roadmap.srt | 2.7 KB |
| 10.1 Colab_Notebook_Section_1_completed.ipynb | 78.5 KB |
| 7.1 Colab_Notebook.ipynb | 77.5 KB |
| 6. (Python Practice) Applied Bag-of-Words.srt | 5.8 KB |
| 4. ML Model Training.srt | 5.7 KB |
| 7. Tokenization.srt | 5.3 KB |
| 1. Preview.srt | 5.2 KB |
| 7. TF-IDF.srt | 4.7 KB |
| 9. (Python Practice) Dataset Overview.srt | 3.0 KB |
| 3. PositiveNegative Word Frequencies.srt | 4.6 KB |
| 3. Text Cleaning (12) - Twitter Features.srt | 4.2 KB |
| 8. (Python Practice) Applied Performance Measures.srt | 4.0 KB |
| 14. (Python Practice) Applied Lemmatization.srt | 3.9 KB |
| 1. Section Overview.srt | 1.2 KB |
| 1. Section Overview.srt | 1.4 KB |
| 1 | 166 bytes |
| 3. Logistic Regression.mp4 | 37.4 MB |
| 8. (Python Practice) Dataset Connection.srt | 3.8 KB |
| 2. What is Text Normalization.srt | 3.7 KB |
| 10. (Python Practice) Dataset Visualization.srt | 3.7 KB |
| 4. (Python Practice) Applied PositiveNegative Frequencies.srt | 3.5 KB |
| 5. Text Cleaning (22) - General Features.srt | 3.5 KB |
| 2. What is Text.srt | 3.5 KB |
| 5. Bag-of-Words.srt | 3.5 KB |
| 10. (Python Practice) Applied Tokenization (33).srt | 3.4 KB |
| 8. (Python Practice) Applied TF-IDF.srt | 3.4 KB |
| 12. (Python Practice) Applied Stemming.srt | 3.3 KB |
| 7. (Python Practice) Google Colab.srt | 3.2 KB |
| 8. (Python Practice) Applied Tokenization (13).srt | 2.3 KB |
| 11. Stemming.srt | 3.1 KB |
| 9. (Python Practice) Applied Tokenization (23).srt | 2.4 KB |
| 3. What is Text Mining.srt | 3.1 KB |
| 5. (Python Practice) TrainTest split.srt | 2.8 KB |
| 15. (Python Pratice) Tweet Pre-Processing.srt | 1.1 KB |
| 2 | 98 bytes |
| 4. ML Model Training.mp4 | 33.8 MB |
| 2. Why Representing Text.srt | 2.6 KB |
| 13. Lemmatization.srt | 2.5 KB |
| 9. (Python Practice) Prediction Pipeline.srt | 2.1 KB |
| 2. Why a model.srt | 1.7 KB |
| 1. Section Overview.srt | 1.1 KB |
| 3 | 157.9 KB |
| 7. Model Performance Measures.mp4 | 33.5 MB |
| 4 | 34.4 KB |
| 6. (Python Practice) Cleaning General Features.mp4 | 30.8 MB |
| 5 | 189.2 KB |
| 6. (Python Practice) ML Model Fitting.mp4 | 29.5 MB |
| 6 | 14.4 KB |
| 6. (Python Practice) Applied Bag-of-Words.mp4 | 29.1 MB |
| 7 | 434.7 KB |
| 1. Section Overview.mp4 | 29.0 MB |
| 8 | 466.0 KB |
| 7. Tokenization.mp4 | 26.2 MB |
| 9 | 320.5 KB |
| 7. TF-IDF.mp4 | 23.5 MB |
| 10 | 47.1 KB |
| 3. PositiveNegative Word Frequencies.mp4 | 23.3 MB |
| 11 | 247.8 KB |
| 1. Section Overview.mp4 | 22.5 MB |
| 12 | 486.8 KB |
| 10. (Python Practice) Dataset Visualization.mp4 | 22.2 MB |
| 13 | 324.3 KB |
| 3. Text Cleaning (12) - Twitter Features.mp4 | 22.2 MB |
| 14 | 327.3 KB |
| 8. (Python Practice) Dataset Connection.mp4 | 21.2 MB |
| 15 | 263.6 KB |
| 4. (Python Practice) Applied PositiveNegative Frequencies.mp4 | 21.0 MB |
| 16 | 38.8 KB |
| 2. What is Text.mp4 | 20.5 MB |
| 17 | 24.0 KB |
| 5. Bag-of-Words.mp4 | 19.6 MB |
| 18 | 409.8 KB |
| 2. What is Text Normalization.mp4 | 19.6 MB |
| 19 | 459.9 KB |
| 8. (Python Practice) Applied Performance Measures.mp4 | 19.1 MB |
| 20 | 397.1 KB |
| 3. What is Text Mining.mp4 | 19.0 MB |
| 21 | 471.0 KB |
| 12. (Python Practice) Applied Stemming.mp4 | 18.8 MB |
| 22 | 221.8 KB |
| 5. Text Cleaning (22) - General Features.mp4 | 18.7 MB |
| 23 | 275.0 KB |
| 14. (Python Practice) Applied Lemmatization.mp4 | 18.6 MB |
| 24 | 361.1 KB |
| 1. Section Overview.mp4 | 18.6 MB |
| 25 | 442.6 KB |
| 10. (Python Practice) Applied Tokenization (33).mp4 | 18.3 MB |
| 26 | 207.3 KB |
| 11. Stemming.mp4 | 18.1 MB |
| 27 | 432.6 KB |
| 8. (Python Practice) Applied TF-IDF.mp4 | 17.7 MB |
| 28 | 328.3 KB |
| 2. Why Representing Text.mp4 | 17.6 MB |
| 29 | 398.4 KB |
| 1. Section Overview.mp4 | 17.2 MB |
| 30 | 306.6 KB |
| 5. (Python Practice) TrainTest split.mp4 | 16.9 MB |
| 31 | 109.1 KB |
| 5. Sentiment Analysis.mp4 | 16.3 MB |
| 32 | 216.9 KB |
| 9. (Python Practice) Dataset Overview.mp4 | 16.2 MB |
| 33 | 294.8 KB |
| 6. Roadmap.mp4 | 16.2 MB |
| 34 | 321.9 KB |
| 13. Lemmatization.mp4 | 14.8 MB |
| 35 | 232.7 KB |
| 4. Text Mining and NLP.mp4 | 14.6 MB |
| 36 | 399.6 KB |
| 9. (Python Practice) Prediction Pipeline.mp4 | 12.6 MB |
| 37 | 379.9 KB |
| 8. (Python Practice) Applied Tokenization (13).mp4 | 12.6 MB |
| 38 | 417.7 KB |
| 7. (Python Practice) Google Colab.mp4 | 12.3 MB |
| 39 | 157.7 KB |
| 9. (Python Practice) Applied Tokenization (23).mp4 | 11.9 MB |
| 40 | 80.3 KB |
| 2. Why a model.mp4 | 11.7 MB |
| 41 | 320.5 KB |
| 15. (Python Pratice) Tweet Pre-Processing.mp4 | 8.4 MB |
| 42 | 132.2 KB |
| 2.1 Section 1 - Theory Deck.pdf | 2.6 MB |
| 43 | 425.9 KB |
| 2.1 Section 2 - Theory Deck.pdf | 1.8 MB |
| 44 | 202.0 KB |
| 8.1 tweet_data.csv | 1.8 MB |
| 45 | 255.2 KB |
| 2.1 Section 4 - Theory Deck.pdf | 1.6 MB |
| 46 | 436.8 KB |
| 2.1 Section 3 - Theory Deck.pdf | 1.5 MB |
Name
DL
Uploader
Size
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Added
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870.3 MB
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2023-06-01
| Uploaded by freecoursewb | Size 870.3 MB | Health [ 0 /0 ] | Added 2023-06-01 |
NOTE
SOURCE: Udemy - Applied Text Mining and Sentiment Analysis with Python
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