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Udemy - Applied Text Mining and Sentiment Analysis with Python

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tutsnode

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Downloads:

362

Type:

Tutorials

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Udemy - Applied Text Mining and Sentiment Analysis with Python

Language:

English

Category:

Other

Size:

961.0 MB

Added:

June 1, 2023, 11:46 p.m.

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Seeders: 3, Leechers: 3 (Last updated: 10 months, 4 weeks ago)

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Files:

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

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