Applied Text Mining and Sentiment Analysis with Python | 2470BAC3D740EBA4ECD833D965F28EC0B0CA1C17

Applied Text Mining and Sentiment Analysis with Python

Name	Applied Text Mining and Sentiment Analysis with Python	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	1.0 GB
Total Files	145
Hash	2470BAC3D740EBA4ECD833D965F28EC0B0CA1C17

/1. Course Preview/
1. Preview.mp4	73.4 MB
1. Preview.srt	5.3 KB
/
TutsNode.com.txt	0.1 KB
[TGx]Downloaded from torrentgalaxy.to .txt	0.6 KB
/3. Text Normalization/
4. (Python Practice) Cleaning Twitter Features.srt	8.2 KB
6. (Python Practice) Cleaning General Features.srt	6.7 KB
4. (Python Practice) Cleaning Twitter Features.mp4	39.9 MB
15.1 Colab_Notebook_Section_2_completed.ipynb	83.9 KB
7. Tokenization.srt	5.5 KB
3. Text Cleaning (12) - Twitter Features.srt	4.3 KB
14. (Python Practice) Applied Lemmatization.srt	4.0 KB
1. Section Overview.srt	1.2 KB
2. What is Text Normalization.srt	3.8 KB
5. Text Cleaning (22) - General Features.srt	3.6 KB
10. (Python Practice) Applied Tokenization (33).srt	3.5 KB
12. (Python Practice) Applied Stemming.srt	3.4 KB
8. (Python Practice) Applied Tokenization (13).srt	2.3 KB
11. Stemming.srt	3.2 KB
9. (Python Practice) Applied Tokenization (23).srt	2.4 KB
15. (Python Pratice) Tweet Pre-Processing.srt	1.1 KB
13. Lemmatization.srt	2.5 KB
6. (Python Practice) Cleaning General Features.mp4	32.3 MB
7. Tokenization.mp4	27.5 MB
3. Text Cleaning (12) - Twitter Features.mp4	23.3 MB
2. What is Text Normalization.mp4	20.5 MB
12. (Python Practice) Applied Stemming.mp4	19.7 MB
5. Text Cleaning (22) - General Features.mp4	19.6 MB
14. (Python Practice) Applied Lemmatization.mp4	19.6 MB
1. Section Overview.mp4	19.5 MB
10. (Python Practice) Applied Tokenization (33).mp4	19.2 MB
11. Stemming.mp4	19.0 MB
13. Lemmatization.mp4	15.5 MB
8. (Python Practice) Applied Tokenization (13).mp4	13.2 MB
9. (Python Practice) Applied Tokenization (23).mp4	12.5 MB
15. (Python Pratice) Tweet Pre-Processing.mp4	8.8 MB
2.1 Section 2 - Theory Deck.pdf	1.9 MB
/5. Sentiment Analysis/
3. Logistic Regression.srt	7.9 KB
7. Model Performance Measures.srt	7.2 KB
6. (Python Practice) ML Model Fitting.srt	6.1 KB
8.1 Colab_Notebook_Section_4_completed.ipynb	87.4 KB
4. ML Model Training.srt	5.8 KB
8. (Python Practice) Applied Performance Measures.srt	4.1 KB
3. Logistic Regression.mp4	39.3 MB
5. (Python Practice) TrainTest split.srt	2.9 KB
4. ML Model Training.mp4	35.5 MB
9. (Python Practice) Prediction Pipeline.srt	2.2 KB
2. Why a model.srt	1.7 KB
1. Section Overview.srt	1.1 KB
7. Model Performance Measures.mp4	35.1 MB
6. (Python Practice) ML Model Fitting.mp4	30.9 MB
8. (Python Practice) Applied Performance Measures.mp4	20.0 MB
1. Section Overview.mp4	18.0 MB
5. (Python Practice) TrainTest split.mp4	17.7 MB
9. (Python Practice) Prediction Pipeline.mp4	13.2 MB
2. Why a model.mp4	12.3 MB
2.1 Section 4 - Theory Deck.pdf	1.6 MB
/.../2. Introduction to Text Mining/
1. Section Overview.srt	2.0 KB
4. Text Mining and NLP.srt	2.5 KB
5. Sentiment Analysis.srt	2.8 KB
6. Roadmap.srt	2.8 KB
10.1 Colab_Notebook_Section_1_completed.ipynb	80.4 KB
7.1 Colab_Notebook.ipynb	79.4 KB
9. (Python Practice) Dataset Overview.srt	3.1 KB
8. (Python Practice) Dataset Connection.srt	3.9 KB
10. (Python Practice) Dataset Visualization.srt	3.8 KB
2. What is Text.srt	3.6 KB
7. (Python Practice) Google Colab.srt	3.2 KB
3. What is Text Mining.srt	3.2 KB
1. Section Overview.mp4	30.5 MB
10. (Python Practice) Dataset Visualization.mp4	23.3 MB
8. (Python Practice) Dataset Connection.mp4	22.3 MB
2. What is Text.mp4	21.5 MB
3. What is Text Mining.mp4	20.0 MB
5. Sentiment Analysis.mp4	17.1 MB
9. (Python Practice) Dataset Overview.mp4	17.0 MB
6. Roadmap.mp4	17.0 MB
4. Text Mining and NLP.mp4	15.3 MB
7. (Python Practice) Google Colab.mp4	12.9 MB
2.1 Section 1 - Theory Deck.pdf	2.7 MB
8.1 tweet_data.csv	1.8 MB
/.pad/
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4	35.2 KB
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11	253.7 KB
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27	442.9 KB
28	336.2 KB
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31	111.8 KB
32	222.1 KB
33	301.9 KB
34	329.6 KB
35	238.3 KB
36	409.2 KB
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39	161.5 KB
40	82.2 KB
41	328.2 KB
42	135.3 KB
43	436.1 KB
44	206.8 KB
45	261.4 KB
46	447.3 KB
/4. Text Vectorization/
8.1 Colab_Notebook_Section_3_completed.ipynb	85.8 KB
6. (Python Practice) Applied Bag-of-Words.srt	5.9 KB
7. TF-IDF.srt	4.8 KB
3. PositiveNegative Word Frequencies.srt	4.7 KB
1. Section Overview.srt	1.4 KB
4. (Python Practice) Applied PositiveNegative Frequencies.srt	3.6 KB
5. Bag-of-Words.srt	3.5 KB
8. (Python Practice) Applied TF-IDF.srt	3.4 KB
2. Why Representing Text.srt	2.6 KB
6. (Python Practice) Applied Bag-of-Words.mp4	30.5 MB
7. TF-IDF.mp4	24.6 MB
3. PositiveNegative Word Frequencies.mp4	24.4 MB
1. Section Overview.mp4	23.6 MB
4. (Python Practice) Applied PositiveNegative Frequencies.mp4	22.0 MB
5. Bag-of-Words.mp4	20.6 MB
8. (Python Practice) Applied TF-IDF.mp4	18.5 MB
2. Why Representing Text.mp4	18.5 MB
2.1 Section 3 - Theory Deck.pdf	1.6 MB
Total files 145