Download Udemy - Applied Text Mining and Sentiment Analysis with Python Torrent

Torrent Contents Size: 961 MB

Udemy - Applied Text Mining and Sentiment Analysis with Python

▼ show more 130 files

0		614.4 B
1		204.8 B
1. Preview.mp4	MP4	70 MB
1. Preview.srt	SRT	5.2 KB
1. Section Overview.mp4	MP4	22.5 MB
1. Section Overview.srt	SRT	1.4 KB
2		102.4 B
10. (Python Practice) Applied Tokenization (33).mp4	MP4	18.3 MB
10. (Python Practice) Applied Tokenization (33).srt	SRT	3.4 KB
10. (Python Practice) Dataset Visualization.mp4	MP4	22.2 MB
10. (Python Practice) Dataset Visualization.srt	SRT	3.7 KB
10.1 Colab_Notebook_Section_1_completed.ipynb	IPYNB	78.5 KB
11. Stemming.mp4	MP4	18.1 MB
11. Stemming.srt	SRT	3.1 KB
12. (Python Practice) Applied Stemming.mp4	MP4	18.8 MB
12. (Python Practice) Applied Stemming.srt	SRT	3.3 KB
13. Lemmatization.mp4	MP4	14.8 MB
13. Lemmatization.srt	SRT	2.5 KB
14. (Python Practice) Applied Lemmatization.mp4	MP4	18.6 MB
14. (Python Practice) Applied Lemmatization.srt	SRT	3.9 KB
15. (Python Pratice) Tweet Pre-Processing.mp4	MP4	8.4 MB
15. (Python Pratice) Tweet Pre-Processing.srt	SRT	1.1 KB
15.1 Colab_Notebook_Section_2_completed.ipynb	IPYNB	82 KB
2. What is Text Normalization.mp4	MP4	19.6 MB
2. What is Text Normalization.srt	SRT	3.7 KB
2. What is Text.mp4	MP4	20.5 MB
2. What is Text.srt	SRT	3.5 KB
2. Why Representing Text.mp4	MP4	17.6 MB
2. Why Representing Text.srt	SRT	2.6 KB
2. Why a model.mp4	MP4	11.7 MB
2. Why a model.srt	SRT	1.7 KB
2.1 Section 1 - Theory Deck.pdf	PDF	2.6 MB
2.1 Section 2 - Theory Deck.pdf	PDF	1.8 MB
2.1 Section 3 - Theory Deck.pdf	PDF	1.5 MB
2.1 Section 4 - Theory Deck.pdf	PDF	1.6 MB
3. Logistic Regression.mp4	MP4	37.4 MB
3. Logistic Regression.srt	SRT	7.7 KB
3. PositiveNegative Word Frequencies.mp4	MP4	23.3 MB
3. PositiveNegative Word Frequencies.srt	SRT	4.6 KB
3. Text Cleaning (12) - Twitter Features.mp4	MP4	22.2 MB
3. Text Cleaning (12) - Twitter Features.srt	SRT	4.2 KB
3. What is Text Mining.mp4	MP4	19 MB
3. What is Text Mining.srt	SRT	3.1 KB
3		157.9 KB
4		34.4 KB
4. (Python Practice) Applied PositiveNegative Frequencies.mp4	MP4	21 MB
4. (Python Practice) Applied PositiveNegative Frequencies.srt	SRT	3.5 KB
4. (Python Practice) Cleaning Twitter Features.mp4	MP4	38 MB
4. (Python Practice) Cleaning Twitter Features.srt	SRT	8 KB
4. ML Model Training.mp4	MP4	33.8 MB
4. ML Model Training.srt	SRT	5.7 KB
4. Text Mining and NLP.mp4	MP4	14.6 MB
4. Text Mining and NLP.srt	SRT	2.4 KB
5. (Python Practice) TrainTest split.mp4	MP4	16.9 MB
5. (Python Practice) TrainTest split.srt	SRT	2.8 KB
5. Bag-of-Words.mp4	MP4	19.6 MB
5. Bag-of-Words.srt	SRT	3.5 KB
5. Sentiment Analysis.mp4	MP4	16.3 MB
5. Sentiment Analysis.srt	SRT	2.7 KB
5. Text Cleaning (22) - General Features.srt	SRT	3.5 KB
5		189.2 KB
5. Text Cleaning (22) - General Features.mp4	MP4	18.7 MB
6		14.4 KB
6. (Python Practice) Applied Bag-of-Words.mp4	MP4	29.1 MB
6. (Python Practice) Applied Bag-of-Words.srt	SRT	5.8 KB
6. (Python Practice) Cleaning General Features.mp4	MP4	30.8 MB
6. (Python Practice) Cleaning General Features.srt	SRT	6.6 KB
6. (Python Practice) ML Model Fitting.mp4	MP4	29.5 MB
6. (Python Practice) ML Model Fitting.srt	SRT	6 KB
6. Roadmap.mp4	MP4	16.2 MB
6. Roadmap.srt	SRT	2.7 KB
7. (Python Practice) Google Colab.mp4	MP4	12.3 MB
7. Model Performance Measures.srt	SRT	7.1 KB
7. TF-IDF.mp4	MP4	23.5 MB
7		434.7 KB
7. (Python Practice) Google Colab.srt	SRT	3.2 KB
7. Model Performance Measures.mp4	MP4	33.5 MB
7. TF-IDF.srt	SRT	4.7 KB
7. Tokenization.mp4	MP4	26.2 MB
7. Tokenization.srt	SRT	5.3 KB
7.1 Colab_Notebook.ipynb	IPYNB	77.5 KB
8. (Python Practice) Applied Performance Measures.mp4	MP4	19.1 MB
8. (Python Practice) Applied Performance Measures.srt	SRT	4 KB
8. (Python Practice) Applied TF-IDF.mp4	MP4	17.7 MB
8. (Python Practice) Applied TF-IDF.srt	SRT	3.4 KB
8. (Python Practice) Applied Tokenization (13).mp4	MP4	12.6 MB
8. (Python Practice) Dataset Connection.srt	SRT	3.8 KB
8		466 KB
8. (Python Practice) Applied Tokenization (13).srt	SRT	2.3 KB
8. (Python Practice) Dataset Connection.mp4	MP4	21.2 MB
8.1 Colab_Notebook_Section_3_completed.ipynb	IPYNB	83.7 KB
8.1 Colab_Notebook_Section_4_completed.ipynb	IPYNB	85.3 KB
8.1 tweet_data.csv	CSV	1.8 MB
9. (Python Practice) Applied Tokenization (23).srt	SRT	2.4 KB
9		320.5 KB
9. (Python Practice) Applied Tokenization (23).mp4	MP4	11.9 MB
9. (Python Practice) Dataset Overview.mp4	MP4	16.2 MB
9. (Python Practice) Dataset Overview.srt	SRT	3 KB
9. (Python Practice) Prediction Pipeline.mp4	MP4	12.6 MB
9. (Python Practice) Prediction Pipeline.srt	SRT	2.1 KB
TutsNode.com.txt	TXT	102.4 B
[TGx]Downloaded from torrentgalaxy.to .txt	TXT	614.4 B
10		47.1 KB
11		247.8 KB
12		486.8 KB
13		324.3 KB
14		327.3 KB
15		263.6 KB
16		38.8 KB
17		24 KB
18		409.8 KB
19		459.9 KB
20		397.1 KB
21		471 KB
22		221.8 KB
23		275 KB
24		361.1 KB
25		442.6 KB
26		207.3 KB
27		432.6 KB
28		328.3 KB
29		398.4 KB
30		306.6 KB
31		109.1 KB
32		216.9 KB
33		294.8 KB
34		321.9 KB
35		232.7 KB
36		399.6 KB
37		379.9 KB
38		417.7 KB
39		157.7 KB
40		80.3 KB
41		320.5 KB
42		132.2 KB
43		425.9 KB
44		202 KB
45		255.2 KB
46		436.8 KB

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Udemy - Applied Text Mining and Sentiment Analysis with Python

Torrent Contents Size: 961 MB

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