|
Please note that this page does not hosts or makes available any of the listed filenames. You
cannot download any of those files from here.
|
| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
| 0 |
606B |
| 1 |
166B |
| 1. Preview.mp4 |
69.96MB |
| 1. Preview.srt |
5.22KB |
| 1. Section Overview.mp4 |
29.04MB |
| 1. Section Overview.mp4 |
22.52MB |
| 1. Section Overview.mp4 |
18.57MB |
| 1. Section Overview.mp4 |
17.20MB |
| 1. Section Overview.srt |
1.95KB |
| 1. Section Overview.srt |
1.19KB |
| 1. Section Overview.srt |
1.37KB |
| 1. Section Overview.srt |
1.05KB |
| 10 |
47.07KB |
| 10. (Python Practice) Applied Tokenization (33).mp4 |
18.30MB |
| 10. (Python Practice) Applied Tokenization (33).srt |
3.42KB |
| 10. (Python Practice) Dataset Visualization.mp4 |
22.18MB |
| 10. (Python Practice) Dataset Visualization.srt |
3.66KB |
| 10.1 Colab_Notebook_Section_1_completed.ipynb |
78.55KB |
| 11 |
247.76KB |
| 11. Stemming.mp4 |
18.08MB |
| 11. Stemming.srt |
3.15KB |
| 12 |
486.77KB |
| 12. (Python Practice) Applied Stemming.mp4 |
18.78MB |
| 12. (Python Practice) Applied Stemming.srt |
3.31KB |
| 13 |
324.30KB |
| 13. Lemmatization.mp4 |
14.77MB |
| 13. Lemmatization.srt |
2.49KB |
| 14 |
327.27KB |
| 14. (Python Practice) Applied Lemmatization.mp4 |
18.65MB |
| 14. (Python Practice) Applied Lemmatization.srt |
3.87KB |
| 15 |
263.62KB |
| 15. (Python Pratice) Tweet Pre-Processing.mp4 |
8.37MB |
| 15. (Python Pratice) Tweet Pre-Processing.srt |
1.09KB |
| 15.1 Colab_Notebook_Section_2_completed.ipynb |
81.98KB |
| 16 |
38.79KB |
| 17 |
23.96KB |
| 18 |
409.80KB |
| 19 |
459.88KB |
| 2 |
98B |
| 2.1 Section 1 - Theory Deck.pdf |
2.58MB |
| 2.1 Section 2 - Theory Deck.pdf |
1.80MB |
| 2.1 Section 3 - Theory Deck.pdf |
1.53MB |
| 2.1 Section 4 - Theory Deck.pdf |
1.57MB |
| 2. What is Text.mp4 |
20.48MB |
| 2. What is Text.srt |
3.47KB |
| 2. What is Text Normalization.mp4 |
19.55MB |
| 2. What is Text Normalization.srt |
3.73KB |
| 2. Why a model.mp4 |
11.69MB |
| 2. Why a model.srt |
1.69KB |
| 2. Why Representing Text.mp4 |
17.61MB |
| 2. Why Representing Text.srt |
2.57KB |
| 20 |
397.06KB |
| 21 |
471.04KB |
| 22 |
221.82KB |
| 23 |
274.96KB |
| 24 |
361.06KB |
| 25 |
442.59KB |
| 26 |
207.28KB |
| 27 |
432.56KB |
| 28 |
328.33KB |
| 29 |
398.41KB |
| 3 |
157.89KB |
| 3. Logistic Regression.mp4 |
37.45MB |
| 3. Logistic Regression.srt |
7.68KB |
| 3. PositiveNegative Word Frequencies.mp4 |
23.26MB |
| 3. PositiveNegative Word Frequencies.srt |
4.58KB |
| 3. Text Cleaning (12) - Twitter Features.mp4 |
22.18MB |
| 3. Text Cleaning (12) - Twitter Features.srt |
4.20KB |
| 3. What is Text Mining.mp4 |
19.04MB |
| 3. What is Text Mining.srt |
3.10KB |
| 30 |
306.55KB |
| 31 |
109.14KB |
| 32 |
216.89KB |
| 33 |
294.82KB |
| 34 |
321.87KB |
| 35 |
232.75KB |
| 36 |
399.61KB |
| 37 |
379.93KB |
| 38 |
417.68KB |
| 39 |
157.75KB |
| 4 |
34.40KB |
| 4. (Python Practice) Applied PositiveNegative Frequencies.mp4 |
20.96MB |
| 4. (Python Practice) Applied PositiveNegative Frequencies.srt |
3.54KB |
| 4. (Python Practice) Cleaning Twitter Features.mp4 |
38.05MB |
| 4. (Python Practice) Cleaning Twitter Features.srt |
7.98KB |
| 4. ML Model Training.mp4 |
33.84MB |
| 4. ML Model Training.srt |
5.68KB |
| 4. Text Mining and NLP.mp4 |
14.61MB |
| 4. Text Mining and NLP.srt |
2.41KB |
| 40 |
80.30KB |
| 41 |
320.49KB |
| 42 |
132.16KB |
| 43 |
425.92KB |
| 44 |
201.97KB |
| 45 |
255.23KB |
| 46 |
436.85KB |
| 5 |
189.24KB |
| 5. (Python Practice) TrainTest split.mp4 |
16.89MB |
| 5. (Python Practice) TrainTest split.srt |
2.79KB |
| 5. Bag-of-Words.mp4 |
19.60MB |
| 5. Bag-of-Words.srt |
3.45KB |
| 5. Sentiment Analysis.mp4 |
16.29MB |
| 5. Sentiment Analysis.srt |
2.74KB |
| 5. Text Cleaning (22) - General Features.mp4 |
18.73MB |
| 5. Text Cleaning (22) - General Features.srt |
3.51KB |
| 6 |
14.37KB |
| 6. (Python Practice) Applied Bag-of-Words.mp4 |
29.08MB |
| 6. (Python Practice) Applied Bag-of-Words.srt |
5.77KB |
| 6. (Python Practice) Cleaning General Features.mp4 |
30.82MB |
| 6. (Python Practice) Cleaning General Features.srt |
6.56KB |
| 6. (Python Practice) ML Model Fitting.mp4 |
29.49MB |
| 6. (Python Practice) ML Model Fitting.srt |
5.99KB |
| 6. Roadmap.mp4 |
16.19MB |
| 6. Roadmap.srt |
2.74KB |
| 7 |
434.65KB |
| 7. (Python Practice) Google Colab.mp4 |
12.35MB |
| 7. (Python Practice) Google Colab.srt |
3.15KB |
| 7.1 Colab_Notebook.ipynb |
77.50KB |
| 7. Model Performance Measures.mp4 |
33.47MB |
| 7. Model Performance Measures.srt |
7.08KB |
| 7. TF-IDF.mp4 |
23.45MB |
| 7. TF-IDF.srt |
4.70KB |
| 7. Tokenization.mp4 |
26.19MB |
| 7. Tokenization.srt |
5.34KB |
| 8 |
466.01KB |
| 8. (Python Practice) Applied Performance Measures.mp4 |
19.11MB |
| 8. (Python Practice) Applied Performance Measures.srt |
4.01KB |
| 8. (Python Practice) Applied TF-IDF.mp4 |
17.68MB |
| 8. (Python Practice) Applied TF-IDF.srt |
3.36KB |
| 8. (Python Practice) Applied Tokenization (13).mp4 |
12.59MB |
| 8. (Python Practice) Applied Tokenization (13).srt |
2.27KB |
| 8. (Python Practice) Dataset Connection.mp4 |
21.24MB |
| 8. (Python Practice) Dataset Connection.srt |
3.79KB |
| 8.1 Colab_Notebook_Section_3_completed.ipynb |
83.75KB |
| 8.1 Colab_Notebook_Section_4_completed.ipynb |
85.30KB |
| 8.1 tweet_data.csv |
1.75MB |
| 9 |
320.54KB |
| 9. (Python Practice) Applied Tokenization (23).mp4 |
11.92MB |
| 9. (Python Practice) Applied Tokenization (23).srt |
2.36KB |
| 9. (Python Practice) Dataset Overview.mp4 |
16.21MB |
| 9. (Python Practice) Dataset Overview.srt |
2.99KB |
| 9. (Python Practice) Prediction Pipeline.mp4 |
12.63MB |
| 9. (Python Practice) Prediction Pipeline.srt |
2.12KB |
| TutsNode.com.txt |
63B |