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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 |
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274.96KB |
24 |
361.06KB |
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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 |
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294.82KB |
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321.87KB |
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232.75KB |
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399.61KB |
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379.93KB |
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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 |
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80.30KB |
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320.49KB |
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132.16KB |
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425.92KB |
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201.97KB |
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255.23KB |
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436.85KB |
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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 |
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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 |
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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 |
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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 |