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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 001 Introduction.en.srt |
2.64Кб |
| 001 Introduction.mp4 |
38.35Мб |
| 002 Introducing NLP.en.srt |
4.90Кб |
| 002 Introducing NLP.mp4 |
55.55Мб |
| 003 Data Science In The Real World_ Part 1.en.srt |
4.93Кб |
| 003 Data Science In The Real World_ Part 1.mp4 |
41.57Мб |
| 004 Data Science In The Real World_ Part 2.en.srt |
3.61Кб |
| 004 Data Science In The Real World_ Part 2.mp4 |
31.48Мб |
| 005 NLP In The Real World.en.srt |
7.79Кб |
| 005 NLP In The Real World.mp4 |
79.39Мб |
| 006 An Overview of NLP Methods.en.srt |
4.97Кб |
| 006 An Overview of NLP Methods.mp4 |
34.09Мб |
| 006 NLP-pipelineSLides.pdf |
7.55Мб |
| 006 NLP-Pipleline-NSS.mp4 |
2.43Мб |
| 007 Text Preprocessing.en.srt |
8.54Кб |
| 007 Text Preprocessing.mp4 |
73.78Мб |
| 008 Text Normalization.en.srt |
1.26Кб |
| 008 Text Normalization.mp4 |
11.60Мб |
| 009 Word Embeddings.en.srt |
10.71Кб |
| 009 Word Embeddings.mp4 |
98.44Мб |
| 010 Build a Model, Transfer Learning, Testing & Evaluating a Model.en.srt |
11.13Кб |
| 010 Build a Model, Transfer Learning, Testing & Evaluating a Model.mp4 |
73.25Мб |
| 011 Top Programming Languages Used In Industry 2020.en.srt |
9.17Кб |
| 011 Top Programming Languages Used In Industry 2020.mp4 |
82.14Мб |
| 012 Top Programming Languages Used In Industry 2020 Part 2_ PHP.en.srt |
1.86Кб |
| 012 Top Programming Languages Used In Industry 2020 Part 2_ PHP.mp4 |
16.52Мб |
| 013 Python in Industry 2020.en.srt |
4.51Кб |
| 013 Python in Industry 2020.mp4 |
42.38Мб |
| 014 Python vs R For Data Science & NLP.en.srt |
5.46Кб |
| 014 Python vs R For Data Science & NLP.mp4 |
47.69Мб |
| 015 Open A New Colab Notebook.en.srt |
1.38Кб |
| 015 Open A New Colab Notebook.mp4 |
15.12Мб |
| 016 Open .IPYNB Files in Google Colab & Find The Resource Folders For This Course.en.srt |
2.55Кб |
| 016 Open .IPYNB Files in Google Colab & Find The Resource Folders For This Course.mp4 |
20.53Мб |
| 017 What is Tokenization_ Introduction to the Linguistic theory for tokenization.en.srt |
2.33Кб |
| 017 What is Tokenization_ Introduction to the Linguistic theory for tokenization.mp4 |
10.82Мб |
| 018 Linguistic theory for Word Segmentation.en.srt |
3.42Кб |
| 018 Linguistic theory for Word Segmentation.mp4 |
14.45Мб |
| 019 How To Open The .IPYNB file For The Next Lecture (Optional).en.srt |
2.55Кб |
| 019 How To Open The .IPYNB file For The Next Lecture (Optional).mp4 |
20.50Мб |
| 020 Codealong-TokenizationNLTK.ipynb |
2.59Кб |
| 020 TokenizationNLTK-complete.ipynb |
4.70Кб |
| 020 Tokenization with NLTK.en.srt |
6.17Кб |
| 020 Tokenization with NLTK.mp4 |
36.36Мб |
| 021 Introducing Regular Expressions.en.srt |
4.40Кб |
| 021 Introducing Regular Expressions.mp4 |
19.87Мб |
| 022 Word Segmentation using Python's .split().en.srt |
3.12Кб |
| 022 Word Segmentation using Python's .split().mp4 |
19.56Мб |
| 023 Sentence Segmentation using Python's .split.en.srt |
4.46Кб |
| 023 Sentence Segmentation using Python's .split.mp4 |
28.62Мб |
| 024 Codealong-ReGex.ipynb |
6.04Кб |
| 024 ReGex Split Method re.split() Regular Expressions.en.srt |
4.25Кб |
| 024 ReGex Split Method re.split() Regular Expressions.mp4 |
32.85Мб |
| 025 Regex Substitute Method re.sub Regular Expressions.en.srt |
6.12Кб |
| 025 Regex Substitute Method re.sub Regular Expressions.mp4 |
38.17Мб |
| 026 Search Method using Regex re.search _ Regular Expressions.en.srt |
5.97Кб |
| 026 Search Method using Regex re.search _ Regular Expressions.mp4 |
43.30Мб |
| 027 Part 1_ Find All Emails in Contact Details _ Regular Expressions re.findall().en.srt |
6.09Кб |
| 027 Part 1_ Find All Emails in Contact Details _ Regular Expressions re.findall().mp4 |
46.17Мб |
| 028 Codealong-ReGex-Complete.ipynb |
9.71Кб |
| 028 Part 2_ Find All Emails in Contact Details _ Regular Expressions re.findall().en.srt |
6.38Кб |
| 028 Part 2_ Find All Emails in Contact Details _ Regular Expressions re.findall().mp4 |
43.04Мб |
| 029 What is a Stemming_.en.srt |
6.51Кб |
| 029 What is a Stemming_.mp4 |
31.91Мб |
| 030 stemming-lemma.ipynb |
5.47Кб |
| 030 stemming-lemma-complete.ipynb |
9.85Кб |
| 030 Stemming with 3 NLTK Methods - Practical.en.srt |
14.43Кб |
| 030 Stemming with 3 NLTK Methods - Practical.mp4 |
92.27Мб |
| 031 Comparing Stemming Methods_ Porter, Lancaster & Snowball.en.srt |
14.04Кб |
| 031 Comparing Stemming Methods_ Porter, Lancaster & Snowball.mp4 |
62.94Мб |
| 032 What is Lemmatization_.en.srt |
11.55Кб |
| 032 What is Lemmatization_.mp4 |
57.51Мб |
| 033 Lemmatization.ipynb |
7.14Кб |
| 033 Lemmatization with NLTK - Practical.en.srt |
9.44Кб |
| 033 Lemmatization with NLTK - Practical.mp4 |
58.27Мб |
| 034 Wordnet Resource.html |
1.34Кб |
| 035 Part 2 Lemmatization with NLTK.en.srt |
7.85Кб |
| 035 Part 2 Lemmatization with NLTK.mp4 |
49.23Мб |
| 036 Part-of-Speech & Lemmatization Precision.en.srt |
14.45Кб |
| 036 Part-of-Speech & Lemmatization Precision.mp4 |
108.32Мб |
| 037 Introducing The Project_ Preprocessing Tweets.en.srt |
8.88Кб |
| 037 Introducing The Project_ Preprocessing Tweets.mp4 |
69.18Мб |
| 038 Coachella-E5-2-DFE.csv |
640.81Кб |
| 038 coachella-tweets.ipynb |
256.63Кб |
| 038 coachella-tweetsComplete.ipynb |
282.25Кб |
| 038 Part 1_ Preprocess Tweets Practical_ Load & Examine Dataset.en.srt |
15.24Кб |
| 038 Part 1_ Preprocess Tweets Practical_ Load & Examine Dataset.mp4 |
132.97Мб |
| 039 Part 2_ Extract Hashtags - Preprocess Tweets Practical.en.srt |
5.35Кб |
| 039 Part 2_ Extract Hashtags - Preprocess Tweets Practical.mp4 |
43.61Мб |
| 040 Part 3_ Remove Usernames, Links, Non-ASCII & Use lower() - Tweets Practical.en.srt |
12.06Кб |
| 040 Part 3_ Remove Usernames, Links, Non-ASCII & Use lower() - Tweets Practical.mp4 |
92.06Мб |
| 041 Part 4_ Try Non-ASCII & Lower Case Functions on Sample Text.en.srt |
4.10Кб |
| 041 Part 4_ Try Non-ASCII & Lower Case Functions on Sample Text.mp4 |
23.41Мб |
| 042 Part 5_ Stopwords Removal.en.srt |
10.87Кб |
| 042 Part 5_ Stopwords Removal.mp4 |
45.29Мб |
| 043 Part 6_ Remove Email Addresses.en.srt |
5.69Кб |
| 043 Part 6_ Remove Email Addresses.mp4 |
33.08Мб |
| 044 Part 7_ Remove Digits & Special Characters.en.srt |
12.64Кб |
| 044 Part 7_ Remove Digits & Special Characters.mp4 |
57.33Мб |
| 045 Part 8_ Clean Tweets In Dataset.en.srt |
33.03Кб |
| 045 Part 8_ Clean Tweets In Dataset.mp4 |
233.94Мб |
| 046 classification-steamreviews.ipynb |
547.55Кб |
| 046 Part 1 _ Steam Game Reviews Project _ Classifier for Sentiment Analysis.en.srt |
8.72Кб |
| 046 Part 1 _ Steam Game Reviews Project _ Classifier for Sentiment Analysis.mp4 |
42.64Мб |
| 046 steamreviews.zip |
4.99Мб |
| 047 Part 2_ Steam Game Reviews Classifier _ Explore Dataset.en.srt |
15.48Кб |
| 047 Part 2_ Steam Game Reviews Classifier _ Explore Dataset.mp4 |
119.94Мб |
| 048 Part 3_ Build Classifier _ Steam Game Reviews.en.srt |
7.25Кб |
| 048 Part 3_ Build Classifier _ Steam Game Reviews.mp4 |
73.37Мб |
| 049 Part 4 _ Split & Format Training Data _ Steam Game Reviews _.en.srt |
14.66Кб |
| 049 Part 4 _ Split & Format Training Data _ Steam Game Reviews _.mp4 |
141.48Мб |
| 050 Part 5 _ Prepare Training Data _ Steam Game Reviews _.en.srt |
6.14Кб |
| 050 Part 5 _ Prepare Training Data _ Steam Game Reviews _.mp4 |
52.33Мб |
| 051 Part 6 _ Train the Model _ Steam Game Reviews _.en.srt |
8.08Кб |
| 051 Part 6 _ Train the Model _ Steam Game Reviews _.mp4 |
77.55Мб |
| 052 Part 7_ Testing the Model _ Steam Game Reviews.en.srt |
12.55Кб |
| 052 Part 7_ Testing the Model _ Steam Game Reviews.mp4 |
67.75Мб |
| 053 Complete-Netflix-Word2vec.ipynb |
72.63Кб |
| 053 netflix.zip |
970.64Кб |
| 053 Netflix-Word2vec.ipynb |
14.56Кб |
| 053 Part 1_ Netflix Recommendation Project_ Data Exploration.en.srt |
21.97Кб |
| 053 Part 1_ Netflix Recommendation Project_ Data Exploration.mp4 |
163.23Мб |
| 054 Part 2_ Preprocessing _ Netflix Recommendation Project.en.srt |
11.15Кб |
| 054 Part 2_ Preprocessing _ Netflix Recommendation Project.mp4 |
107.82Мб |
| 055 google-embed.rtf |
732б |
| 055 Part 3_ Pre-trained Data _ Netflix Recommendation System.en.srt |
12.87Кб |
| 055 Part 3_ Pre-trained Data _ Netflix Recommendation System.mp4 |
125.02Мб |
| 056 Part 4_ Examine Similarities with most_similar Function.en.srt |
6.11Кб |
| 056 Part 4_ Examine Similarities with most_similar Function.mp4 |
53.29Мб |
| 057 Part 5_ Write Vectorize() Function _ Netflix Recommendation System.en.srt |
5.01Кб |
| 057 Part 5_ Write Vectorize() Function _ Netflix Recommendation System.mp4 |
47.52Мб |
| 058 Part 6_ Make function to Get Most Similar Shows _ Netflix Recommendation Project.en.srt |
7.42Кб |
| 058 Part 6_ Make function to Get Most Similar Shows _ Netflix Recommendation Project.mp4 |
71.17Мб |
| 059 Part 7_ Sorted() Function.en.srt |
7.36Кб |
| 059 Part 7_ Sorted() Function.mp4 |
48.89Мб |
| 060 Part 8_ Final Recommendation Output.en.srt |
10.01Кб |
| 060 Part 8_ Final Recommendation Output.mp4 |
59.77Мб |
| 061 BBC-08-APR-17-to-08-JUN-E7.csv |
2.74Мб |
| 061 BBC News NMF Part 1_ Explore Dataset.en.srt |
17.34Кб |
| 061 BBC News NMF Part 1_ Explore Dataset.mp4 |
110.75Мб |
| 061 topicmodel-nmf-bbc.ipynb |
9.80Кб |
| 062 BBC News NMF Part 2_ Preprocessing.en.srt |
5.14Кб |
| 062 BBC News NMF Part 2_ Preprocessing.mp4 |
45.18Мб |
| 063 BBC News NMF Part 3_ Extract Topics.en.srt |
21.87Кб |
| 063 BBC News NMF Part 3_ Extract Topics.mp4 |
181.75Мб |
| 064 BBC News NMF Part 4_ Assign Topics.en.srt |
12.22Кб |
| 064 BBC News NMF Part 4_ Assign Topics.mp4 |
106.19Мб |
| 065 BBC News NMF Part 5_ Create Filtered Dataset, With Only The Articles Needed.en.srt |
12.95Кб |
| 065 BBC News NMF Part 5_ Create Filtered Dataset, With Only The Articles Needed.mp4 |
97.48Мб |
| 066 BBC News NMF Part 6_ Wordcloud With Filtered Articles.en.srt |
5.32Кб |
| 066 BBC News NMF Part 6_ Wordcloud With Filtered Articles.mp4 |
40.28Мб |
| 066 India-News.zip |
77.47Мб |
| 066 indiatimes.jpg |
27.66Кб |
| 067 Neural Networks Overview.en.srt |
2.26Кб |
| 067 Neural Networks Overview.mp4 |
6.83Мб |
| 068 Machine Learning Overview.en.srt |
12.14Кб |
| 068 Machine Learning Overview.mp4 |
43.46Мб |
| 069 Neural Networks Explained.en.srt |
5.77Кб |
| 069 Neural Networks Explained.mp4 |
19.93Мб |
| 070 Forward Propagation.en.srt |
12.21Кб |
| 070 Forward Propagation.mp4 |
54.44Мб |
| 071 Activation Functions.en.srt |
12.50Кб |
| 071 Activation Functions.mp4 |
50.15Мб |
| 072 Model Training_ Part 1 - Loss Functions.en.srt |
12.58Кб |
| 072 Model Training_ Part 1 - Loss Functions.mp4 |
49.23Мб |
| 073 Model Training_ Part 2 - Backpropagation.en.srt |
14.21Кб |
| 073 Model Training_ Part 2 - Backpropagation.mp4 |
59.95Мб |
| 074 Learning Rates_ With Explained Example.en.srt |
18.84Кб |
| 074 Learning Rates_ With Explained Example.mp4 |
86.19Мб |
| 075 Model Testing_ Overfitting.en.srt |
22.77Кб |
| 075 Model Testing_ Overfitting.mp4 |
98.34Мб |
| 076 Iterations of The Model.en.srt |
5.24Кб |
| 076 Iterations of The Model.mp4 |
21.61Мб |
| 077 Evaluating A Model.en.srt |
10.49Кб |
| 077 Evaluating A Model.mp4 |
43.47Мб |
| 078 Overview For Getting Good Model Performance.en.srt |
6.31Кб |
| 078 Overview For Getting Good Model Performance.mp4 |
21.72Мб |
| 079 Chatbot #1_ Part1 - Rule-Based For Hard-Coded Exact Matching.en.srt |
1.73Кб |
| 079 Chatbot #1_ Part1 - Rule-Based For Hard-Coded Exact Matching.mp4 |
13.77Мб |
| 079 chatbot-rule.ipynb |
5.28Кб |
| 080 Chatbot #1_ Part 2 - Rule-Based For Hard-Coded Exact Matching.en.srt |
11.83Кб |
| 080 Chatbot #1_ Part 2 - Rule-Based For Hard-Coded Exact Matching.mp4 |
84.12Мб |
| 080 chatbot-rule-complete.ipynb |
7.14Кб |
| 081 Chatbot #2_ Rule-Based Using Keywords.en.srt |
19.37Кб |
| 081 Chatbot #2_ Rule-Based Using Keywords.mp4 |
148.47Мб |
| 081 chatbot-rule.ipynb |
5.28Кб |
| 081 chatbot-rule-complete2.ipynb |
9.38Кб |
| 082 fakenews.zip |
37.04Мб |
| 082 Fake-News-Detector-LSTM.ipynb |
13.31Кб |
| 082 Fake-News-Detector-LSTM-Complete.ipynb |
53.93Кб |
| 082 FakeNews LSTM Part 1_ Import Libraries, Load Dataset.en.srt |
4.19Кб |
| 082 FakeNews LSTM Part 1_ Import Libraries, Load Dataset.mp4 |
34.07Мб |
| 083 FakeNews LSTM Part 2_ Remove Null Values.en.srt |
7.21Кб |
| 083 FakeNews LSTM Part 2_ Remove Null Values.mp4 |
63.79Мб |
| 084 FakeNews LSTM Part3_ Preprocess Data.en.srt |
9.91Кб |
| 084 FakeNews LSTM Part3_ Preprocess Data.mp4 |
90.94Мб |
| 085 Jetsons Cartoon, Google Assistant_ NLP & Sound Recognition.en.srt |
6.37Кб |
| 085 Jetsons Cartoon, Google Assistant_ NLP & Sound Recognition.mp4 |
66.75Мб |
| 086 audio-text.ipynb |
6.04Кб |
| 086 Convert Speech to Text - Load Resource File.en.srt |
1.06Кб |
| 086 Convert Speech to Text - Load Resource File.mp4 |
8.04Мб |
| 086 Merry-Christmas-SoundBible.com-1120316507.wav |
861.37Кб |
| 087 Part 1_ Convert Speech to Text.en.srt |
6.17Кб |
| 087 Part 1_ Convert Speech to Text.mp4 |
55.23Мб |
| 088 Part2_ Recognise Speech & Convert to Text.en.srt |
7.16Кб |
| 088 Part2_ Recognise Speech & Convert to Text.mp4 |
64.66Мб |
| 089 Why Python for Data Science_.en.srt |
4.74Кб |
| 089 Why Python for Data Science_.mp4 |
19.15Мб |
| 090 Python_ Variables.en.srt |
9.66Кб |
| 090 Python_ Variables.mp4 |
27.14Мб |
| 090 Python-Crash-Course.ipynb |
27.57Кб |
| 091 Python_ Lists & Dictionaries.en.srt |
15.44Кб |
| 091 Python_ Lists & Dictionaries.mp4 |
56.36Мб |
| 092 Python_ Conditionals.en.srt |
9.49Кб |
| 092 Python_ Conditionals.mp4 |
30.53Мб |
| 093 Python_ Loops.en.srt |
11.65Кб |
| 093 Python_ Loops.mp4 |
36.29Мб |
| 094 Python_ Functions.en.srt |
7.88Кб |
| 094 Python_ Functions.mp4 |
22.20Мб |
| 095 Python_ Classes.en.srt |
11.47Кб |
| 095 Python_ Classes.mp4 |
39.55Мб |
| TutsNode.com.txt |
63б |