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A Complete Natural Language Processing Beginner Masterclass
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A Complete Natural Language Processing Beginner Masterclass (Size: 5.56 GB)
| TutsNode.com.txt | 63 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 585 B | ||
| [TutsNode.com] - A Complete Natural Language Processing Beginner Masterclass | |||
| 01 Introduction | |||
| 001 Introduction.en.srt | 2.64 KB | ||
| 001 Introduction.mp4 | 38.35 MB | ||
| 02 Intro_ NLP, Data Science & Machine Learning - Are they different_ | |||
| 002 Introducing NLP.en.srt | 4.9 KB | ||
| 002 Introducing NLP.mp4 | 55.55 MB | ||
| 003 Data Science In The Real World_ Part 1.en.srt | 4.93 KB | ||
| 003 Data Science In The Real World_ Part 1.mp4 | 41.57 MB | ||
| 004 Data Science In The Real World_ Part 2.en.srt | 3.61 KB | ||
| 004 Data Science In The Real World_ Part 2.mp4 | 31.48 MB | ||
| 005 NLP In The Real World.en.srt | 7.79 KB | ||
| 005 NLP In The Real World.mp4 | 79.39 MB | ||
| 03 NLP Pipeline _Must Watch Section | |||
| 006 An Overview of NLP Methods.en.srt | 4.97 KB | ||
| 006 An Overview of NLP Methods.mp4 | 34.09 MB | ||
| 006 NLP-Pipleline-NSS.mp4 | 2.43 MB | ||
| 006 NLP-pipelineSLides.pdf | 7.55 MB | ||
| 007 Text Preprocessing.en.srt | 8.54 KB | ||
| 007 Text Preprocessing.mp4 | 73.78 MB | ||
| 008 Text Normalization.en.srt | 1.26 KB | ||
| 008 Text Normalization.mp4 | 11.6 MB | ||
| 009 Word Embeddings.en.srt | 10.71 KB | ||
| 009 Word Embeddings.mp4 | 98.44 MB | ||
| 010 Build a Model, Transfer Learning, Testing & Evaluating a Model.en.srt | 11.13 KB | ||
| 010 Build a Model, Transfer Learning, Testing & Evaluating a Model.mp4 | 73.25 MB | ||
| 04 Why Learn Python for NLP & Data Science_ | |||
| 011 Top Programming Languages Used In Industry 2020.en.srt | 9.17 KB | ||
| 011 Top Programming Languages Used In Industry 2020.mp4 | 82.14 MB | ||
| 012 Top Programming Languages Used In Industry 2020 Part 2_ PHP.en.srt | 1.86 KB | ||
| 012 Top Programming Languages Used In Industry 2020 Part 2_ PHP.mp4 | 16.52 MB | ||
| 013 Python in Industry 2020.en.srt | 4.51 KB | ||
| 013 Python in Industry 2020.mp4 | 42.38 MB | ||
| 014 Python vs R For Data Science & NLP.en.srt | 5.46 KB | ||
| 014 Python vs R For Data Science & NLP.mp4 | 47.69 MB | ||
| 05 Google Colab - Setting Up | |||
| 015 Open A New Colab Notebook.en.srt | 1.38 KB | ||
| 015 Open A New Colab Notebook.mp4 | 15.12 MB | ||
| 016 Open .IPYNB Files in Google Colab & Find The Resource Folders For This Course.en.srt | 2.55 KB | ||
| 016 Open .IPYNB Files in Google Colab & Find The Resource Folders For This Course.mp4 | 20.53 MB | ||
| 06 Tokenization & Regular Expressions | |||
| 017 What is Tokenization_ Introduction to the Linguistic theory for tokenization.en.srt | 2.33 KB | ||
| 017 What is Tokenization_ Introduction to the Linguistic theory for tokenization.mp4 | 10.82 MB | ||
| 018 Linguistic theory for Word Segmentation.en.srt | 3.42 KB | ||
| 018 Linguistic theory for Word Segmentation.mp4 | 14.45 MB | ||
| 019 How To Open The .IPYNB file For The Next Lecture (Optional).en.srt | 2.55 KB | ||
| 019 How To Open The .IPYNB file For The Next Lecture (Optional).mp4 | 20.5 MB | ||
| 020 Codealong-TokenizationNLTK.ipynb | 2.59 KB | ||
| 020 Tokenization with NLTK.en.srt | 6.17 KB | ||
| 020 Tokenization with NLTK.mp4 | 36.36 MB | ||
| 020 TokenizationNLTK-complete.ipynb | 4.7 KB | ||
| 021 Introducing Regular Expressions.en.srt | 4.4 KB | ||
| 021 Introducing Regular Expressions.mp4 | 19.87 MB | ||
| 022 Word Segmentation using Python's .split().en.srt | 3.12 KB | ||
| 022 Word Segmentation using Python's .split().mp4 | 19.56 MB | ||
| 023 Sentence Segmentation using Python's .split.en.srt | 4.46 KB | ||
| 023 Sentence Segmentation using Python's .split.mp4 | 28.62 MB | ||
| 024 Codealong-ReGex.ipynb | 6.04 KB | ||
| 024 ReGex Split Method re.split() Regular Expressions.en.srt | 4.25 KB | ||
| 024 ReGex Split Method re.split() Regular Expressions.mp4 | 32.85 MB | ||
| 025 Regex Substitute Method re.sub Regular Expressions.en.srt | 6.12 KB | ||
| 025 Regex Substitute Method re.sub Regular Expressions.mp4 | 38.17 MB | ||
| 026 Search Method using Regex re.search _ Regular Expressions.en.srt | 5.97 KB | ||
| 026 Search Method using Regex re.search _ Regular Expressions.mp4 | 43.3 MB | ||
| 027 Part 1_ Find All Emails in Contact Details _ Regular Expressions re.findall().en.srt | 6.09 KB | ||
| 027 Part 1_ Find All Emails in Contact Details _ Regular Expressions re.findall().mp4 | 46.17 MB | ||
| 028 Codealong-ReGex-Complete.ipynb | 9.71 KB | ||
| 028 Part 2_ Find All Emails in Contact Details _ Regular Expressions re.findall().en.srt | 6.38 KB | ||
| 028 Part 2_ Find All Emails in Contact Details _ Regular Expressions re.findall().mp4 | 43.04 MB | ||
| 07 Stemming & Lemmatization | |||
| 029 What is a Stemming_.en.srt | 6.51 KB | ||
| 029 What is a Stemming_.mp4 | 31.91 MB | ||
| 030 Stemming with 3 NLTK Methods - Practical.en.srt | 14.43 KB | ||
| 030 Stemming with 3 NLTK Methods - Practical.mp4 | 92.27 MB | ||
| 030 stemming-lemma-complete.ipynb | 9.85 KB | ||
| 030 stemming-lemma.ipynb | 5.47 KB | ||
| 031 Comparing Stemming Methods_ Porter, Lancaster & Snowball.en.srt | 14.04 KB | ||
| 031 Comparing Stemming Methods_ Porter, Lancaster & Snowball.mp4 | 62.94 MB | ||
| 032 What is Lemmatization_.en.srt | 11.55 KB | ||
| 032 What is Lemmatization_.mp4 | 57.51 MB | ||
| 033 Lemmatization with NLTK - Practical.en.srt | 9.44 KB | ||
| 033 Lemmatization with NLTK - Practical.mp4 | 58.27 MB | ||
| 033 Lemmatization.ipynb | 7.14 KB | ||
| 034 Wordnet Resource.html | 1.34 KB | ||
| 035 Part 2 Lemmatization with NLTK.en.srt | 7.85 KB | ||
| 035 Part 2 Lemmatization with NLTK.mp4 | 49.23 MB | ||
| 036 Part-of-Speech & Lemmatization Precision.en.srt | 14.45 KB | ||
| 036 Part-of-Speech & Lemmatization Precision.mp4 | 108.32 MB | ||
| 08 Text Preprocessing_ Detailed Step-By-Step Practical Examples | |||
| 037 Introducing The Project_ Preprocessing Tweets.en.srt | 8.88 KB | ||
| 037 Introducing The Project_ Preprocessing Tweets.mp4 | 69.18 MB | ||
| 038 Coachella-E5-2-DFE.csv | 640.81 KB | ||
| 038 Part 1_ Preprocess Tweets Practical_ Load & Examine Dataset.en.srt | 15.24 KB | ||
| 038 Part 1_ Preprocess Tweets Practical_ Load & Examine Dataset.mp4 | 132.97 MB | ||
| 038 coachella-tweets.ipynb | 256.63 KB | ||
| 038 coachella-tweetsComplete.ipynb | 282.25 KB | ||
| 039 Part 2_ Extract Hashtags - Preprocess Tweets Practical.en.srt | 5.35 KB | ||
| 039 Part 2_ Extract Hashtags - Preprocess Tweets Practical.mp4 | 43.61 MB | ||
| 040 Part 3_ Remove Usernames, Links, Non-ASCII & Use lower() - Tweets Practical.en.srt | 12.06 KB | ||
| 040 Part 3_ Remove Usernames, Links, Non-ASCII & Use lower() - Tweets Practical.mp4 | 92.06 MB | ||
| 041 Part 4_ Try Non-ASCII & Lower Case Functions on Sample Text.en.srt | 4.1 KB | ||
| 041 Part 4_ Try Non-ASCII & Lower Case Functions on Sample Text.mp4 | 23.41 MB | ||
| 042 Part 5_ Stopwords Removal.en.srt | 10.87 KB | ||
| 042 Part 5_ Stopwords Removal.mp4 | 45.29 MB | ||
| 043 Part 6_ Remove Email Addresses.en.srt | 5.69 KB | ||
| 043 Part 6_ Remove Email Addresses.mp4 | 33.08 MB | ||
| 044 Part 7_ Remove Digits & Special Characters.en.srt | 12.64 KB | ||
| 044 Part 7_ Remove Digits & Special Characters.mp4 | 57.33 MB | ||
| 045 Part 8_ Clean Tweets In Dataset.en.srt | 33.03 KB | ||
| 045 Part 8_ Clean Tweets In Dataset.mp4 | 233.94 MB | ||
| 09 Text Classification Used For Sentiment Analysis | |||
| 046 Part 1 _ Steam Game Reviews Project _ Classifier for Sentiment Analysis.en.srt | 8.72 KB | ||
| 046 Part 1 _ Steam Game Reviews Project _ Classifier for Sentiment Analysis.mp4 | 42.64 MB | ||
| 046 classification-steamreviews.ipynb | 547.55 KB | ||
| 046 steamreviews.zip | 4.99 MB | ||
| 047 Part 2_ Steam Game Reviews Classifier _ Explore Dataset.en.srt | 15.48 KB | ||
| 047 Part 2_ Steam Game Reviews Classifier _ Explore Dataset.mp4 | 119.94 MB | ||
| 048 Part 3_ Build Classifier _ Steam Game Reviews.en.srt | 7.25 KB | ||
| 048 Part 3_ Build Classifier _ Steam Game Reviews.mp4 | 73.37 MB | ||
| 049 Part 4 _ Split & Format Training Data _ Steam Game Reviews _.en.srt | 14.66 KB | ||
| 049 Part 4 _ Split & Format Training Data _ Steam Game Reviews _.mp4 | 141.48 MB | ||
| 050 Part 5 _ Prepare Training Data _ Steam Game Reviews _.en.srt | 6.14 KB | ||
| 050 Part 5 _ Prepare Training Data _ Steam Game Reviews _.mp4 | 52.33 MB | ||
| 051 Part 6 _ Train the Model _ Steam Game Reviews _.en.srt | 8.08 KB | ||
| 051 Part 6 _ Train the Model _ Steam Game Reviews _.mp4 | 77.55 MB | ||
| 052 Part 7_ Testing the Model _ Steam Game Reviews.en.srt | 12.55 KB | ||
| 052 Part 7_ Testing the Model _ Steam Game Reviews.mp4 | 67.75 MB | ||
| 10 Word Embedding_ Word2Vec | |||
| 053 Complete-Netflix-Word2vec.ipynb | 72.63 KB | ||
| 053 Netflix-Word2vec.ipynb | 14.56 KB | ||
| 053 Part 1_ Netflix Recommendation Project_ Data Exploration.en.srt | 21.97 KB | ||
| 053 Part 1_ Netflix Recommendation Project_ Data Exploration.mp4 | 163.23 MB | ||
| 053 netflix.zip | 970.64 KB | ||
| 054 Part 2_ Preprocessing _ Netflix Recommendation Project.en.srt | 11.15 KB | ||
| 054 Part 2_ Preprocessing _ Netflix Recommendation Project.mp4 | 107.82 MB | ||
| 055 Part 3_ Pre-trained Data _ Netflix Recommendation System.en.srt | 12.87 KB | ||
| 055 Part 3_ Pre-trained Data _ Netflix Recommendation System.mp4 | 125.02 MB | ||
| 055 google-embed.rtf | 732 B | ||
| 056 Part 4_ Examine Similarities with most_similar Function.en.srt | 6.11 KB | ||
| 056 Part 4_ Examine Similarities with most_similar Function.mp4 | 53.29 MB | ||
| 057 Part 5_ Write Vectorize() Function _ Netflix Recommendation System.en.srt | 5.01 KB | ||
| 057 Part 5_ Write Vectorize() Function _ Netflix Recommendation System.mp4 | 47.52 MB | ||
| 058 Part 6_ Make function to Get Most Similar Shows _ Netflix Recommendation Project.en.srt | 7.42 KB | ||
| 058 Part 6_ Make function to Get Most Similar Shows _ Netflix Recommendation Project.mp4 | 71.17 MB | ||
| 059 Part 7_ Sorted() Function.en.srt | 7.36 KB | ||
| 059 Part 7_ Sorted() Function.mp4 | 48.89 MB | ||
| 060 Part 8_ Final Recommendation Output.en.srt | 10.01 KB | ||
| 060 Part 8_ Final Recommendation Output.mp4 | 59.77 MB | ||
| 11 Topic Modelling_ NMF with Sklearn | |||
| 061 BBC News NMF Part 1_ Explore Dataset.en.srt | 17.34 KB | ||
| 061 BBC News NMF Part 1_ Explore Dataset.mp4 | 110.75 MB | ||
| 061 BBC-08-APR-17-to-08-JUN-E7.csv | 2.74 MB | ||
| 061 topicmodel-nmf-bbc.ipynb | 9.8 KB | ||
| 062 BBC News NMF Part 2_ Preprocessing.en.srt | 5.14 KB | ||
| 062 BBC News NMF Part 2_ Preprocessing.mp4 | 45.18 MB | ||
| 063 BBC News NMF Part 3_ Extract Topics.en.srt | 21.87 KB | ||
| 063 BBC News NMF Part 3_ Extract Topics.mp4 | 181.75 MB | ||
| 064 BBC News NMF Part 4_ Assign Topics.en.srt | 12.22 KB | ||
| 064 BBC News NMF Part 4_ Assign Topics.mp4 | 106.19 MB | ||
| 065 BBC News NMF Part 5_ Create Filtered Dataset, With Only The Articles Needed.en.srt | 12.95 KB | ||
| 065 BBC News NMF Part 5_ Create Filtered Dataset, With Only The Articles Needed.mp4 | 97.48 MB | ||
| 066 BBC News NMF Part 6_ Wordcloud With Filtered Articles.en.srt | 5.32 KB | ||
| 066 BBC News NMF Part 6_ Wordcloud With Filtered Articles.mp4 | 40.28 MB | ||
| 066 India-News.zip | 77.47 MB | ||
| 066 indiatimes.jpg?042148 | 27.66 KB | ||
| 12 Deep Learning & Neural Networks Explained | |||
| 067 Neural Networks Overview.en.srt | 2.26 KB | ||
| 067 Neural Networks Overview.mp4 | 6.83 MB | ||
| 068 Machine Learning Overview.en.srt | 12.14 KB | ||
| 068 Machine Learning Overview.mp4 | 43.46 MB | ||
| 069 Neural Networks Explained.en.srt | 5.77 KB | ||
| 069 Neural Networks Explained.mp4 | 19.93 MB | ||
| 070 Forward Propagation.en.srt | 12.21 KB | ||
| 070 Forward Propagation.mp4 | 54.44 MB | ||
| 071 Activation Functions.en.srt | 12.5 KB | ||
| 071 Activation Functions.mp4 | 50.15 MB | ||
| 072 Model Training_ Part 1 - Loss Functions.en.srt | 12.58 KB | ||
| 072 Model Training_ Part 1 - Loss Functions.mp4 | 49.23 MB | ||
| 073 Model Training_ Part 2 - Backpropagation.en.srt | 14.21 KB | ||
| 073 Model Training_ Part 2 - Backpropagation.mp4 | 59.95 MB | ||
| 074 Learning Rates_ With Explained Example.en.srt | 18.84 KB | ||
| 074 Learning Rates_ With Explained Example.mp4 | 86.19 MB | ||
| 075 Model Testing_ Overfitting.en.srt | 22.77 KB | ||
| 075 Model Testing_ Overfitting.mp4 | 98.34 MB | ||
| 076 Iterations of The Model.en.srt | 5.24 KB | ||
| 076 Iterations of The Model.mp4 | 21.61 MB | ||
| 077 Evaluating A Model.en.srt | 10.49 KB | ||
| 077 Evaluating A Model.mp4 | 43.47 MB | ||
| 078 Overview For Getting Good Model Performance.en.srt | 6.31 KB | ||
| 078 Overview For Getting Good Model Performance.mp4 | 21.72 MB | ||
| 13 Rule-Based Chatbot for Banking Customer Service | |||
| 079 Chatbot #1_ Part1 - Rule-Based For Hard-Coded Exact Matching.en.srt | 1.73 KB | ||
| 079 Chatbot #1_ Part1 - Rule-Based For Hard-Coded Exact Matching.mp4 | 13.77 MB | ||
| 079 chatbot-rule.ipynb | 5.28 KB | ||
| 080 Chatbot #1_ Part 2 - Rule-Based For Hard-Coded Exact Matching.en.srt | 11.83 KB | ||
| 080 Chatbot #1_ Part 2 - Rule-Based For Hard-Coded Exact Matching.mp4 | 84.12 MB | ||
| 080 chatbot-rule-complete.ipynb | 7.14 KB | ||
| 081 Chatbot #2_ Rule-Based Using Keywords.en.srt | 19.37 KB | ||
| 081 Chatbot #2_ Rule-Based Using Keywords.mp4 | 148.47 MB | ||
| 081 chatbot-rule-complete2.ipynb | 9.38 KB | ||
| 081 chatbot-rule.ipynb | 5.28 KB | ||
| 14 Deep Learning_ Build LSTM Model for Fake News Detection | |||
| 082 Fake-News-Detector-LSTM-Complete.ipynb | 53.93 KB | ||
| 082 Fake-News-Detector-LSTM.ipynb | 13.31 KB | ||
| 082 FakeNews LSTM Part 1_ Import Libraries, Load Dataset.en.srt | 4.19 KB | ||
| 082 FakeNews LSTM Part 1_ Import Libraries, Load Dataset.mp4 | 34.07 MB | ||
| 082 fakenews.zip | 37.04 MB | ||
| 083 FakeNews LSTM Part 2_ Remove Null Values.en.srt | 7.21 KB | ||
| 083 FakeNews LSTM Part 2_ Remove Null Values.mp4 | 63.79 MB | ||
| 084 FakeNews LSTM Part3_ Preprocess Data.en.srt | 9.91 KB | ||
| 084 FakeNews LSTM Part3_ Preprocess Data.mp4 | 90.94 MB | ||
| 15 Speech Recognition Practical | |||
| 085 Jetsons Cartoon, Google Assistant_ NLP & Sound Recognition.en.srt | 6.37 KB | ||
| 085 Jetsons Cartoon, Google Assistant_ NLP & Sound Recognition.mp4 | 66.75 MB | ||
| 086 Convert Speech to Text - Load Resource File.en.srt | 1.06 KB | ||
| 086 Convert Speech to Text - Load Resource File.mp4 | 8.04 MB | ||
| 086 Merry-Christmas-SoundBible.com-1120316507.wav | 861.37 KB | ||
| 086 audio-text.ipynb | 6.04 KB | ||
| 087 Part 1_ Convert Speech to Text.en.srt | 6.17 KB | ||
| 087 Part 1_ Convert Speech to Text.mp4 | 55.23 MB | ||
| 088 Part2_ Recognise Speech & Convert to Text.en.srt | 7.16 KB | ||
| 088 Part2_ Recognise Speech & Convert to Text.mp4 | 64.66 MB | ||
| 16 Python Crash Course_ A Beginner's Guide | |||
| 089 Why Python for Data Science_.en.srt | 4.74 KB | ||
| 089 Why Python for Data Science_.mp4 | 19.15 MB | ||
| 090 Python-Crash-Course.ipynb | 27.57 KB | ||
| 090 Python_ Variables.en.srt | 9.66 KB | ||
| 090 Python_ Variables.mp4 | 27.14 MB | ||
| 091 Python_ Lists & Dictionaries.en.srt | 15.44 KB | ||
| 091 Python_ Lists & Dictionaries.mp4 | 56.36 MB | ||
| 092 Python_ Conditionals.en.srt | 9.49 KB | ||
| 092 Python_ Conditionals.mp4 | 30.53 MB | ||
| 093 Python_ Loops.en.srt | 11.65 KB | ||
| 093 Python_ Loops.mp4 | 36.29 MB | ||
| 094 Python_ Functions.en.srt | 7.88 KB | ||
| 094 Python_ Functions.mp4 | 22.2 MB | ||
| 095 Python_ Classes.en.srt | 11.47 KB | ||
| 095 Python_ Classes.mp4 | 39.55 MB |
Description
Description
This course takes you from a beginner level to being able to understand NLP concepts, linguistic theory, and then practice these basic theories using Python – with very simple examples as you code along with me.
If you are new to Python or any computer programming, the course instructions make it easy for you to code together with me. I explain code line by line.
The gentle pace takes you gradually from these basics of NLP foundation to being able to understand Mathematical & Linguistic (English-Language based, Non-Mathematical) theories of Deep Learning.
Natural Language Processing Foundation
Linguistics & Semantics – study the background theory on natural language to better understand the Computer Science applications
Pre-processing Data (cleaning)
Regex, Tokenization, Stemming, Lemmatization
Part-of-Speech Tagging
The topics outlined below are taught using practical Python projects!
Text Classification & Sentiment Analysis
Unsupervised Sentiment Analysis
Topic Modelling
Word Embedding with Deep Learning Models
LSTM using TensorFlow, Keras Sequence Model
Speech Recognition
Convert Speech to Text
Who this course is for:
Anyone who is curious about data science & NLP
Those who are in the Business & Marketing world – learn use NLP to gain insight into customers & products. Can help at interviews & job promotions.
If you intend to enrol in an NLP/Data Science course but are a total newbie, complete this course before to avoid being lost in class since it can seem overwhelming if classmates already have a foundation in Python or Datascience.
Requirements
No previous programming knowledge necessary. The lectures slowly explain the python syntax as you code alone with me.
New to Python: you get explanations of the code as you code along with me but not only that – theory slides explain concepts to help you understand what’s going on behind the code.
No data science knowledge required: lectures teach how to work with data and key modelling concepts.
No NLP knowledge required. Linguistic concepts are taught to give a strong foundation of NLP even before you get into practical coding. This helps you to grasp NLP modelling techniques and cleaning concepts better.
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