8.28 GB | 00:14:45 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
1  Introduction to Natural Language Processing  (42.98 MB)
2  Regenesys Graduate Atributes  (48.45 MB)
3  Course Contents - What You'll Learn  (28.37 MB)
1  What is Word Embedding  (121.16 MB)
2  Frequently Asked Questions about N-Grams  (52.63 MB)
3  How Embedding Vectors Can be Created from Text  (33.99 MB)
1  What are the Different Ways of Creating a Model for NLP Tasks  (72.24 MB)
2  What kind of vectorization Tf Idf performed  (55.22 MB)
3  Introduction to Recurrent Neural Network  (45.76 MB)
4  Variations or Applications of Recurrent Neural Networks (RNN)  (60.38 MB)
1  Introduction to Word Embedding and Word2Vec  (61.43 MB)
2  Difference between CBOW and skip-gram Models  (81.45 MB)
3  Doc2Vec and BERT Models Explained  (48.67 MB)
4  Which Vectorization Method to Use  (34.91 MB)
5  What are the Pre-trained Models and their Advantages & Limitations  (27.72 MB)
6  Frequently Asked Questions about Pre-trained Models  (76.44 MB)
1  What is Word Embeddings  (46.42 MB)
2  Text Vectorization Using Word2Vec Model  (62.33 MB)
3  Building CBOW and Skip Gram model  (66.42 MB)
4  Getting Similar Words Using CBOW Model  (118.01 MB)
5  Getting Similar Words Using Skip Gram Model  (69.2 MB)
1  Token Based Text Embedding Trained on English Wikipedia Corpus  (51.41 MB)
2  Using Tensorflow Hub Model  (70.6 MB)
3  Feed Forward Neural Network Explained  (101.74 MB)
4  Which Vectorization Method to Use  (34.93 MB)
5  Example of Using Pre-Trained Models  (72.41 MB)
6  More Questions About N-Grams  (99.54 MB)
1  Overview of Text Summarization in NLP  (71.89 MB)
2  What is NLP Text Summarization  (31.74 MB)
3  Types of Text Summarization  (105.42 MB)
4  Use CasesApplicationsEveryday Examples of Text Summarization  (27.45 MB)
5  Steps for Extractive Text Summarization  (44.92 MB)
1  Tokenization and Get Word Frequency  (43.29 MB)
2  Getting Normalized Word Frequency and Sentence Tokenization  (48.71 MB)
3  Calculating Score for Each Sentence and Ranking The Sentences  (159.66 MB)
4  Creating a Text Summarization Application  (41.71 MB)
5  A Gentle Introduction To Text Summarization by Jason Brownlee  (36.27 MB)
6  How to Open Text Summarization Code File in Colab  (54.37 MB)
1  How to import Spacy by using Pip Install Spacy  (107.16 MB)
2  Removing Stopwords in Text Summarization  (69 MB)
3  Getting Pre-Trained English Language Models from Spacy Library  (64.07 MB)
4  Word Frequency Counter using NLTK  (44.32 MB)
5  Normalizing of Word Frequency and Sentence Tokenization  (32.11 MB)
6  Calculating Score for Each Sentence  (46.44 MB)
7  Text Summarization using Sentence Scoring Method  (182.75 MB)
8  Spacy English Model Memory Requirements  (65.51 MB)
1  Getting YouTube Transcript Using YouTube Transcript api  (133.32 MB)
2  Understanding Text Embedding with Word2Vec  (61.65 MB)
1  Overview of Movie Recommendation System Using NLP  (39.36 MB)
2  What is Recommendation System and its Advantages  (47.35 MB)
3  Movie Recommendation Methods Using NLP  (50.99 MB)
4  Introduction To Demographic Filtering  (105.31 MB)
5  Introduction To Collaborative Filtering  (66.67 MB)
1  What is Natural Language Processing(NLP)  (45.03 MB)
2  History of Natural Language Processing  (38.97 MB)
3  Why Natural Language Processing is important in Today's World  (69.46 MB)
4  Applications of Natural Language Processing  (36.5 MB)
5  Variations in Natural Language Processing Machine Learning  (31.49 MB)
1  TMDB 5000 Movie Dataset  (140.02 MB)
2  Uploading and Read in Some Fixed Folder on Your Drive  (63.46 MB)
3  What Does df2 Contain  (78.87 MB)
4  Introduction To Content Based Filtering  (57.13 MB)
5  Computing Score for Every Movie using IMDB Weighted Rating Formula  (163.73 MB)
6  Sorting Movies Based on Calculated Score  (94.46 MB)
1  Finding Cosine Similarity Score With TF-IDF Vectorizer  (102.47 MB)
2  Computing Cosine Similarity Matrix Using Linear Kernel  (43.66 MB)
3  Getting the Pairwise Similarity Scores of All Movies  (43.88 MB)
4  Sorting the Movies and Getting the Top 10 Most Similar Movies  (53.5 MB)
5  Cast, Crew, Keywords and Features Used For Recommendations  (197.11 MB)
6  Frequently Asked Questions About Movie Recommendation System  (72.17 MB)
1  Averaging Word Embedding Vectors  (100.83 MB)
2  Implementation of A Doc2Vec Model Using Gensim  (91.28 MB)
1  Overview of Text Classification System  (32.41 MB)
2  What is Text Classification System and Its Advantages  (78.26 MB)
3  Step-by-step Explanation of Text Classification  (194.47 MB)
4  Basic Text Classification in NLP  (81.97 MB)
1  Installing Tensorflow Hub and Getting The Words Embedded  (77.71 MB)
2  Defining LSTM Models  (89.76 MB)
3  Encoding Code to Number Using Preprocessing  (38.85 MB)
4  Building, Training & Testing the LSTM Model  (65.45 MB)
5  Frequently Asked Questions About Using LSTM Models For Sentiment Analysis  (152.46 MB)
1  Introduction to Sentiment Analysis Dataset  (47.21 MB)
2  Counting Sentiment Value in Text Classification  (79.2 MB)
3  Visualizing Sentiment Value Count  (30.83 MB)
4  Downsampling The Dataset and Visualizing After Downsampling  (22.49 MB)
5  Data Pre-Processing in Machine Learning Method  (44.76 MB)
6  Visualizing Sentiment Analysis With Word Clouds  (41.09 MB)
7  Naive Bayes Classifier in Machine Learning  (85.92 MB)
8  TF-IDF for Sentiment Analysis  (96.48 MB)
9  Frequently Asked Questions About Machine Learning Method for Sentiment Analysis  (21.25 MB)
1  Sample Dataset for Natural Language Processing(NLP) Tasks  (57.12 MB)
2  Natural Language Processing(NLP) Project - Core Steps for Success  (90.99 MB)
3  Essential Python Libraries for NLP Projects  (49.89 MB)
4  Mastering Regular Expressions (Re) Library  (123.71 MB)
5  Regular Expressions (Re) Library for Data Cleaning  (12.4 MB)
1  What is Tokenization in Natural Language Processing (NLP)  (57.89 MB)
2  Differentiation between Stemming and Lemmatization  (31 MB)
3  Data Cleaning Process in Natural Language Processing  (36.2 MB)
4  Few Steps in Pre-processing for Natural Language Processing (NLP)  (63.23 MB)
1  Understanding Text with Bag-of-Words (BOW) Model  (119.26 MB)
2  Related Terms to Explore  (123.39 MB)
3  Sample Example of Text Processing  (45.67 MB)
4  Explaining N-grams in Natural Language Processing (NLP)  (96.94 MB)
5  Applications of Language Models  (29.31 MB)
1  Intro to NLTK for NLP with Python  (67.99 MB)
2  Stemming and Lemmatization In Python  (88.31 MB)
3  How to import and use stopwords list from NLTK  (72.91 MB)
4  Tokenize text using NLTK in python  (68.26 MB)
5  Removing stop words with NLTK in Python  (84.92 MB)
1  N-Gram Language Modelling with NLTK  (38.48 MB)
2  What are bigrams in NLP  (110.41 MB)
3  How to Implement N-grams in Python  (150.86 MB)
4  Using CountVectorizer for NLP feature extraction  (146.69 MB)
5  Implementation of TF-IDF for NLP  (63.98 MB)
1  Different Tokenization Methods in NLP  (39.22 MB)
2  Stemming and Lemmatization Using nltk  (64.23 MB)
3  Part of Speech(POS) Tagging with Stop words using NLTK  (121.77 MB)
1  Python Regex match search methods  (129.12 MB)
2  Substituting Patterns in Text Using Regex  (76.43 MB)
3  Finding All Matches using findall Method  (42.81 MB)
4  Finding All Email Addresses in the String  (10.43 MB)]
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