Natural Language Processing (NLP) with Python 2020
Natural Language Processing (NLP) with Python: 2020
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 9h 51m | 4.49 GB
Instructor: Ankit Mistry, Vijay Gadhave
Learn Natural Language Processing with Python, Machine Learning, Deep Learning, SpaCy, NLTK, Sklearn, CNN, RNN & more !
What you'll learn
The Complete understanding of Natural Language Processing
Implement NLP related task with Scikit-learn, NLTK and SpaCy
Apply Machine Learning Model to Classify Text Data
Text Classification (Spam Detection, Amazon product Review Classification)
Text Summarization (Turn 5000 word article into 200 Words)
Calculate Sentiment Score from Recently Posted Tweet (Tweeter API)
Refresh your Deep Learning Concepts (ANN, CNN & RNN)
Build your own Word Embedding (Word2vec) Model with Keras
Word Embeddings application with Google Pretrained Model
Spam Message Detection with Neural Network Based CNN and RNN Model
Automatic Text Generation using TensorFlow, Keras and LSTM
Working with Text Files & PDF in Python (PyPDF2 module)
Tokenization, Stemming and Lemmatization
Stop Words, Parts of Speech (POS) Tagging with NLTK
Vocabulary, Matching, Named Entity Recognition (NER)
Data Analysis with Numpy and Pandas
Data Visualization with MatDescriptionlib library
Hi Data Lovers,
Do you have idea about Which Artificial Intelligence field is going to get big in upcoming year?
According to statista dot com which field of AI is predicted to reach $43 billion by 2025?
If answer is 'Natural Language Processing', You are at right place.
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Do you want to know
How Google News classify millions of news article into hundreds of different category.
How Android speech recognition recognize your voice with such high accuracy.
How Google Translate actually translate hundreds of pairs of different languages into one another.
If answer is "Yes", You are on right track.
and to help yourself, me and my friend Vijay have created comprehensive course For Students and Professionals to learn Natural Language Processing from very Beginning.
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NLP - "Natural Language Processing" has found space in every aspect of our daily life.
Cell phone internet are the integral part of our life. Any most application you will find the use of NLP methods, from search engine of Google to recommendation system of Amazon & Netflix.
Chatbot
Google Now, Apple Siri, Amazon Alexa
Machine Translation
Sentiment analysis
Speech Recognition and many more.
So, welcome to my course on NLP.
Complete Natural Language Processing (NLP) with Spacy & NLTK
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Course Outline :
1 : Welcome In this section we will get complete idea about what we are going to learn in the whole course and understanding related to natural language processing.
2 : Installation & Setup In this section we will get our online environment Google Colab setup.
3 : Basics of Natural Language Processing In this section we will dive into all basic NLP task like Tokenization, Lemmatization, stop word removal, name entity recognition, part of speech tagging, and see how to apply with different functions available in a Spacy and NLTK library.
4, 5, 6 : Spam Message Classification, Restaurant Review Prediction (Good or bad), IMDB, Amazon and Yelp review Classification
In the next 3 section we will get dive into a real world data set for text classification, spam detection, restaurant review classification, Amazon IMDb reviews. We will see how to do Pre-Processing and make your data suitable for machine learning algorithm and apply different Machine Learning estimator (Logistic Regression, SVM, Decision Tree) for classifying text.
7, 8 : Automated Text Summarization, Twitter sentiment Analysis In this 2 section we will work upon real world application of NLP.
Automatic text summarisation, Which compress your text to find the summary of big articles
Another one we will work is finding the sentiment from the recently posted tweet about some specific keyword with the help of Twitter API - tweepy library
9 : Deep Learning Basics In This Section we will get a basic idea about Deep learning concept, like artificial neural network activation function and how ANN works.
10 : Word Embedding In This Section, we will see How to implement word2vec on our custom datasets, as well as using Pretrained Google Model.
11, 12 : Text Classification with CNN & RNN In this section we will see how to apply advanced deep learning model like convolution neural networks and recurrent neural networks for text classification.
13 : Automatic Text Generation using TensorFlow, Keras and LSTM In this section we will apply neural network based LSTM model to automatically generate text.
14, 15, 16, 17 : Numpy, Pandas, MatDescriptionlib + File Processing In this section, for all of you who want refresh concept related to data analysis with Numpy and Pandas library, Data Visualization with MatDescriptionlib library, and Text File processing and PDF File processing.
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So, This is the one of the most comprehensive course on natural language processing,
And I am expecting you to know basic knowledge of python and your curiosity to learn Different techniques in NLP world.
Regards
Ankit & Vijay
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