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Master Natural Language Processing & Build NLP Web App torrent


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Info hash: 0D571EE347C7A928FB353CAF12CC147A2ADC7DB3
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Torrent language: English English
Total Size: 1.30 GB
Number of files:
104   
Uploader:
freecoursewb
Torrent added:2021-08-06 15:04:20

Download Master Natural Language Processing & Build NLP Web App torrent




Torrent Description
Master Natural Language Processing & Build NLP Web App

Last Update: 7/2021
Duration: 4h 32m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 1.25 GB
Genre: eLearning | Language: English
Create Word Cloud App Using Streamlit | Sentiment Analysis | Speech to text | Spam Detection | Code Walkthrough

What you'll learn:
You will gain insights on what Natural Language Processing(NLP) is, its Applications & Challenges
You will learn Sentence Segmentation, Word Tokenization, Stemming, Lemmatization, Parsing, POS & Ambiguities in NLP
You will learn to execute using Machine Learning, NLTK & Spacey
You will learn to work with Text Files with Python
You will utilize Regular Expressions for pattern searching in text
You will use Part of Speech Tagging to automatically process raw text files
You will visualize POS and NER with Spacy
You will understand Vocabulary Matching with Spacy
You will use NLTK for Sentiment Analysis

Requirements:
None. (Python is covered extensively in the course)

Description:
Natural Language Processing (NLP) is a very interesting field associated with AI and is at the forefront of many useful applications like a chatbot. Knowledge of NLP is considered a necessity for those pursuing a career in AI. This course covers both the theory as well as the applications of NLP. Case studies are explained along with a walkthrough of the codes for a better understanding of the subject.
A detailed explanation of how to build a web app for NLP using Streamlit is also explained.
NLP is a subfield of computer science and artificial intelligence concerned with interactions between computers and human (natural) languages. It is used to apply machine learning algorithms to text and speech.
For example, we can use NLP to create systems like speech recognition, document summarization, machine translation, spam detection, named entity recognition, question answering, autocomplete, predictive typing and so on.
Nowadays, most of us have smartphones that have speech recognition. These smartphones use NLP to understand what is said. Also, many people use laptops whose operating system has built-in speech recognition.

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