The Ultimate Beginners Guide to Natural Language Processing torrent

The Ultimate Beginners Guide to Natural Language Processing



Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.06 GB | Duration: 6h 3m
Learn step-by-step the main concepts of natural language processing in Python! Build a sentiment classifier!
What you'll learn
Understand the basic concepts of natural language processing, such as: part-of-speech, lemmatization, stemming, named entity recognition, and stop words
Understand more advanced concepts, such as: dependency parsing, tokenization, word and sentence similarity
Load texts from the Internet to apply natural language processing techniques
How to visualize the most frequent terms using wordcloud
Implement text summarization and keyword search
Learn how to represent texts using Bag of Words and TF-IDF
Implement sentiment analysis using NLTK library (natural language toolkit), TF-IDF and spaCy library

Description
The area of ​​Natural Language Processing (NLP) is a subarea of ​​Artificial Intelligence that aims to make computers capable of understanding human language, both written and spoken. Some examples of practical applications are: translators between languages, translation from text to speech or speech to text, chatbots, automatic question and answer systems (Q&A), automatic generation of descriptions for images, generation of subtitles in videos, classification of sentiments in sentences, among many others! Learning this area can be the key to bringing real solutions to present and future needs!

Based on that, this course was designed for those who want to grow or start a new career in Natural Language Processing, using the spaCy and NLTK (Natural Language Toolkit) libraries and the Python programming language! SpaCy was developed with the focus on use in production and real environments, so it is possible to create applications that process a lot of data. It can be used to extract information, understand natural language and even preprocess texts for later use in deep learning models.

The course is divided into three parts: