Natural Language Processing (NLP) Fundamentals, 3rd Edition

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Free Download Natural Language Processing (NLP) Fundamentals, 3rd Edition
ISBN: 9780135439692 | .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 6h 14m | 1.34 GB
Instructor: Bruno Goncalves

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Introduction
Natural Language Processing (NLP) Fundamentals: Introduction
Lesson 1: Text Representation
Topics
1.1 One-hot Encoding
1.2 Bag of Words
1.3 Stop Words
1.4 TF-IDF
1.5 N-grams
1.6 Word Embeddings
1.7 Demo
Lesson 2: Text Cleaning
Topics
2.1 Stemming
2.2 Lemmatization
2.3 Regular Expressions
2.4 Text Cleaning Demo
Lesson 3: Named Entity Recognition
Topics
3.1 Part of Speech Tagging
3.2 Chunking
3.3 Chinking
3.4 Named Entity Recognition
3.5 Demo
Lesson 4: Topic Modeling
Topics
4.1 Explicit Semantic Analysis
4.2 Document Clustering
4.3 Latent Semantic Analysis
4.4 LDA
4.5 Non-negative Matrix Factorization
4.6 Demo
Lesson 5: Sentiment Analysis
Topics
5.1 Quantify Words and Feelings
5.2 Negations and Modifiers
5.3 Corpus-based Approaches
5.4 Demo
Lesson 6: Text Classification
Topics
6.1 Feed Forward Networks
6.2 Convolutional Neural Networks
6.3 Applications
6.4 Demo
Lesson 7: Sequence Modeling
Topics
7.1 Recurrent Neural Networks
7.2 Gated Recurrent Unit
7.3 Long Short-term Memory
7.4 Auto-encoder Models
7.5 Demo
Lesson 8: Applications
Topics
8.1 Word2vec Embeddings
8.2 GloVe
8.3 Transfer Learning
8.4 Language Detection
8.5 Demo
Lesson 9: NLP with Large Language Models
Topics
9.1 Large Language Models
9.2 Transformers
9.3 BERT
9.4 HuggingFace
9.5 NLP Tasks
9.6 Demo
Summary
Natural Language Processing (NLP) Fundamentals: Summary
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Natural Language Processing Fundamentals
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 6h 3m | 13.44 GB
Instructors: Anthony Ng, Dwight Gunning, Sohom Ghosh​

Use Python and NLTK (Natural Language Toolkit) to build your own text classifiers and solve common NLP problems.

Learn

Obtain, verify, and clean data before transforming it into a correct format for use
Perform data analysis and machine learning tasks using Python
Understand the basics of computational linguistics
Build models for general natural language processing tasks
Evaluate the performance of a model with the right metrics
Visualize, quantify, and perform exploratory analysis from any text data

About

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems.

You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots.

By the end of this course, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The course will easily equip you with the knowledge you need to build applications that interpret human language.

Features

Assimilate key NLP concepts and terminologies
Explore popular NLP tools and techniques
Gain practical experience using NLP in application code

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