What problems does NLP solve?

Sentiment analysis, language translation, and named entity recognition are just a few examples of tasks classified as NLP problems. To enhance NLP solutions and applications, identifying these examples is crucial.

Does ChatGPT use NLP?

Indeed, ChatGPT implements natural language processing (NLP). In reality, NLP is a fundamental technology that enables ChatGPT to comprehend, generate, and respond to human language in a meaningful manner.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Natural Language Processing Essentials

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Natural Language Processing Essentials

Ce cours fait partie de Spécialisation "Mastering NLP: Tokenization, Sentiment Analysis & Neural MT "

Instructeur : Edureka

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4 modules

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4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Remember key NLP concepts and terminology used in processing human language and modern AI applications.
Understand core linguistic principles like morphology, syntax, semantics, and pragmatics in NLP.
Apply Python tools and techniques to clean, preprocess, and extract features from text data effectively.
Develop and evaluate basic NLP models for tasks like text classification and named entity recognition.

Compétences que vous acquerrez

Catégorie : Machine Learning Methods
Catégorie : Data Preprocessing
Catégorie : Model Evaluation
Catégorie : Embeddings

Outils que vous découvrirez

Catégorie : Classification Algorithms

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Il y a 4 modules dans ce cours

This course introduces the fundamentals of Natural Language Processing (NLP), combining core linguistic concepts with hands-on programming techniques to help you understand how machines process human language. Whether you're new to NLP or looking to build foundational skills, this course provides a clear and practical path into one of the most exciting areas of AI and data science.

Through guided lessons and real-world examples, you'll learn how to clean, structure, and analyze text data, apply feature extraction techniques, and build basic NLP models for tasks like text classification and named entity recognition. By the end of this course, you will be able to: • Understand NLP basics and key language concepts like morphology, syntax, semantics, and pragmatics. • Apply text cleaning and preprocessing techniques using NLTK and SpaCy, including tokenization, stemming, lemmatization, and embeddings. • Analyze text features by extracting Bag of Words, TF-IDF, and Word2Vec representations. • Evaluate machine learning models built for text classification. • Create NLP solutions by implementing Named Entity Recognition and syntactic parsing. This course is ideal for beginners, data enthusiasts, and aspiring NLP practitioners who want to gain a strong foundation in natural language processing and its applications in AI. No prior experience with NLP is required. A basic understanding of Python or machine learning concepts will be helpful, but not mandatory. Join us to begin your journey into the world of Natural Language Processing and text analysis with Python!

In this module, learners will develop a foundational understanding of Natural Language Processing (NLP) and its role in interpreting and processing human language. They will explore the history of NLP, its key challenges, and real-world applications. The module also introduces essential linguistic concepts—morphology, syntax, semantics, pragmatics, and discourse—that form the basis of how machines understand and work with human language.

Inclus

22 vidéos3 lectures4 devoirs1 sujet de discussion

22 vidéos Total 109 minutes

Specialization Introduction 5 minutes
Course Introduction 3 minutes
What is NLP? 6 minutes
Classification and Working of NLP 7 minutes
History of NLP Development 6 minutes
Key Challenges: Ambiguity, Variation, Bias 4 minutes
Further Exploration of NLP Challenges 4 minutes
Real-World NLP Applications 6 minutes
Rule-Based vs. Statistical Approaches 5 minutes
Morphology: Words, Stems, Lemmas 4 minutes
Sentence Structuring 3 minutes
Parsing 3 minutes
Semantics in NLP: Understanding Meaning and Context 7 minutes
Pragmatics: Context and Conversational Meaning 7 minutes
Discourse Analysis in NLP 5 minutes
Steps in an NLP Workflow 6 minutes
Basic Text Cleaning: Stopwords, Lowercasing, Tokenization 4 minutes
Introduction to Word Embeddings: One-Hot Encoding 5 minutes
Handling Noise and Special Characters 7 minutes
Demonstration: Lowercasing, Stopword Removal and Tokenization 7 minutes
Demonstration: One-Hot Encoding 5 minutes
Summary of Introduction to NLP and Linguistics 1 minute

3 lectures Total 50 minutes

Welcome to Natural Language Processing Essentials 10 minutes
Evolution of NLP: From Rule-Based Systems to Deep Learning Approaches 20 minutes
Linguistics for NLP: Morphology, Syntax, and Semantics 20 minutes

4 devoirs Total 48 minutes

Practice Quiz: Overview of Natural Language Processing 6 minutes
Practice Quiz: Linguistic Basics for NLP 6 minutes
Practice Quiz: NLP Pipeline and Text Representation 6 minutes
Knowledge Check: Introduction to NLP and Linguistics 30 minutes

1 sujet de discussion Total 10 minutes

Introduce Yourself 10 minutes

This module focuses on preparing textual data for analysis by exploring techniques like tokenization, normalization, stemming, and lemmatization. Learners will also examine various feature extraction methods, including Bag-of-Words, TF-IDF, and word embeddings like Word2Vec and GloVe to represent language in machine-readable formats.

Inclus

44 vidéos4 lectures6 devoirs

44 vidéos Total 200 minutes

Using Regex for NLP 3 minutes
Types of Tokenization: Subword tokenization 3 minutes
Types of Tokenization: Character tokenization 4 minutes
Handling Punctuation and Special Characters 6 minutes
Normalization Techniques: Accents, Unicode, Special Characters 5 minutes
Demonstration: Word Tokenization 4 minutes
Demonstration: Subword Tokenization 4 minutes
Demonstration: Normalization 5 minutes
Rule Based Stemming 3 minutes
Porter Stemmer 6 minutes
Snowball Stemmer 5 minutes
Lancaster Stemmer 4 minutes
Lovins Stemmer, Krovetz Stemmer and Context-Aware Stemming 5 minutes
Introduction to Lemmatization 6 minutes
Applications of Lemmatization 4 minutes
Rule-Based, Dictionary, Hybrid and Machine Learning Based Lemmatizations 4 minutes
Lemmatization: Different Approaches 4 minutes
Rule Based Stemming and Porter Stemmer 6 minutes
Snowball, Lancaster and Lovins 7 minutes
Demonstration: Lemmatization Techniques 2 minutes
Demonstration: Text Blob, WordNet, and Neural Lemmatizer using Stanza 2 minutes
Part-of-Speech (POS) Tagging 6 minutes
Text Representation: Bag of Words (BoW) 4 minutes
Text Representation: TF-IDF 6 minutes
Word Embeddings: Word2Vec 5 minutes
Word Embeddings: GloVe 4 minutes
Word Embeddings: FastText 5 minutes
Feature extraction using Bag of Words and TF-IDF 6 minutes
Handling Noisy Datasets (Typos, Emojis, Abbreviations) 6 minutes
Processing Code-Mixed (Multilingual) Text 5 minutes
Text Preprocessing in Domain-Specific Contexts (e.g., Medical, Legal, Financial) 5 minutes
Demonstration: Handling Noisy Datasets - Typos, Emojis and Abbreviations 7 minutes
Demonstration: Processing Code-Mixed (Multilingual) Text 5 minutes
Text Classification in NLP using Common ML Models 5 minutes
Common ML Models: Naïve Bayes, SVM 4 minutes
Feature Selection for Classification 6 minutes
Applications and Challenges of Feature Selection 3 minutes
Peformance Metrics: Accuracy and Precision 5 minutes
Peformance Metrics: Recall and F1 Score 3 minutes
Supervised Learning for Text Classification 6 minutes
Text Classification Demo using COVID-19 Tweets Dataset 5 minutes
Feature Extraction, Train and Evaluate Model Performance 6 minutes
Comparing Models for Best Performance 2 minutes
Summary of Text Processing and Feature Engineering 2 minutes

4 lectures Total 75 minutes

Tokenization and Normalization: Preparing Text for Language Processing 20 minutes
Rule-Based vs. Context-Aware Stemming and Lemmatization Techniques 20 minutes
Feature Extraction in NLP: From Frequency to Semantic Vectors 20 minutes
Text Classification with ML Models: An Introductory Overview 15 minutes

6 devoirs Total 60 minutes

Practice Quiz: Tokenization and Normalization 6 minutes
Practice Quiz: Stemming and Lemmatization 6 minutes
Practice Quiz: Vector Representation and Feature Extraction 6 minutes
Practice Quiz: Advanced Preprocessing Techniques 6 minutes
Practice Quiz: Basics of Text Classification 6 minutes
Knowledge Check: Text Processing and Feature Engineering 30 minutes

In this module, learners will study techniques for identifying entities and extracting structured information from text. It covers rule-based and deep learning-based NER models, dependency and constituency parsing methods, and syntactic tree construction to enable deeper text understanding.

Inclus

13 vidéos3 lectures4 devoirs

13 vidéos Total 53 minutes

What is NER and where It's Used? 7 minutes
Pretrained NER Models: SpaCy, StanfordNLP 5 minutes
Transformer-Based NER Models (BERT-NER, RoBERTa-Based Approaches) 7 minutes
Challenges in NER: Ambiguity, Overlapping Entities 4 minutes
Parsing Algorithms: Earley, CYK 3 minutes
Dependency Parsing with SpaCy & StanfordNLP 2 minutes
Building a Syntax Tree in Python 4 minutes
Demonstration: Data Preparation for Parsing 3 minutes
Demonstration: Constituency and Dependency Parsing 5 minutes
Relation Extraction Techniques 4 minutes
Coreference Resolution (Tracking Entities in Text) 3 minutes
Text Summarization: Extractive & Abstractive 5 minutes
Summary of Named Entity Recognition (NER) & Parsing 1 minute

3 lectures Total 50 minutes

Named Entity Recognition: Concepts, Models, and Evaluation 15 minutes
Constituency and Dependency Parsing: Understanding Sentence Structure 20 minutes
From Entities to Insights: Relation Extraction and Summarization 15 minutes

4 devoirs Total 48 minutes

Practice Quiz: Named Entity Recognition (NER) 6 minutes
Practice Quiz: Parsing & Dependency Trees 6 minutes
Practice Quiz: Information Extraction and Text Mining 6 minutes
Knowledge Check: Named Entity Recognition (NER) & Parsing 30 minutes

This module is designed to assess learners on the key concepts and techniques covered throughout the course. It includes a graded quiz that tests knowledge of NLP foundations, linguistic principles, text preprocessing, feature engineering, entity recognition, and parsing methods using both classical and deep learning approaches.

Inclus

1 vidéo1 lecture1 devoir1 sujet de discussion

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Foire Aux Questions

NLP (Natural Language Processing) is a branch of artificial intelligence designed to help computers understand, interpret, and generate human language. It is an extensive field with many applications, such as machine translation, chatbots, text analysis, and sentiment analysis.

The key components of NLP are:

Natural Language Understanding (NLU): The process of mapping human language input to a representation that can be understood by the computer.

Natural Language Generation (NLG): The process of generating human language output from a representation that can be understood by the computer.

Some common applications of NLP are:

Machine Translation: The process of translating text from one language to another.

Chatbots: Interactive systems that can communicate with users in natural language.

Text Analysis: The process of extracting information and insights from text data.

Sentiment analysis: Determining the emotional tone of text.

Question Answering: The development of systems that are capable of responding to inquiries regarding a specific text or knowledge base.

Some common challenges in NLP include:

Ambiguity: Words and phrases can have multiple meanings, making it difficult for computers to understand the intended meaning.

Context: The meaning of words and phrases can vary depending on the context in which they are used.

Computational Complexity: Processing large amounts of text data can be computationally expensive.

Bias: NLP models can reflect the biases present in the data they are trained on.

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