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Natural Language Processing Essentials

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Natural Language Processing Essentials

Instructor: Edureka

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1 week to complete
at 10 hours a week
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Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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.

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Assessments

15 assignments

Taught in English

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This course is part of the Mastering NLP: Tokenization, Sentiment Analysis & Neural MT Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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 like morphology, syntax, semantics, pragmatics, and discourse, that form the basis of how machines understand and work with human language.

What's included

22 videos3 readings4 assignments1 discussion prompt

22 videos•Total 110 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•6 minutes
  • Parsing•3 minutes
  • Semantics in NLP: Understanding Meaning and Context•5 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 readings•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 assignments•Total 48 minutes
  • Knowledge Check: Introduction to NLP and Linguistics•30 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
1 discussion prompt•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.

What's included

44 videos4 readings6 assignments

44 videos•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 readings•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 assignments•Total 60 minutes
  • Knowledge Check: Text Processing and Feature Engineering•30 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

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.

What's included

13 videos3 readings4 assignments

13 videos•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 readings•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 assignments•Total 48 minutes
  • Knowledge Check: Named Entity Recognition (NER) & Parsing•30 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

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.

What's included

1 video1 reading1 assignment1 discussion prompt

1 video•Total 2 minutes
  • Course Summary: Natural Language Processing Essentials•2 minutes
1 reading•Total 30 minutes
  • Final Project: Public Response Analysis•30 minutes
1 assignment•Total 30 minutes
  • End Course Knowledge Check: Natural Language Processing Essentials•30 minutes
1 discussion prompt•Total 10 minutes
  • Describe your Learning Journey•10 minutes

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Instructor

Edureka
203 Courses•185,724 learners

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Frequently asked 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.

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.

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.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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.

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.

Financial aid available,