Course Description

  • Why should you take this Natural Language Processing (NLP) course?

    Natural language processing applies analytic tools to read from a huge collection of natural language data to derive a meaningful conclusion. It has given rise to chatbots and virtual assistants to address the queries of millions of users. It is speculated that by 2025, NLP is going to help the AI market to expand more than $60 Billion (Source: Forbes). According to, the annual average salary of an NLP professional is $128,857.

  • What are the objectives of this course?

    This course is designed to familiarize you with natural language processing using both machine and deep learning methods. It introduces you to the fundamentals of natural language processing using the most popular library, Python’s Natural Language Toolkit (NLTK). The course will teach you about statistical machine translation, deep semantic similarity models (DSSM) and their applications, deep reinforcement learning techniques applied in NLP, and vision-language multimodal intelligence.

  • What skills will you learn from this Natural Language Processing training course?

    After completing this NLP training in Python, you will be able to:

    • Learn how to perform text processing and find a pattern
    • Find the most relevant document by applying TF-IDF
    • Write a script for applying parts-of-speech and extraction on focus words
    • Create your own NLP module
    • Classify the cluster for articles
    • Create a basic speech model
    • Convert speech to text

  • Who should take this online course on NLP?

    The course is ideal for anyone who wants to become familiar with this emerging and exciting domain of AI, including:

    • Data scientists
    • Analytics managers 
    • Data analysts
    • Data engineers
    • Data architects

  • What are the prerequisites for Simplilearn's Natural Language Processing course?

    Learners who attend this course should have a basic understanding of:

    • Math and statistics
    • Data science  
    • Machine learning

Course Preview

    • Lesson 1 Working with Text Corpus

      • 1.1 The Course Overview
      • 1.2 Access and Use the Built-in Corpora of NLTK
      • 1.3 Loading a Corpus
      • 1.4 An Example of Conditional Frequency Distribution
      • 1.5 An Example of Lexical Resouce
    • Lesson 2 Processing Raw Text with NLTK

      • 2.1 Working with an NLP Pipeline
      • 2.2 Implementing Tokenization
      • 2.3 Regular Expressions
      • 2.4 Regular Expressions Used in Tokenization
    • Lesson 3 A Practical Real World Example of Text Classification

      • 3.1 Naive Bayes Text Classification
      • 3.2 Age Prediction Application
      • 3.3 Document Classifier Application
    • Lesson 4 Finding Useful Information from Piles of Text

      • 4.1 Hierarchy of Ideas or Chunking
      • 4.2 Chunking in Python NLTK
      • 4.3 Chinking Non Chunk Patterns in NLTK
    • Lesson 5 Developing a Speech to Text Application Using Python

      • 5.1 Python Speech Recognition Module
      • 5.2 Speech to Text with Recurrent Neural Networks
      • 5.3 Speech to Text with Convolutional Neural Networks Part One
      • 5.4 Speech to Text with Convolutional Neural Networks Part Two
    • Lesson 1 - Introduction to NLP

      • Introduction to Natural Language Processing
      • Components of NLP
      • Applications of NLP
      • Challenges and scope
      • Data formats
      • Text Processing
      • Assisted Practice: Implement Text Processing Using Stemming and Regular Expression after Noise Removal and Convert It into List of Phrases
      • Tweets Cleanup and Analysis Using Regular Expressions
    • Lesson 2 - Feature Engineering on text data

      • N-Gram
      • Bag of Words
      • Document Term Matrix
      • TF-IDF
      • Levenshtein Distance
      • Word Embedding(Word2Vec)
      • Doc2vec
      • PCA
      • Word Analogies
      • Topic Modelling
      • Assisted Practice: Word2vec Model Creation
      • Assisted Practice: Word Analogies Demo
      • Assisted Practice: Identify Topics from News Items
      • Build Your Own News Search Engine
    • Lesson 3 - Natural Language Understanding techniques

      • Parts of Speech Tagging
      • Dependency Parsing
      • Constituency Parsing
      • Morphological Parsing
      • Named Entity Recognition
      • Coreference Resolution
      • Word Sense Disambiguation
      • Fuzzy Search
      • Document and Sentence Similarity
      • Document Indexing
      • Sentiment Analysis
      • Assisted Practice: Analyzing the Disease and Instrument Name with the Action Performed
      • Assisted Practice: Analyzing the Sentiments
      • Assisted Practice: Extract City and Person Name from Text
      • Identifying Top Product Feature from User Reviews
    • Lesson 4 - Natural Language Generation

      • Retrieval based model
      • Generative based model
      • AIML
      • Language Modelling
      • Sentence Correction
      • Assisted Practice: Create AIML Patterns for QnA on Mental Wellness
      • Assisted Practice: To Predict the Next Word in a Sentence
      • Create your Own Spell Checker
    • Lesson 5 - NLP Libraries

      • Spacy
      • NLTK
      • Gensim
      • TextBlob
      • StanfordNLP
      • LUIS
      • Assisted Practice: Simplilearn Review Analysis
      • Create your Own NLP Module
    • Lesson 6 - NLP with Machine Learning and Deep Learning

      • Neural Machine Translation
      • Text Classification
      • Text Summarization
      • Document Clustering
      • Attention Mechanism
      • Question Answering Engine
      • Assisted Practice: Target Spam Words and Patterns
      • Assisted Practice: Summarization of News
      • Document Clustering for BBC News
    • Lesson 7 - Speech recognition techniques

      • Basic concepts for voice/sound
      • Reading, loading and processing the voice data
      • Creating speech model
      • Saving model
      • Implementation/use cases
      • Speech libraries
      • Assisted Practice: Translation from Speech to Text
      • Speech to Text: Extract Keywords from Audio Reviews
    • Section 3 - Practice Projects

      • Twitter Hate
      • Zomato Rating
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Exam & Certification

  • What do I need to do to unlock my Simplilearn certificate?

    You must complete this course in order to unlock the Simplilearn certificate.


        • What is Natural Language Processing (NLP)?

          Natural language processing (or text analytics/text mining) applies analytic tools to read from a huge collection of natural language data to derive a meaningful conclusion. It has given rise to chatbots and virtual assistants to address queries of millions of users. It is a branch of artificial intelligence that has important implications on the ways that computers and humans interact. Natural language processing will help bridge the gap between human communication and digital data.

        • How do I enroll in this online training?

          You can enroll in this training on our website and make an online payment using any of the following options:
          ·     Visa Credit or Debit Card
          ·     MasterCard
          ·     American Express
          ·     Diner’s Club
          ·     PayPal
          Once payment is received you will automatically receive a payment receipt and access information via email.

        • Can I cancel my enrollment? Will I get a refund?

          Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our refund policy.

        • What is covered under the 24/7 Support promise?

          We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

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              • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.