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 Indeed.com, 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

      26:17
      • 1.1 The Course Overview
        03:59
      • 1.2 Access and Use the Built-in Corpora of NLTK
        06:20
      • 1.3 Loading a Corpus
        04:08
      • 1.4 An Example of Conditional Frequency Distribution
        05:11
      • 1.5 An Example of Lexical Resouce
        06:39
    • Lesson 2 Processing Raw Text with NLTK

      23:12
      • 2.1 Working with an NLP Pipeline
        06:14
      • 2.2 Implementing Tokenization
        05:31
      • 2.3 Regular Expressions
        05:30
      • 2.4 Regular Expressions Used in Tokenization
        05:57
    • Lesson 3 A Practical Real World Example of Text Classification

      19:38
      • 3.1 Naive Bayes Text Classification
        07:06
      • 3.2 Age Prediction Application
        06:37
      • 3.3 Document Classifier Application
        05:55
    • Lesson 4 Finding Useful Information from Piles of Text

      13:24
      • 4.1 Hierarchy of Ideas or Chunking
        02:33
      • 4.2 Chunking in Python NLTK
        05:18
      • 4.3 Chinking Non Chunk Patterns in NLTK
        05:33
    • Lesson 5 Developing a Speech to Text Application Using Python

      28:43
      • 5.1 Python Speech Recognition Module
        06:11
      • 5.2 Speech to Text with Recurrent Neural Networks
        09:36
      • 5.3 Speech to Text with Convolutional Neural Networks Part One
        06:29
      • 5.4 Speech to Text with Convolutional Neural Networks Part Two
        06:27
    • Practice Projects

      • Twitter Hate
      • Zomato Rating
    • Lesson 1_Working_with_Text_Corpus

      26:17
      • 1.1 The Course Overview
        03:59
      • 1.2 Access and Use the Built-in Corpora of NLTK
        06:20
      • 1.3 Loading a Corpus
        04:08
      • 1.4 An Example of Conditional Frequency Distribution
        05:11
      • 1.5 An Example of Lexical Resouce
        06:39
    • Lesson 2_Processing_Raw_Text_with_NLTK

      23:12
      • 2.1 Working with an NLP Pipeline
        06:14
      • 2.2 Implementing Tokenization
        05:31
      • 2.3 Regular Expressions
        05:30
      • 2.4 Regular Expressions Used in Tokenization
        05:57
    • Lesson 3_A_Practical_Real_World_Example_of_Text_Classification

      19:38
      • 3.1 Naive Bayes Text Classification
        07:06
      • 3.2 Age Prediction Application
        06:37
      • 3.3 Document Classifier Application
        05:55
    • Lesson 4_Finding_Usefule_Information_from_Piles_of_Text

      13:24
      • 4.1 Hierarchy of Ideas or Chunking
        02:33
      • 4.2 Chunking in Python NLTK
        05:18
      • 4.3 Chinking Non Chunk Patterns in NLTK
        05:33
    • Lesson 5_Developing_a_Speech_to_Text_Application_Using_Python

      28:43
      • 5.1 Python Speech Recognition Module
        06:11
      • 5.2 Speech to Text with Recurrent Neural Networks
        09:36
      • 5.3 Speech to Text with Convolutional Neural Networks Part One
        06:29
      • 5.4 Speech to Text with Convolutional Neural Networks Part Two
        06:27
    • Lesson 6 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.

      FAQs

      • 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.

          Contact Us

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          (Toll Free)

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