Natural Language Processing Course

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Course Overview

The Natural Language Processing course covers concepts like statistical machine translation and neural models, deep semantic similarity models (DSSM), neural knowledge base embedding, deep reinforcement learning techniques, neural models applied in image captioning, and visual question answering with Python’s Natural Language Toolkit.

Key Features

  • Lifetime access to self-paced learning
  • Industry-recognized course completion certificate
  • Lifetime access to self-paced learning
  • Industry-recognized course completion certificate
  • Lifetime access to self-paced learning
  • Industry-recognized course completion certificate

Skills Covered

  • Perform text processing
  • Create an NLP module
  • Create a basic speech model
  • Working with NLP Pipeline
  • Classify cluster for articles
  • Convert speech to text
  • Perform text processing
  • Working with NLP Pipeline
  • Create an NLP module
  • Classify cluster for articles
  • Create a basic speech model
  • Convert speech to text
  • Perform text processing
  • Working with NLP Pipeline
  • Create an NLP module
  • Classify cluster for articles
  • Create a basic speech model
  • Convert speech to text

Begin your journey to success

Get lifetime access to self-paced e-learning content


The NLP market size is expected to reach $26.4 billion by 2024, with a growth rate of 21 percent from 2019 to 2024. Chatbots and virtual assistants have effectively used NLP—the increasing use of smart devices and cloud-based solutions will drive this growth.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Source: Indeed
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Source: Indeed
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    JPMorgan Chase
    Source: Indeed
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Wells Fargo
    Procter and Gamble
    Source: Indeed

Training Options

online Bootcamp

  • Flexi Pass Enabled: Flexibility to reschedule your cohort within first 90 days of access.
  • Lifetime access to high-quality self-paced e-learning content and live class recordings
  • 24x7 learner assistance and support
  • Batch starting from:
25th May, Weekend Class
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Corporate Training

Customised to enterprise needs

  • Blended learning delivery model (self-paced e-learning and/or instructor-led options)
  • Course, category, and all-access pricing
  • Enterprise-class learning management system (LMS)
  • Enhanced reporting for individuals and teams
  • 24x7 teaching assistance and support

Course Curriculum


Natural Language Processing course is ideal for anyone who wants to become familiar with this emerging and exciting domain of artificial intelligence (AI), including data scientists, analytics managers, data analysts, data engineers, and data architects.
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Learners are looking to enroll for Natural Language Processing course should have a basic understanding of math, statistics, data science, and machine learning.
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Course Content

  • Section 01 - NLP Overview (Self Learning)

    • 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 01: Natural Language Processing - Introduction

      • 1.01 Introduction
    • 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 02: Natural Language

      • 2.01 Natural Language
    • 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 03: Corpus

      • 3.01 Corpus
    • 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 04: Text Analytics

      • 4.01 Text Analytics
    • 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 05: Feature Extraction

      • 5.01 Feature Extraction
    • Lesson 06: Machine Learning

      • 6.01 Machine Learning
    • Lesson 07: Python Toolkits

      • 7.01 Python Toolkits
    • Lesson 08: Bagging

      • 8.01 Bagging
    • Lesson 09: Deep Learning

      • 9.01 Deep Learning: Part One
      • 9.02 Deep Learning: Part Two
    • Lesson 10: Demonstrations

      • 10.01 Demo - Setup
      • 10.02 Demo - RNN One: Build Model
      • 10.03 Demo - RNN Two: Analyze Results
      • 10.04 Demo - Clustering and Topic Modeling: Part One
      • 10.05 Demo - Clustering and Topic Modeling: Part Two
      • 10.06 Demo - Sentiment Analysis
      • 10.07 Demo - NLTK
      • 10.08 Demo - sPacy
      • 10.09 Demo - Glove
      • 10.10 Demo - SpamHam
  • Section 02 - Live Class Curriculum

    • Introduction to NLP

      • Definition and scope of NLP
      • Real-world applications and significance of NLP
      • Basic terminologies such as corpus, tokenization, and syntactic analysis
    • Text Data Analysis

      • Data preprocessing techniques tokenization, stop-word removal, and stemming, Lemmatization
      • Text data exploration and visualization.
      • Feature Engineering
      • Text classification - sentiment analysis using NLTK- Naive Bayes Classifier
      • Assisted Practices : Implementation of twitter data sentiment analysis using NLTK Naive Bayes Classifier
      • Lesson-end project
    • NLP Text Vectorization

      • Vector Representation of Text - One Hot Encoding
      • Understanding BoW Technique
      • TFIDF
      • Assisted Practices
      • Lesson-end project
    • Distributed Representations

      • Word embeddings and their importance in NLP
      • Detailed explanation of Word2Vec and Glove embeddings.
      • Training and using pre-trained word embeddings.
      • Assisted Practices
      • Lesson-end project
    • Machine Translation and Document Search

      • Machine translation systems and their applications.
      • Building a basic machine translation system.
      • Introduction to document search using methods like TF-IDF and BM25.
      • Evaluation metrics for machine translation and information retrieval.
      • Assisted Practices
      • Lesson-end project
    • Sequence Models

      • Introduction to sequence modeling in NLP
      • Recurrent Neural Networks (RNNs) and their applications in NLP tasks
      • Applications of sequence models in sentiment analysis
      • Challenges in training RNNs, such as vanishing gradients
      • Assisted Practices using RNNs for NLP tasks
      • Lesson-end project
    • Attention Models

      • sequence-to-sequence models
      • Introduction to attention mechanisms in NLP
      • In-depth exploration of the Transformer architecture.
      • Modern NLP models like BERT and GPT, which utilize attention mechanisms.
      • Assisted Practices
      • Lesson-end project
    • Introduction to Audio Analytics

      • Important Definitions
      • Overview of the Python ecosystem for audio analytics
      • Introduction to audio file formats (WAV, MP3, etc.)
      • Reading and playing audio files using Python libraries (Librosa, PyDub)
      • Load, visualize, and manipulate audio data
      • Extract basic features (e.g., amplitude, duration)
      • Assisted Practices
      • Lesson-end project
    • Digital Signal Processing and Feature Extraction

      • Basics of signal processing (convolution, Fourier Transform)
      • Frequency domain analysis using Python
      • Introduction to MFCCs and other spectral features
      • Implementation of feature extraction in Python
      • Extract and visualize MFCCs from audio data
      • Compare different feature extraction techniques
      • Assisted Practices
      • Lesson-end project
    • Deep Learning for speech

      • Applications of machine learning in audio
      • Building Deep Learning Models for Speech Recognition
      • Transfer Learning for Speech Recognition
      • Assisted Practices
      • Lesson-end project
    • Audio Synthesis and Generation Generative Models for Audio

      • Introduction to generative adversarial networks (GANs) for audio
      • Generating realistic audio samples using GANs
      • Music Generation with Deep Learning
      • Applying deep learning to generate music
      • Understanding and implementing models for music composition
      • Assisted Practices
      • Lesson-end project
  • Natural Language Processing

    • Section 03 - Practice Projects

      • Twitter Hate
      • Zomato Rating

Exam & Certification

Natural Language Processing Certificate
  • Who provides the certificate and how long it is valid for?

    Once you successfully complete the Natural Language Processing course as part of Artificial Intelligence Engineer Master’s Program, Simplilearn will provide you with an industry-recognized course completion certificate which will have a lifelong validity.

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

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

Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts


  • 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 become an NLP Engineer?

    This course will give you a complete overview of Natural Language Processing, enough to prepare you for a lucrative career as a Natural Language Processing Engineer. You will get Simplilearn’s NLP certification that will be a testament to your new skills and on-the-job expertise.

  • How do beginners learn Natural Language Processing?

    Beginners can learn Natural Language Processing through this NLP certification course. This certification course covers concepts like statistical machine translation and neural models, deep semantic similarity models (DSSM), neural knowledge base embedding, deep reinforcement learning techniques, neural models applied in image captioning, and visual question answering with Python’s Natural Language Toolkit, which can help a beginner learn everything about NLP.

  • Who are the instructors and how they are selected?

    All of Simplilearn’s Natural Language Processing trainers are experienced industry experts. Each of them have gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We at Simplilearn also ensure that only those trainers with a high alumni rating are selected as our faculty.

  • What is online classroom training?

    Online classroom training for the Natural Language Processing certification is conducted via live streaming. The classes are conducted by an NLP certified trainer with more than 15 years of work and training experience to ensure that you learn everything about NLP.

  • Is this live training, or will I watch pre-recorded videos?

    Natural Language Processing course is conducted through live streaming. They are interactive sessions that enable you to ask questions and participate in discussions during class time. We do, however, provide recordings of each session you attend for your future reference. Classes are attended by a global audience to enrich your learning experience.

  • How do I enroll in this Natural Language Processing course?

    You can enroll in this Natural Language Processing course 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.

  • What is Global Teaching Assistance?

    All of Simplilearn’s teaching assistants are subject matter experts who will help you learn Natural Language Processing thoroughly and get certified in the first attempt itself. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

  • Whom should I contact to learn more about this Natural Language Processing course?

    If you want to know more about this NLP certification course, you can contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link in the live chat tab present in all the pages at the bottom right part of the webpage. Our customer service representatives will be able to give you more details.

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