Natural Language Processing (NLP) with Python Training

Foundations of Statistical Natural Language Processing

  • 30 hours of Instructor led Training
  • Interactive Learning
  • Comprehensive Hands-on with Python
  • Basic to advanced level


This workshop will introduce you to the basics of NLP. You will get started with the NLP toolkit and learn to use Python to process text, extract data from unstructured text, create algorithms and use NLP to solve business issues.

Natural language processing is one of the technologies that drives Artificial Intelligence. Its core functionality is to allow machines to understand human speech. Technologies such as Google Assistant and Alexa use NLP to translate our words into text,  that is then decoded by a complex set of algorithms which can be understood by machines. With the help of NLP it is possible to create intelligent and intuitive machines that can communicate with us.
As more and more companies understand the use and need of NLP, its market revenue is steadily increasing. From 277 million U.S. dollars. In 2015 it is expected to reach 919.3 million U.S. dollars in 2020. This has naturally raised the demand for NLP professionals who are coveted for their skills in creating cutting edge technologies. This is the right time to enrol in this course and get started on a brilliant career in NLP.

What You Will Learn:


Python Programming

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Who Should Attend

  • Data Engineers /Big Data Engineers
  • Software Developers who know C/C++/Java/Python or a similar language
  • You should have experience in R/Python/ Scala
  • You should be comfortable with data wrangling
  • You should have implemented statistical or NLP models in past
  • You should have statistics or mathematics background

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Learning Objectives:

Learn about the interaction between computers and human beings which gives computers the ability to understand human speech with the help of machine learning. Understand the concept behind tokenization and normalization


  • Introduction to Regular Expressions
  • Tokenization of text
  • Normalization of text
  • Substituting and correcting tokens
  • Applying Zipf's law to text
  • Applying similarity measures using the Edit Distance Algorithm
  • Applying similarity measures using Jaccard's Coefficient
  • Applying similarity measures using Smith Waterman


Apply various similarity measures to strings using NLTK

Learning Objectives:

Understand the preprocessing tasks or the computations that can be performed on natural language text. Learn about the ways to calculate word frequencies, the Maximum Likelihood Estimation (MLE) model, interpolation on data, and so on


  • Understanding word frequency
  • Applying smoothing on the MLE model
  • Develop a backup mechanism for MLE
  • Data Interpolation
  • Language modelling using metropolis hastings
  • Gibbs sampling in language processing


Implement Maximum Likelihood Estimation in NLTK and perform language modeling

Learning Objectives:

Learn about stemming and lemmatization, stemmer and lemmatizer for non-English languages, developing a morphological analyzer and morphological generator using machine learning tools, search engines, and many such concepts


  • Introducing Morphology
  • Understanding stemmer
  • Lemmatization
  • Morphological analyzer
  • Morphological generator


Perform preprocessing on the original text in order to implement or build an application. Implement stemming, lemmatization, and morphological analysis and generation in NLTK

Learning Objectives:

Understand the process of finding whether a character sequence, written in natural language, is in accordance with the rules defined in formal grammar. Also, learn about the process of breaking the sentences into words or phrase sequences and providing them a particular component category (noun, verb, preposition, and so on)


  • Introducing Parsing
  • Treebank construction
  • Extracting Context Free Grammar (CFG) rules from Treebank
  • CYK chart parsing algorithm
  • Earley chart parsing algorithm


Implement Context-free Grammar, Probabilistic Context-free Grammar, the CYK algorithm and the Earley algorithm

Learning Objectives:

Understand the process of determining the meaning of character sequences or word sequences which may be used for performing the task of disambiguation


  • Introducing semantic analysis
  • Named-entity recognition (NER)
  • NER system using the HMM
  • Training NER using machine learning toolkits
  • NER using POS tagging
  • Generation of the synset id from Wordnet
  • Disambiguating senses using Wordnet

Learning Objectives:

Understand the process of determining the sentiments behind a character sequence. It may be used to determine whether the speaker or the person expressing the textual thoughts is in a happy or sad mood, or it represents a neutral expression


  • Introducing sentiment analysis
  • Sentiment analysis using NER
  • Sentiment analysis using machine learning
  • Evaluation of the NER system

Learning Objectives:

Understand the process of retrieving information (for example, the number of times the word "Analysis" has appeared in the document) corresponding to a query that has been made by the user


  • Introducing information retrieval
  • Stop word removal
  • Information retrieval using a vector space model
  • Vector space scoring and query operator interactions
  • Text summarization


Implement text summarization, question-answering systems, and vector space models

Learning Objectives:

Understand the process of determining contextual information that is useful for performing other tasks, such as anaphora resolution (AR), NER, and so on


  • Introducing discourse analysis
  • Discourse analysis using Centering Theory
  • Anaphora resolution


Use NLTK to implement first order predicate logic using UML diagrams

Learning Objectives:

Learn to analyze whether a given NLP system produces the desired result or not and the desired performance is achieved or not which may be performed automatically using predefined metrics, or it may be performed manually by comparing human output with the output obtained by an NLP system


  • The need for the evaluation of NLP systems
  • Evaluation of IR Systems
  • Metrics for error identification
  • Metrics based on lexical matching
  • Metrics based on syntactic matching
  • Metrics using shallow semantic matching


Projects Taken by Previous Batch Students

Covers text analysis, semantic analysis, sentiment analysis, and information retrieval.

Determining the meaning of character sequences or word sequences

Implement NER, NER using HMM, NER using Machine Learning Toolkits to determine the meaning of character sequences or word sequences.

Studying the market perceptions in various social networking platforms

Stock market prediction has been an interesting research topic for many years.

Read More

Perform parser evaluation using gold data

Evaluate using three metrics, namely Precision, Recall, F-Measure and perform parser evaluation using gold data.

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The Course

As organizations realize the benefits of AI in driving their business, technologies such as NLP that support AI are becoming the need of the hour.  NLP experts are in much demand as is evident from the number of job postings and salaries they earn. The average salary for "natural language processing" ranges from approximately $74,584 per year for Research Scientist to $144,193 per year for Machine Learning Engineer. This shows the demand there is for NLP experts. Enroll now and master the fundamentals of this technology for a bright future.

After completing our course, you will be able to understand the mathematics behind algorithms and how you can modify them to suit your needs so that you can transition to a Senior Natural Language Processing role.

Tools and Technologies used are

  • Python
  • Natural Language Toolkit (NLTK)

There are no restrictions but participants would benefit if they have knowledge in Python programming and machine learning techniques

Yes, KnowledgeHut offers this training online.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are UI/UX experts who have years of industry experience.

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of the written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.

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