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Python Text Analysis with NLP Course Overview

Python Text Analysis with NLP Course Overview

Unlock the power of Python for Text Analysis with our comprehensive course on Natural Language Processing (NLP). This course is designed to equip you with essential skills in understanding and analyzing textual data. By the end, you'll be able to effectively manipulate text, perform sentiment analysis, and extract meaningful insights from unstructured data.

Key learning objectives include mastering libraries like NLTK and spaCy, building text classifiers, and developing chatbots. Real-world applications of these concepts will empower you to enhance business intelligence, improve customer interactions, and innovate in various fields such as marketing and data analysis. Join us to dive into the world of NLP and transform your understanding of text data!

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1,700

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Course Fee 1,700
Total Fees
1,700 (USD)
  • Live Training (Duration : 40 Hours)
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  • Classroom Training fee on request
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Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

  • Live Training (Duration : 40 Hours)
  • Per Participant
  • Classroom Training fee on request
Koeing Learning Stack

Koenig Learning Stack

Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

Request More Information

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Target Audience for Python Text Analysis with NLP

Python Text Analysis with NLP equips learners with essential skills for processing and analyzing textual data, making it ideal for various professionals seeking expertise in natural language processing.


  • Data Scientists
  • Machine Learning Engineers
  • NLP Researchers
  • Software Developers
  • Business Analysts
  • Marketing Analysts
  • Academic Researchers
  • IT Professionals
  • Students in Computer Science and Linguistics
  • Content Analysts
  • Social Media Analysts
  • Chatbot Developers


Learning Objectives - What you will Learn in this Python Text Analysis with NLP?

Course Introduction

The Python Text Analysis with NLP course equips students with essential skills to analyze and process textual data, enabling them to leverage Natural Language Processing techniques effectively in their projects.

Learning Objectives and Outcomes

  • Understand the fundamentals of Natural Language Processing (NLP).
  • Utilize Python libraries such as NLTK and SpaCy for text analysis.
  • Perform tokenization, stemming, and lemmatization techniques.
  • Implement sentiment analysis on textual data.
  • Extract key phrases and named entities from texts.
  • Develop text classification models using machine learning.
  • Preprocess and clean textual data for analysis.
  • Visualize textual data insights using appropriate tools.
  • Apply NLP techniques to solve real-world business problems.
  • Gain hands-on experience through practical projects and exercises.

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