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TEXT Analysis with Python ( NLP) Course Overview

TEXT Analysis with Python ( NLP) Course Overview

The "Text Analysis with Python (NLP)" course provides comprehensive natural language processing training designed to equip learners with the skills necessary to analyze, understand, and generate insights from textual data. Starting with the Natural Language Basics, students are introduced to the fundamentals of linguistics and language structure, ensuring a solid foundation in the principles underpinning natural language processing.

Through the subsequent modules, participants will receive a Python refresher, covering essential programming concepts and Regex, which are crucial for handling text data. The course then dives into practical NLP techniques, including Text tokenization, Text classification, and Sentiment analysis, providing hands-on experience with real-world applications.

By the end of this natural language processing course, learners will be adept at performing Text summarization, understanding Text similarity, and Clustering, as well as Semantic analysis. This training not only imparts theoretical knowledge but also emphasizes practical skills, preparing students to tackle NLP challenges across various domains.

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Course Prerequisites

To successfully undertake the Text Analysis with Python (NLP) course offered by Koenig Solutions, the following minimum prerequisites are recommended:


  • Basic understanding of programming concepts.
  • Familiarity with Python programming language, including:
    • Basic syntax and structure of Python.
    • Working with data structures such as lists, dictionaries, and tuples.
    • Control flow statements (if, for, while loops, etc.).
    • Basic understanding of functions and classes in Python.
  • Elementary knowledge of regular expressions (Regex) in Python.
  • Basic understanding of linguistic concepts and the structure of natural language.
  • Proficiency in English, as it is often the primary language used in natural language processing (NLP) tasks and examples.
  • Willingness to learn and explore new concepts in natural language processing and machine learning.

No advanced knowledge of linguistics, machine learning, or deep learning is required to start this course, as foundational concepts will be covered within the training. However, a keen interest in text analysis and processing will greatly enhance the learning experience.


Target Audience for TEXT Analysis with Python ( NLP)

Koenig Solutions' TEXT Analysis with Python (NLP) course is designed for professionals seeking to leverage natural language processing using Python.


Target Audience for the Course:


  • Data Scientists and Analysts
  • Machine Learning Engineers
  • Software Developers with an interest in NLP
  • Business Intelligence Professionals
  • AI and NLP Researchers
  • Text Mining and Data Mining Specialists
  • Computational Linguists
  • BI and Data Visualization Experts
  • IT Professionals looking to expand their skill set in NLP
  • Academic Researchers and Graduate Students in Computer Science
  • Product Managers and Strategists focusing on AI-driven products
  • Content Strategists and Web Analytics Professionals
  • SEO Specialists interested in content analysis and optimization
  • Digital Marketers seeking to understand customer sentiment


Learning Objectives - What you will Learn in this TEXT Analysis with Python ( NLP)?

Introduction to the Course's Learning Outcomes and Concepts Covered

This course equips students with the tools and techniques for advanced text analysis using Python, covering everything from natural language basics to sentiment analysis.

Learning Objectives and Outcomes

  • Understand the fundamentals of natural language and linguistics, including language syntax, structure, and processing.
  • Refresh Python skills, focusing on syntax, data structures, control flow, functional programming, classes, and regular expressions.
  • Learn to process and understand text through tokenization, normalization, and syntax analysis.
  • Master text classification by understanding its concepts, creating normalization workflows, extracting features, employing classification algorithms, and evaluating model performance.
  • Gain knowledge in text summarization techniques, including information extraction, keyphrase extraction, topic modeling, and automated document summarization.
  • Develop skills in text similarity and clustering, learning about information retrieval, feature engineering, term/document similarity analysis, and document clustering.
  • Explore semantic analysis, including working with WordNet, word sense disambiguation, and named entity recognition for deeper text comprehension.
  • Implement sentiment analysis to determine the emotional tone behind texts, which is pivotal for understanding user-generated content.
  • Apply practical hands-on knowledge to real-world datasets, enhancing skills in analyzing and interpreting complex text data.
  • Acquire the ability to build and evaluate NLP models effectively, preparing for further exploration or professional application of natural language processing.

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