Course Details
This comprehensive course introduces you to the fundamentals of text analytics and natural language processing. Whether you’re a beginner or looking to expand your skills, this course provides practical, hands-on experience with techniques and tools to analyze and extract insights from textual data.
Skills You’ll Master:
- Text preprocessing – Clean, tokenize, and prepare raw text for analysis.
- Sentiment analysis – Identify emotions and opinions expressed in text.
- Keyword and entity extraction – Detect important terms, names, and topics automatically.
- Natural language processing (NLP) basics – Understand core concepts for language understanding.
Apply these skills in real-world projects such as customer feedback analysis, social media monitoring, and automated document classification.
What You’ll Learn
This comprehensive course covers the fundamentals and advanced concepts of text analytics. Here are the key skills and techniques you’ll master:
- Fundamentals of Text Analytics and NLP – Understand the core concepts of text analysis and natural language processing.
- Textual Data and Sources – Learn about different types of textual data and where they come from.
- Text Cleaning and Preprocessing – Techniques for cleaning, normalizing, and preparing text data.
- Tokenization, Stemming, and Lemmatization – Break down text into tokens and reduce words to their base forms.
- Feature Extraction – Use bag-of-words, TF-IDF, and word embeddings to represent text data.
- Sentiment Analysis – Determine the emotional tone of text content.
- Topic Modelling – Uncover hidden themes in large text datasets.
- Named Entity Recognition (NER) – Identify and classify entities like names, dates, and locations.
- Text Data Visualization – Create word clouds, frequency plots, and other visualizations.
- Text Classification and Clustering – Apply machine learning algorithms to categorize and group text.
- Evaluation and Interpretation – Assess and interpret results for business or research purposes.
Pro Tip: This course emphasizes practical, hands-on projects using real-world text datasets, so you can immediately apply your skills in business or research contexts.
Course Content
Requirements
This course is designed to be beginner-friendly and is open to anyone,
regardless of prior experience or background.
Course Description
Text Analytics 101 introduces you to the core concepts, techniques, and
applications of analyzing unstructured text data. You will begin by
understanding the fundamentals of text analytics and the role of natural
language processing (NLP) in extracting insights from text. The course covers
data preprocessing steps such as cleaning, tokenization, stemming, and
lemmatization, along with feature extraction methods like bag-of-words, TF-
IDF, and word embeddings. You will learn how to perform sentiment analysis
to gauge emotional tone, apply topic modeling to uncover hidden themes, and
use named entity recognition to identify people, places, and organizations in
text. The course also explores visualization techniques such as word clouds and
frequency distributions, and guides you through applying machine learning
models for text classification and clustering. By the end of this course, you will
be equipped to analyze, interpret, and present text-based insights for real-
world business and research needs.
Course Content
Our comprehensive curriculum is organized into 8 core modules plus bonus content, designed to take you from beginner to proficient in text analytics and natural language processing techniques.
- General Information
- Learning Objectives
- Syllabus
- Grading Scheme
- Certificate and Badge Information
- Change Log
- Copyrights and Trademarks
- Learning Objectives
- Intro to Info Extraction (IE) – Part 1.1
- Representative IE Tasks – Unit 1.1.2 (3:45)
- Challenges and Requirements for IE – Unit 1.1.3 (8:20)
- Anatomy of an IE System – Part 1.2
- Evaluation
- Review Questions (3 Questions)
- Learning Objectives
- Intro to Grammar-based IE – Unit 1.3.1 (6:16)
- Limitations in Expressivity – Unit 1.3.3 (5:47)
- Limitations in Performance – Unit 1.3.4 (4:27)
- Review Questions (3 Questions)
- Learning Objectives
- Intro to SystemT Data Model (2:40)
- Visual Constructions in SystemT Web Tool (3:12)
- Atomic Constructs (5:07)
- Composite Constructs (1:11)
- Output Refinement (2:34)
- Review Questions (3 Questions)
- Learning Objectives
- Approaching IE Differently – Unit 3.1.1 (6:01)
- Relational Style Statements – SELECT – Unit 3.1.3 (2:34)
- Review Questions (3 Questions)
- Learning Objectives
- Relational Style Statements: UNION ALL – 2.1 (3:54)
- AQL Modules – Unit 3.2.2 (3:35)
- Lab – Watson Knowledge Studio
- Review Questions (3 Questions)
- Learning Objectives
- The DETAG Statement – Unit 3.3.1 (3:42)
- Example 1: Building a Library of UDFs – Unit 3.3.2 (1:58)
- Morphological Support for Multiple Languages – Unit 3.3.3 (1:58)
- Review Questions (3 Questions)
- Learning Objectives
- Declarative IE – Unit 3.4.1 (2:11)
- SystemT Algebra – Unit 3.4.2 (3:11)
- Addressing Limitations – Unit 3.4.3
- Optimization – Unit 3.4.4
- Review Questions (3 Questions)
- Learning Objectives
- High Performance Extractors – Unit 3.5.1 (5:18)
- AQL Profiler – Unit 3.5.2 (4:09)
- AQL Views – Unit 3.5.3
- Review Questions (3 Questions)
- Instructions
- Final Exam (10 Questions) – Timed Exam
- (Optional) Retake Exam
- How to Claim Your Certificate
- Badge
Highlights of this Course:
Upon successful completion of the course, you will receive a Course Completion Certificate and a Project Completion Certificate.
