NLP & Text Analysis
Learn NLP from preprocessing to transformers. Build text classifiers, extract entities, analyze sentiment — 8 hands-on lessons with certificate.
Every time you search Google, ask a voice assistant a question, or get a spam email filtered from your inbox, you’re using NLP. Natural language processing is how machines understand, interpret, and generate human language — and it’s a $36.8 billion industry growing at nearly 20% per year.
This course takes you from raw text to working NLP systems. You’ll learn how to preprocess text, convert words into numbers machines can work with, build classifiers, extract entities, and analyze sentiment. By lesson 7, you’ll understand how transformer models like BERT and GPT power every major language AI today.
Who this is for: Developers, data analysts, and AI practitioners who want to build text-based AI systems. You should be comfortable with basic programming concepts and have a general understanding of machine learning fundamentals.
What you’ll build: By the end of this course, you’ll know how to design NLP pipelines that go from messy text to structured insights — the same pipelines that power search engines, chatbots, document analysis, and content moderation at scale.
What You'll Learn
- Explain how NLP systems process raw text through tokenization, stemming, and lemmatization
- Compare text representation methods from bag-of-words and TF-IDF to Word2Vec and transformer embeddings
- Build text classification pipelines for spam detection, topic labeling, and document categorization
- Apply named entity recognition to extract people, organizations, dates, and locations from unstructured text
- Implement sentiment analysis systems that classify opinions at document and aspect level
- Evaluate when to use BERT, GPT, or T5 architectures for different NLP tasks
After This Course, You Can
What You'll Build
Course Syllabus
Frequently Asked Questions
Do I need to know Python before taking this course?
Basic Python familiarity helps but isn't required. This course focuses on NLP concepts and workflows — you'll learn what each technique does and when to use it, with enough technical depth to start implementing with tools like spaCy and Hugging Face.
How is this different from a general AI or machine learning course?
This course focuses specifically on text data — how to preprocess it, represent it numerically, classify it, extract information from it, and analyze sentiment. General AI courses cover broader topics. This goes deep on NLP.
Will this course cover ChatGPT and other LLMs?
Yes. Lesson 7 covers how transformer models like BERT, GPT, and T5 work for NLP tasks. You'll learn when to use each architecture and how they compare for classification, extraction, and generation.
What career opportunities does NLP knowledge open up?
NLP engineers earn $107K-$170K on average, and NLP is the most requested AI skill — appearing in 19.7% of AI job postings. Roles include NLP engineer, ML engineer, data scientist, and AI product manager.
