Text Preprocessing Courses and Certifications

Text Data Preprocessing in Python

Master text preprocessing techniques in Python, from lowercasing and tokenization to removing stopwords and stemming, using NLTK library and SMS Spam dataset for practical NLP applications.

• CodeSignal
• 3 hours
• On-Demand
• Free Certificate

Packt

NLP – Embeddings & Text Preprocessing in Python

Master the essentials of NLP with text preprocessing techniques, vector models, and neural word embeddings. Learn to transform raw text into usable formats for machine learning through hands-on Python implementations.

• Coursera
• 9 hours 22 minutes
• On-Demand
• Paid Course

Foundations of NLP Data Processing

Master text cleaning, vectorization techniques (TF-IDF, embeddings), modern tokenization, and scalable data pipelines for NLP and LLMs through hands-on practice.

• CodeSignal
• 1 hour
• On-Demand
• Free Certificate

Learn to clean text with Python 3 using regular expressions (regex) and NLTK.

• Codecademy
• 2 hours
• On-Demand
• Free Trial Available

Feature Engineering for NLP in Python

13 ratings at DataCamp

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Microcredential

Introduction to Natural Language Processing

22 ratings at CodeSignal

Dive into foundational NLP concepts, from text preprocessing to machine learning models, while mastering data exploration, vectorization techniques, and text classification using Python and real-world datasets.

• CodeSignal
• 10 hours
• On-Demand
• Free Certificate
• 4 courses

Text Representation Techniques for RAG Systems

Master practical text representation methods for RAG systems, from Bag-of-Words to semantic embeddings, with hands-on JavaScript implementation and performance comparison techniques.

• CodeSignal
• 2 hours
• On-Demand
• Free Certificate

[2026] Machine Learning: Natural Language Processing (V2)

7213 ratings at Udemy

NLP: Use Markov Models, NLTK, Agentic AI, Artificial Intelligence, Machine Learning, and Data Science in Python

• Udemy
• 23 hours 31 minutes
• On-Demand
• Paid Course

Natural Language Processing in Python

778 ratings at Udemy

Learn NLP in Python — text preprocessing, machine learning, transformers & LLMs using scikit-learn, spaCy & Hugging Face

• Udemy
• 12 hours 38 minutes
• On-Demand
• Paid Course

Hands-on Text Mining and Analytics

40 ratings at Coursera

Learn key text mining techniques like preprocessing, sentiment analysis, and topic modeling using real-world datasets and a Java toolkit. Gain hands-on experience to become a competent data scientist.

• Coursera
• 14 hours 8 minutes
• On-Demand
• Paid Course

NLP in Python: Probability Models, Statistics, Text Analysis

54 ratings at Udemy

Master Language Models, Hidden Markov Models, Bayesian Methods & Sentiment Analysis for Real-World Applications

• Udemy
• 6 hours 24 minutes
• On-Demand
• Paid Course

Packt Microcredential

Applied NLP and Generative AI

Master practical NLP and Generative AI skills using Python, from text preprocessing to transformer fine-tuning and deploying real-world applications like chatbots.

• Coursera
• 4 weeks, 10 hours a week
• On-Demand
• Paid Course
• 3 courses

EDUCBA

Chatbots with Keras & NLP: Build & Evaluate

Master chatbot development using Keras & NLP techniques, from text preprocessing to advanced neural architectures with attention mechanisms for real-world AI applications.

• Coursera
• 7 hours 19 minutes
• On-Demand
• Paid Course

Coursera

Preparing Text for AI Models

Master text preprocessing, tokenization, and dataset structuring for training large language models with practical skills in sourcing, cleaning, and formatting data.

• Coursera
• 7 hours 6 minutes
• On-Demand
• Paid Course

EDUCBA

Apply Natural Language Processing Techniques in Python

Master NLP fundamentals and advanced text preprocessing techniques, then integrate machine learning algorithms to build effective models for real-world language problems.

• Coursera
• 4 hours 42 minutes
• On-Demand
• Paid Course