NLP: Natural Language Processing with Python

Jose Portilla builds NLP foundations through text handling, linguistic processing, and machine-learning applications. The course uses established Python libraries such as spaCy, NLTK, and scikit-learn. It is useful when you want to understand the steps between raw text and a classifier instead of beginning with a chatbot interface.

What you will learn

• Process text files and pattern matches
• Apply tokenization and linguistic analysis
• Prepare text features for models
• Build and evaluate text classification workflows

Who this suits

Best suited to someone comfortable with Python basics and introductory data work. Familiarity with machine learning will help in the modeling sections. Practice on a small text collection whose labels and errors you can inspect.

Before you enroll

The listing reports an April 2023 update. Setup, spaCy models, and deep-learning dependencies may need adaptation. Treat it as foundational NLP material rather than a current LLM application course, and consult current library documentation when an example differs.

Paid course access follows Udemy’s current purchase or subscription terms. Check regional pricing, plan inclusion, and completion-certificate conditions on the provider page.

Put it into practice

Build a simple text classifier and inspect ten mistakes. Compare preprocessing choices against the same held-out set instead of accepting the first accuracy figure.

Source and review

This overview is based on the provider's published information, checked on 11 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Udemy.

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