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Learn Natural Language Processing

Teach computers to read, classify and generate human language.

Free to start. Built from a single prompt. No credit card.

Natural language processing, or NLP, is the field of getting computers to work with human language: classifying sentiment, extracting names and dates, translating, answering questions and generating text. The field moved from hand-built rules, to counting words with bag-of-words and TF-IDF, to dense word embeddings, and now to transformer models like BERT and GPT that learn language from enormous text collections. Understanding each stage helps you pick the simplest tool that works. Learnr builds a personalized NLP course and teaches it through short interactive lessons that connect each technique to working Python code.

How to learn Natural Language Processing, step by step

Start with text preprocessing in Python. Tokenize sentences, lowercase, remove or keep punctuation deliberately, and compare stemming with lemmatization using NLTK or spaCy. See how each choice changes the vocabulary of a small corpus.
Build a classic text classifier. Turn reviews or emails into TF-IDF vectors with scikit-learn and train logistic regression to predict sentiment or spam. This baseline is fast, explainable and often surprisingly hard for fancier models to beat on small datasets.
Learn word embeddings. Study how word2vec and GloVe place words with similar contexts near each other in vector space, and use cosine similarity to find neighbors. Notice how embeddings also absorb biases present in their training text.
Understand attention and transformers. Learn subword tokenization, self-attention, positional information and the difference between encoder models like BERT and decoder models like GPT. Drawing the attention computation for a short sentence makes the matrices concrete.
Fine-tune a pretrained model. Use the Hugging Face transformers library to fine-tune a small pretrained model for classification or named entity recognition, and compare it with your TF-IDF baseline using precision, recall and F1 rather than accuracy alone.
Ship a small language project. Build a semantic search over your notes, a support ticket classifier or a summarizer, and document what fails. Short daily Learnr reviews keep the concepts sharp while you iterate.

What you'll learn

Preprocess text with tokenization, normalization and lemmatization
Build text classifiers with bag-of-words, TF-IDF and logistic regression
Explain word embeddings and how they capture meaning
Describe how attention and transformer models like BERT and GPT work
Fine-tune a pretrained transformer and evaluate it with precision, recall and F1

What's inside your Natural Language Processing course

A sample path Learnr builds for Natural Language Processing. Your real course adapts to your goal and level.

1. Text as data: tokens, vocabularies and preprocessing
2. Bag-of-words, TF-IDF and classic classifiers
3. Word embeddings and semantic similarity
4. Sequence models and the attention mechanism
5. Transformers, pretraining and fine-tuning
6. Applications: search, extraction and generation

How long does it take to learn Natural Language Processing?

With Python and basic machine learning, the core of NLP from TF-IDF to fine-tuning transformers takes about 2 to 5 months at 5 to 8 hours a week. Starting without machine learning experience roughly doubles that.

What you need before you start

Python, the basics of machine learning such as training and test splits and classification metrics, and some linear algebra for vectors and dot products. Deep learning basics help for the transformer sections.

Common mistakes to avoid

Jumping straight to large transformer models without a simple TF-IDF baseline, so you cannot tell whether the extra complexity is worth it.
Evaluating on text that overlaps with the training set, such as near-duplicate reviews, which inflates scores.
Treating model outputs as neutral, when embeddings and language models reproduce biases from their training text. Test on varied inputs.

How Learnr builds your course

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Natural Language Processing questions, answered

Is NLP the same as large language models?

No. Large language models are one part of NLP. The field also includes classic tasks like tokenization, tagging, named entity recognition and text classification, many of which are still solved with smaller, cheaper models.

Which Python libraries are used for NLP?

Common choices include NLTK for teaching and classic tools, spaCy for fast production pipelines, scikit-learn for TF-IDF and classic models, and Hugging Face transformers for pretrained transformer models.