A good way to start with NLP is by building a strong foundation in Python and understanding basic machine learning concepts. Once you’re comfortable with those, learn core NLP tasks like text preprocessing, tokenization, stemming, lemmatization, and vectorization.

After that, explore libraries such as NLTK, spaCy, and scikit-learn by working on simple projects like sentiment analysis, spam detection, or text classification. As you gain confidence, move on to transformer models using Hugging Face and experiment with tasks like question answering, text summarization, and named entity recognition.

The key is to learn by building. Small, practical projects will teach you far more than only reading tutorials, and they’ll also help you create a portfolio that demonstrates your skills.