User: NLPenthusiast_new
Subject: Best way to learn NLP and text processing?

Want to get into natural language processing but dont know where to start. I have basic Python knowledge and some machine learning basics. What resources or courses do you recommend for learning NLP? Should I start with traditional NLP or jump directly to transformers and BERT?

Reply by: NLP_Researcher_PhD
Start with fundamentals first. Learn about tokenization, stemming, lemmatization, POS tagging, named entity recognition. Understand TF-IDF and word embeddings like Word2Vec. Then move to sequence models like RNNs and LSTMs. Only after this foundation move to transformers. If you jump directly to BERT without understanding basics, you wont really understand what's happening under the hood.

Reply by: Practical_NLP_Engineer
I disagree slightly. You can start with modern approaches using libraries like Hugging Face Transformers. Use pre-trained models for your projects and learn by doing. Understanding every detail of traditional NLP is not necessary unless you're doing research. Most real world applications now use transfer learning with pre-trained models anyway. Learn the basics but dont spend months on theory.

Reply by: DataScience_Mentor
Both approaches have merit. I suggest do 70-30 split. Spend 30% time on fundamentals and 70% on modern approaches. This way you have enough foundation to understand concepts but also get hands-on experience with state of art models. Resources: NLTK library for basics, Hugging Face tutorials for transformers, fast.ai NLP course is also excellent and free.