This Natural Language Processing (NLP) Full Course is designed to take learners from beginner to advanced level in understanding how machines process human language. It starts with core NLP concepts such as tokenization, stemming, lemmatization, and text vectorization, which form the foundation of text processing in artificial intelligence.
The course then moves into machine learning techniques applied to NLP, including supervised and unsupervised learning, decision trees, random forests, and support vector machines. Learners also explore how neural networks and deep learning models are used to improve language understanding and prediction accuracy.
Advanced sections cover modern AI technologies such as recurrent neural networks (RNNs), transformers, and large language models (LLMs). It also introduces Generative AI concepts, ChatGPT, prompt engineering, LangChain, and Retrieval-Augmented Generation (RAG), which are essential for building intelligent AI systems.
Practical coding examples using Python libraries like NLTK, spaCy, and Hugging Face help learners gain hands-on experience. By the end of this course, learners will understand how NLP systems are built and will be able to develop real-world applications such as text classification, sentiment analysis, and AI-powered chat systems.
