Natural Language Processing (NLP) is at the core of AI-driven communication, enabling machines to understand, interpret, and generate human language. From chatbots to sentiment analysis, NLP is revolutionizing industries. This course will provide hands-on experience in text processing, machine learning for NLP, and state-of-the-art transformer models like BERT and GPT. Learn how to build intelligent language-based applications using real-world datasets and AI frameworks.
This course covers the fundamental concepts of NLP, including tokenization, lemmatization, Named Entity Recognition (NER), and deep learning-based NLP models. You will explore libraries like NLTK, spaCy, and Hugging Face Transformers to develop applications such as chatbots, language translation, and speech recognition. Through practical exercises, you will learn to fine-tune NLP models for various AI applications.
- Fundamentals of NLP and text preprocessing.
- Tokenization, stemming, and lemmatization techniques.
- Sentiment analysis and Named Entity Recognition (NER).
- Deep learning for NLP using LSTMs, BERT, and GPT.
- Building AI-powered chatbots and voice assistants.
- Real-world NLP applications in business and healthcare.
Upon completing this course, you will receive a Techbriq certification in NLP, demonstrating your ability to build and deploy AI-driven language applications using state-of-the-art models.
- Overview of NLP and its applications
- Challenges in natural language understanding
- Introduction to NLP libraries (NLTK, spaCy, Hugging Face)
- Tokenization, stemming, and lemmatization
- Stopwords removal and part-of-speech tagging
- Word embeddings (Word2Vec, GloVe, FastText)
- Recurrent Neural Networks (RNNs) and LSTMs
- Transformer models (BERT, GPT, T5)
- Fine-tuning pre-trained NLP models
- Sentiment analysis on real-world datasets
- Named Entity Recognition (NER) application
- Developing a chatbot using NLP models
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