Unlock the power of Generative AI with our comprehensive course on Natural Language Processing (NLP) Architectures. This course delves into the three foundational approaches to NLP, providing you with a deep understanding of how machines process and interpret human language.

1. Probabilistic Methods-Based Architectures: Begin your journey by exploring the roots of NLP, where statistical methods form the backbone of early language models. You’ll learn how probabilistic models like n-grams and Hidden Markov Models (HMMs) were used to tackle language tasks such as speech recognition, part-of-speech tagging, and more.

2. Neural Networks-Based Architectures: Move forward to the era of neural networks, where the introduction of deep learning transformed NLP. This section covers the evolution from simple feedforward networks to more complex architectures like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. Understand how these models handle sequential data and their applications in tasks like sentiment analysis, language translation, and text generation.

3. Transformer-Based Architectures: Finally, delve into the cutting-edge of NLP with transformer-based models. Learn how transformers have revolutionized language processing with their ability to capture long-range dependencies and parallelize training, leading to state-of-the-art models. This section provides hands-on experience with these models, allowing you to see firsthand how they power applications like chatbots, summarization, and more.

By the end of this course, you’ll have a solid grasp of the key NLP architectures that have shaped the field, equipping you with the knowledge to apply these techniques to real-world language processing challenges. Whether you are a beginner looking to enter the world of NLP or an experienced professional wanting to update your skills, this course offers the essential insights needed to stay ahead in the evolving landscape of natural language processing.