Embark on a comprehensive learning journey through Natural Language Processing (NLP) and Large Language Models (LLMs). Starting with NLP basics—text preprocessing, representation, and classification—you’ll progress to deep learning methodologies and conclude with the most advanced technologies, including attention mechanisms and transformer-based LLMs.
From understanding core concepts to implementing real-world applications like translation and summarization, this course empowers you to stay ahead in the evolving world of NLP and AI.
This course is ideal for:
This Learning Kit with more than 21 hours of learning is divided into three tracks:
This track provides a comprehensive introduction to the core concepts and techniques in NLP. Beginning with an overview of NLP components, including natural language understanding (NLU) and natural language generation (NLG), the track explores common NLP tasks such as speech recognition and sentiment analysis. Participants will then delve into preprocessing text data using NLTK, covering essential techniques such as text cleaning, sentence segmentation, and parts-of-speech tagging. Additionally, the track explores methods for representing text in numeric format, including one-hot encoding and TF-IDF encoding, before introducing classification models for text data. Through hands-on exercises and practical examples, participants will learn how to build classification models using rule-based approaches, Naive Bayes classification, and other techniques, leveraging tools like Scikit-learn pipelines and grid search for optimal performance. Participants will then harness the power of TensorFlow for building deep learning models, followed by an in-depth exploration of text preprocessing techniques such as normalization, tokenization, and text vectorization. Through hands-on exercises, learners will delve into the intricacies of modeling building, training, and evaluation for text classification tasks, encompassing binary classification and multi-class classification using dense neural networks, recurrent neural networks (RNNs), and RNNs with LSTM cells. The track will also cover hyperparameter tuning using the Keras tuner to optimize model performance. Participants will gain proficiency in leveraging word embeddings, including training embedding layers in models, exploring and visualizing embeddings, and utilizing embeddings for tasks like word and semantic similarity. Moreover, the track will explore text translation using RNNs and demonstrate the utilization of pre-trained models for semantic textual similarity, providing participants with a comprehensive understanding of cutting-edge NLP techniques in the context of deep learning.
This track is designed to immerse participants in the transformative world of Large Language Models (LLMs), leveraging state-of-the-art techniques powered by deep learning and attention mechanisms. Participants will gain a deep understanding of attention mechanisms and the revolutionary transformer architecture, including self-attention and multi-head attention mechanisms. Through hands-on exercises and practical demonstrations, learners will explore the foundational concepts of LLMs and delve into implementing translation models using transformers. Moreover, participants will be introduced to the Hugging Face platform, learning to leverage pre-trained models from the Hugging Face library and fine-tune them for specific use cases. From text classification to language translation, question answering, text summarization, and natural language generation, participants will acquire the skills needed to harness the full potential of LLMs for a wide range of NLP tasks.
