Natural Language Processing and LLMs E-Learning

Dive into our Natural Language Processing and LLMs Training series. Start with the Fundamentals of NLP to build a strong base in text preprocessing, representation, and classification. Advance to Deep Learning for NLP to master sophisticated techniques, and explore LLMs with cutting-edge attention mechanisms and transformer architectures. Learn to implement LLMs for tasks like language translation and text summarization. Book now to stay at the forefront of NLP innovation!

This Learning Kit with more than 21 hours of learning is divided into three tracks:

Course content

Track 1: Natural Language Processing

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.

Courses:

• Fundamentals of NLP: Introducing Natural Language Processing
• Fundamentals of NLP: Preprocessing Text Using NLTK and SpaCy
• Fundamentals of NLP: Rule-based Models for Sentiment Analysis
• Fundamentals of NLP: Representing Text as Numeric Features
• Fundamentals of NLP: Word Embeddings to Capture Relationships in Text
• Natural Language Processing Using Deep Learning
• Using Recurrent Networks For Natural Language Processing
• Using Out-of-the-Box Transformer Models for Natural Language Processing
• Attention-based Models and Transformers for Natural Language Processing

Assessment:

• Final Exam: Natural Language Processing Fundamentals

Track 2: Architecting LLM for your Technical solutions

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.

Courses:

• NLP with LLMs: Working with Tokenizers in Hugging Face
• NLP with LLMs: Hugging Face Classification, QnA, & Text Generation Pipelines
• NLP with LLMs: Language Translation, Summarization, & Semantic Similarity
• NLP with LLMs: Fine-tuning Models for Classification & Question Answering
• NLP with LLMs: Fine-tuning Models for Language Translation, & Summarization

Assessment:

• Final Exam: Architecting LLMs for Your Technical Solutions