Learn Building Practical Skills in NLP and Generative AI in Just 3 Days

• Module 1: Introduction to Generative AI
• Overview of Generative AI and its evolution.
• Introduction to Large Language Models (LLMs).

• Understanding Tokens, Embeddings, and Transformers.
• Architectural insights into NLP systems.

• Exploration of real-world applications of LLMs in various sectors.
• Future visions in AI technologies.
• Lab 1: Hands-On with LangChain and VectorDB
• Using LangChain tools and VectorDB for enhanced NLP workflows.

• Module 4: Prompt Engineering Essentials
• Fundamentals of crafting effective prompts for AI.
• Techniques for refining AI outputs and iterative prompt engineering.

• In-depth exploration of Bag-of-Words, TF-IDF, and modern word embeddings.
• Utilizing Python for complex NLP tasks.
• Lab 2: Building Advanced NLP Models
• Implementing practical NLP solutions using advanced techniques.

• Deep dive into RNNs, LSTMs, and the use of attention mechanisms.
• Lab 3: Implementing LSTM for Text Generation
• You'll get hands-on experience with LSTMs by using them to generate text.

• Module 7: Understanding Advanced Generative Models
• Overview of Seq2Seq, Autoencoders, and the innovation of attention in these models.
• Lab 4: Implementing a Seq2Seq Model for Machine Translation
• In this lab, you will use a Seq2Seq model to build a simple machine translation system.

• Comparative analysis of GPT and BERT architectures.
• Understanding their applications and advancements.
• Lab 5: Applying LLMs in Predictive Analytics
• Practical session on leveraging LLMs for data augmentation and analysis.

This 3-day instructor-led course covers the fundamentals and practical applications of Natural Language Processing (NLP), Generative AI, and Large Language Models (LLMs). Topics include tokens, embeddings, Transformers, prompt engineering, traditional and modern NLP techniques, RNNs, LSTMs, Seq2Seq models, GPT, BERT, LangChain, VectorDB, machine translation, predictive analytics, and AI ethics. The course combines instructor-led learning with hands-on labs so participants can apply NLP and Generative AI concepts in practical scenarios.

After completing the course, you’ll have a practical understanding of how NLP systems, Generative AI models, and LLMs are designed and used. You’ll be able to work with prompts, embeddings, language models, Python-based NLP techniques, LangChain, vector databases, sequential models, and modern Transformer architectures. You’ll also gain hands-on experience building text-generation, machine-translation, and LLM-powered analytics solutions.

The course begins with the foundations of Generative AI, NLP, and Large Language Models, so participants do not need to already understand every advanced AI concept. However, the training becomes progressively more technical and includes Python, deep learning architectures, LSTMs, Seq2Seq models, and NLP model development. Basic programming and familiarity with machine learning concepts can therefore be helpful.

Traditional NLP focuses on enabling computers to process, analyze, classify, and understand human language, while Generative AI can create new content such as text based on learned patterns. This course covers both approaches, including traditional techniques such as Bag-of-Words and TF-IDF as well as modern approaches involving embeddings, Transformers, GPT, BERT, and LLMs.

The course covers several important NLP and deep learning architectures, including RNNs, LSTMs, Seq2Seq models, Autoencoders, Transformers, GPT, and BERT. You’ll learn how these architectures work, how they differ, and where they can be applied to NLP and Generative AI problems.

The course concludes by examining the future of Generative AI, NLP, Large Language Models, and emerging AI technologies. It also addresses the ethical considerations associated with AI, helping participants understand both the technical opportunities and broader challenges surrounding the continued development of Generative AI.