Building Practical Skills in NLP and Generative AI

Course 1293

  • Duration: 3 days
  • Labs: Yes
  • Language: English
  • Level: Advanced

Welcome to the 3-day intensive course on Building Practical Skills in NLP and Generative AI! This course is designed to equip you with a deep understanding and practical skills in the latest developments in Natural Language Processing (NLP) and Generative AI technologies.

By exploring foundational principles, advanced techniques, and real-world applications, this course will enable you to navigate and harness the capabilities of state-of-the-art AI models.

Skills in NLP and Generative AI Training Delivery Methods

  • In-Person

  • Online

  • Upskill your whole team by bringing Private Team Training to your facility.

Skills in NLP and Generative AI Training Information

This course will address the following pain points:

  • Foundation Building: Gain a solid understanding of the core concepts behind Generative AI and NLP.
  • Advanced Techniques: Learn about the latest advancements in AI technologies including Transformers, GPT, and BERT architectures.
  • Hands-On Application: Participate in hands-on labs to apply concepts in real-world scenarios.
  • Industry Insight: Understand the applications of these technologies across various industries.

Training Prerequisites

  • Basic knowledge of Python programming is required as labs and examples use Python.
  • Familiarity with general machine learning concepts is recommended but not essential.
  • No advanced mathematical or deep learning knowledge is required upfront.

Skills in NLP and Generative AI Training Outline

Module 1: Introduction to Generative AI

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

Module 2: Core Concepts of NLP

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

Module 3: Practical Applications

  • 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.

Module 5: Advanced NLP Techniques

  • 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.

Module 6: Introduction to Sequential Models

  • 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.

Module 8: Deep Learning Architectures

  • 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.

Module 9: Future of AI and Wrap-Up

  • Discussions on the ethical implications and future trends in AI.
  • Review of the course content and guidance for further learning.

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Skills in NLP and Generative AI Training FAQs

  • Data Scientists
  • AI Engineers
  • Research Scientists
  • AI Product Managers
  • AI Consultants and Developers

This course is structured to ensure a progressive learning curve, leading you from basic concepts to complex applications, and is filled with interactive labs to cement your knowledge through practical application. Join us to advance your skills in NLP and Generative AI, paving the way for innovative solutions in your professional field.

As a data scientist, you are probably well-versed in analytical and predictive models. This course will introduce you to generative models, which can create new data instances. You will gain knowledge of cutting-edge models like transformers and gain hands-on experience with various applications like text generation and sentiment analysis.

Although the course's examples and labs use Python, the primary focus is on grasping AI concepts and not on the programming language itself. Python and Java share many similarities, and with your programming experience, you should quickly pick up the Python syntax used in machine learning libraries like TensorFlow and PyTorch.

The course indeed covers the principles of the transformer architecture and the engine behind models like GPT-4. You will delve into transformers' self-attention mechanisms and how they are employed in state-of-the-art models. The course may not explicitly cover GPT-4, but the principles taught will enable you to understand it and similar models.

The course is designed to give you practical experience with various NLP techniques and generative AI architectures. Lab sessions involve coding tasks such as implementing Bag-of-Words for text classification, creating sentiment analysis models, and applying LSTM and transformer architectures for text generation. You will primarily be working with established libraries like TensorFlow or PyTorch.

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