Building Practical Skills in NLP and Generative AI

Course 1293

  • Duration: 2 days
  • Language: English
  • Level: Advanced

Welcome to the 2-day Building Practical Skills in NLP and Generative AI course! This exciting course offers a comprehensive introduction to the rapidly expanding field of Generative AI, home to some of the most advanced AI models, including ChatGPT, developed by OpenAI. 

You will explore from the fundamental concepts of Natural Language Processing (NLP) to the advanced architectures in Generative AI, all structured in easy-to-follow modules coupled with hands-on labs.

Skills in NLP and Generative AI Training Delivery Methods

  • In-Person

  • Online

Skills in NLP and Generative AI Training Information

This course will address the following pain points:

  • Understanding of NLP and Generative AI: This course starts with basic principles and gradually introduces more complex concepts, helping participants build a solid understanding of both NLP (Natural Language Processing) and Generative AI.
  • Applying Advanced AI Techniques: With practical lab sessions, this course provides hands-on experience applying advanced AI techniques like GRUs (Gated Recurrent Units), LSTMs (long short-term memory), and Transformer architectures.
  • Transitioning from traditional NLP to modern methods: The course guides participants from traditional NLP techniques, such as Bag-of-Words, to modern methods involving word embeddings and advanced neural networks, easing this often-challenging transition.
  • Lack of practical AI experience: The numerous lab sessions in this course ensure that participants get to implement what they learn, translating theoretical knowledge into practical skills.

Training Prerequisites

  • To get the most out of this course, it would be helpful to have a basic understanding of Python programming since most examples and labs will use this language.
  • Some familiarity with general concepts of machine learning would be beneficial, but not strictly necessary, as the course is designed to gradually introduce more complex concepts and techniques in AI.
  • No advanced mathematical or deep learning knowledge is required upfront but it will be helpful.

Skills in NLP and Generative AI Training Outline

Module 1: Introduction to Natural Language Processing (NLP)

  • Overview of NLP
  • Importance and applications of NLP

Module 2: Traditional NLP Techniques

  • Understanding Bag-of-Words (BoW) approach
  • Limitations of BoW and transitioning to more advanced techniques

Lab 1: Text Classification using Bag-of-Words

  • In this lab, you will apply BoW to build a simple text classification model.

Module 3: Moving to Word Embeddings

  • Introduction to word embeddings
  • Importance and advantages of word embeddings over BoW
  • Understanding Word2Vec and GloVe

Module 4: Applying NLP - Sentiment Analysis Case Study

  • Walkthrough: Building a restaurant review sentiment analysis model
  • Analysing and interpreting the results

Lab 2: Restaurant Review Sentiment Analysis

  • Here, you'll implement a sentiment analysis model to classify restaurant reviews.

Module 5: Dealing with Sequential Data in NLP

  • Understanding the nature of sequential data
  • Importance of order and context in text data

Module 6: Introduction to Recurrent Neural Networks (RNNs)

  • Concept and architecture of RNNs
  • How RNNs handle sequential data

Module 7: Overcoming the Shortcomings of RNNs with Gated Recurrent Units (GRUs)

  • Understanding the limitations of RNNs
  • Introduction to GRUs and how they address RNNs' limitations

Lab 3: Text Generation using RNNs and GRUs

  • In this lab, you will design and train an RNN with GRUs for text generation.

Module 8: Exploring Long Short-Term Memory Networks (LSTM)

  • Understanding LSTM architecture
  • How LSTM handles long sequences and prevents vanishing gradients

Lab 4: Implementing LSTM for Text Generation

  • You'll get hands-on experience with LSTMs by using them to generate text.

Module 9: Introduction to Autoencoders

  • Understanding the concept and architecture of Autoencoders
  • Autoencoders in generative models

Module 10: Sequence-to-Sequence Models (Seq2Seq)

  • Understanding Seq2Seq models
  • Applications of Seq2Seq models

Lab 5: Implementing a Seq2Seq Model for Machine Translation

  • In this lab, you will use a Seq2Seq model to build a simple machine translation system.

Module 11: Enhancing Seq2Seq with Attention Mechanisms

  • Introduction to attention mechanisms
  • How attention improves the performance of Seq2Seq models

Module 12: Transformers and Their Impact

  • Understanding the architecture and principle of Transformers
  • Transformers in NLP: From BERT to GPT, including the groundbreaking ChatGPT

Lab 6: Text Classification using Transformers

  • You'll work with Transformers, the technology behind ChatGPT, to build a text classification model.

Module 13: Exploring Language Models (LLMs)

  • Understanding the concept of LLMs
  • Importance and applications of LLMs

Module 14: Deep and Wide Architectures in AI

  • Exploring deep and wide architectures
  • Choosing between deep and wide models based on use-cases

Module 15: Conclusion and Further Study

  • Review of key concepts from the course
  • Discussion on future trends and developments in generative AI, including continued advancements in models like ChatGPT
  • Suggestions for further reading and study

Need Help Finding The Right Training Solution?

Our training advisors are here for you.

Skills in NLP and Generative AI Training FAQs

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

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.