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Building Practical Skills in NLP and Generative AI

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

Day 1: Understanding NLP and Sequential Data Processing

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.

Day 2: Deep Dive into Advanced Neural Networks and Generative AI

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

Get This Course 20 930 kr

2-day Instructor-Led Training
Hands-On labs & Sandbox
End-of-course Learning Tree exam available
After-course Instructor Coaching Included

Get the Advantage 35 495 kr

Take this course and gain unlimited access to more than 300 virtual instructor-led courses
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Build real skills through hands-on learning in more than 180 virtual labs
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nov 6 - 7 9:00 - 16:30 CET

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jan 22 - 23 15:00 - 22:30 CET

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feb 5 - 6 9:00 - 16:30 CET

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feb 20 - 21 15:00 - 22:30 CET

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mar 25 - 26 14:00 - 21:30 CET

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apr 22 - 23 15:00 - 22:30 CEST

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maj 8 - 9 9:00 - 16:30 CEST

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maj 8 - 9 10:00 - 18:00 CEST

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maj 21 - 22 15:00 - 22:30 CEST

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jun 24 - 25 15:00 - 22:30 CEST

Herndon, VA or Virtual
jul 22 - 23 15:00 - 22:30 CEST

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aug 5 - 6 9:00 - 16:30 CEST

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aug 5 - 6 10:00 - 18:00 CEST

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aug 19 - 20 15:00 - 22:30 CEST

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sep 17 - 18 15:00 - 22:30 CEST

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Take this course and gain unlimited access to more than 300 virtual instructor-led courses
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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

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.

Building Practical Skills in NLP and Generative AI

Skills in NLP and Generative AI Training Delivery Methods

Skills in NLP and Generative AI Training Information

Skills in NLP and Generative AI Training Outline

Day 1: Understanding NLP and Sequential Data Processing

Day 2: Deep Dive into Advanced Neural Networks and Generative AI

Upgrade your course experience with: Subscriptions

Get the Advantage 35 495 kr

Thank you for choosing subscriptions!

Upgrade your course experience with: Subscriptions

Get the Advantage 35 495 kr

Thank you for choosing subscriptions!

Skills in NLP and Generative AI Training FAQs

Who should attend this course?

I am a data scientist. How can this course advance my understanding of generative models?

My programming background is mainly in Java, but I have basic Python skills. How steep will the learning curve be for the programming aspect of the course?

Will the course touch on the latest developments like GPT-4 and their underlying architectures?

What sort of hands-on tasks will the course involve? Will I code my models from scratch?

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