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This course is part of multiple programs.
Instructors: Joseph Santarcangelo
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Intermediate level
Machine learning and neural network fundamentals. Working knowledge of Python. Knowledge of PyTorch is an asset, but not critical.
(134 reviews)
Recommended experience
Intermediate level
Machine learning and neural network fundamentals. Working knowledge of Python. Knowledge of PyTorch is an asset, but not critical.
Differentiate between generative AI architectures and models, such as RNNs, Transformers, VAEs, GANs, and Diffusion Models.
Describe how LLMs, such as GPT, BERT, BART, and T5, are used in language processing.
Implement tokenization to preprocess raw textual data using NLP libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer.
Create an NLP data loader using PyTorch to perform tokenization, numericalization, and padding of text data.
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This IBM short course, a part of Generative AI Engineering Essentials with LLMs Professional Certificate, will teach you the basics of using generative AI and Large Language Models (LLMs). This course is suitable for existing and aspiring data scientists, machine learning engineers, deep-learning engineers, and AI engineers.
You will learn about the types of generative AI and its real-world applications. You will gain the knowledge to differentiate between various generative AI architectures and models, such as Recurrent Neural Networks (RNNs), Transformers, Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), and Diffusion Models. You will learn the differences in the training approaches used for each model. You will be able to explain the use of LLMs, such as Generative Pre-Trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT). You will also learn about the tokenization process, tokenization methods, and the use of tokenizers for word-based, character-based, and subword-based tokenization. You will be able to explain how you can use data loaders for training generative AI models and list the PyTorch libraries for preparing and handling data within data loaders. The knowledge acquired will help you use the generative AI libraries in Hugging Face. It will also prepare you to implement tokenization and create an NLP data loader. For this course, a basic knowledge of Python and PyTorch and an awareness of machine learning and neural networks would be an advantage, though not strictly required.
In this module, you will learn about the significance of generative AI models and how they are used across a wide range of fields for generating various types of content. You will learn about the architectures and models commonly used in generative AI and the differences in the training approaches of these models. You will learn how large language models (LLMs) are used to build NLP-based applications. You will build a simple chatbot using the transformers library from Hugging Face.
5 videos2 readings2 assignments1 app item3 plugins
In this module, you will learn to prepare data for training large language models (LLMs) by implementing tokenization. You will learn about the tokenization methods and the use of tokenizers. You will also learn about the purpose of data loaders and how you can use the DataLoader class in PyTorch. You will implement tokenization using various libraries such as nltk, spaCy, BertTokenizer, and XLNetTokenizer. You will also create a data loader with a collate function that processes batches of text.
2 videos5 readings2 assignments2 app items2 plugins
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
At IBM, we know how rapidly tech evolves and recognize the crucial need for businesses and professionals to build job-ready, hands-on skills quickly. As a market-leading tech innovator, we’re committed to helping you thrive in this dynamic landscape. Through IBM Skills Network, our expertly designed training programs in AI, software development, cybersecurity, data science, business management, and more, provide the essential skills you need to secure your first job, advance your career, or drive business success. Whether you’re upskilling yourself or your team, our courses, Specializations, and Professional Certificates build the technical expertise that ensures you, and your organization, excel in a competitive world.
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Reviewed on Jan 2, 2025
It was very informative and I enjoyed the journey I learned the patterns from the deep.
Reviewed on Oct 17, 2024
I am pretty much new to NLP data preparation. However this course made me comfortable with Date preparation activities.
Reviewed on Oct 20, 2024
I highly recommend using a human to deliver the lectures, which might enhance student engagement. Great introductory course.
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It will take only two weeks to complete this course if you spend two hours of study time per week.
It will be good if you have a basic knowledge of Python and PyTorch and a familiarity with machine learning and neural network concepts.
This course is part of a specialization. When you complete the specialization, you will prepare yourself with the skills and confidence to take on jobs such as AI Engineer, NLP Engineer, Machine Learning Engineer, Deep Learning Engineer, and Data Scientist.
Only a modern web browser is required to complete this course and all hands-on labs.
You will be provided access to cloud-based environments to complete the labs at no charge.
You will sign up for platforms such as Hugging Face and use functionalities that are not charged.
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