H2O ai Large Language Models (LLMs) - Level 1
Learn the fundamentals of LLMs, from neural networks to transformer architecture, for natural language processing applications.
Learn the fundamentals of LLMs, from neural networks to transformer architecture, for natural language processing applications.
Begin your exploration of Large Language Models (LLMs) with this foundational Level 1 course! Tailored for both beginners and those with some machine learning experience, this course provides a deep understanding of essential concepts and techniques in language modeling. Led by Andreea Turcu, H2O ai's expert in AI education, you will start by learning what a language model is and its crucial role in natural language understanding. We'll explore the evolution of these models and delve into the techniques used to develop and refine them. The course also highlights real-world applications across industries, demonstrating the transformative power of LLMs. You will also gain a strong foundation in neural networks and deep learning, essential for mastering advanced AI techniques. A significant portion of the course focuses on transformer architecture, the backbone of modern LLMs, and compares it with other architectures to highlight key innovations. We'll guide you through the methodologies of pre-training and fine-tuning LLMs, emphasizing transfer learning and domain-specific adaptation.
Instructors:
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What you'll learn
Understand the definition and role of language models in natural language understanding
Explore the evolution and techniques commonly used in language models
Identify real-world applications of language models across various industries
Master the basics of neural networks, deep learning, and transformer architecture
Learn about pre-training and fine-tuning methodologies for LLMs
Gain knowledge about transfer learning and domain-specific adaptation
Skills you'll gain
This course includes:
59 Minutes PreRecorded video
1 assignment
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FullTime access
Shareable certificate
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There are 4 modules in this course
This foundational course introduces learners to Large Language Models (LLMs) and their applications. The curriculum covers essential concepts starting with the definition and evolution of language models, exploring their real-world applications across various industries. Students will build a strong foundation in neural networks and deep learning, with special focus on transformer architecture - the backbone of modern LLMs. The course also examines pre-training and fine-tuning methodologies, emphasizing transfer learning and domain-specific adaptation. Led by Andreea Turcu, H2O ai's expert in AI education, this course bridges theoretical knowledge with practical applications, preparing learners for more advanced studies in the field.
Introduction to Language Models
Module 1 · 21 Minutes to complete
Understanding Transformers
Module 2 · 26 Minutes to complete
Advanced Techniques in LLM
Module 3 · 12 Minutes to complete
Course Completion Quiz
Module 4 · 15 Minutes to complete
Fee Structure
Instructor
Data Science and AI Strategy Leader
Andreea Turcu has established herself as a Data Scientist with over 7 years of experience specializing in AI platforms and business transformation. Her expertise lies in translating complex AI concepts into practical business advantages, focusing on creating competitive edges through data-driven solutions. Her perspective that "AI is essentially Economics, turbocharged by data, with a sprinkle of innovation" reflects her practical approach to implementing AI solutions. As a specialist in making AI and Data Science accessible, she develops engaging training programs and integrates machine learning into real-world applications. Her work focuses on bridging the gap between technical AI capabilities and business outcomes, helping organizations navigate the transition to AI-driven operations while maintaining a focus on practical, measurable results. Her approach combines technical expertise with business acumen, enabling organizations to leverage AI effectively for sustainable growth and innovation.
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