Large Language Models (LLMs) are transforming the way organizations interact with data, automate tasks, and deliver personalized experiences. This course unpacks the architecture, training methods, and strategic implementation of LLMs—core skills for anyone looking to thrive in the evolving AI landscape.
Decoding Large Language Models
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Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Explore the architecture and components of modern large language models
Implement and manage LLMs effectively in organizational settings
Master techniques for training, fine-tuning, and deploying LLMs
Skills you'll gain
Details to know
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Recently updated!
November 2025
Assessments
15 assignments
Taught in English
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There are 15 modules in this course
In this section, we explore LLM architecture, focusing on Transformer models, attention mechanisms, and their advantages over RNNs, enhancing understanding of modern language systems.
What's included
2 videos9 readings1 assignment
In this section, we examine how LLMs use probability and statistical analysis for decision-making, focusing on mechanisms, challenges, and practical implications for model reliability and accuracy.
What's included
1 video6 readings1 assignment
In this section, we explore data preparation, training environment setup, and hyperparameter tuning for LLMs, emphasizing balanced datasets and strategies to address overfitting and underfitting.
What's included
1 video6 readings1 assignment
In this section, we explore transfer learning, curriculum learning, and multitasking to enhance LLM performance, focusing on practical applications and real-world adaptability.
What's included
1 video8 readings1 assignment
In this section, we explore techniques like LoRA and PEFT to enhance LLM adaptability for NLP tasks, focusing on efficient fine-tuning and precision in model customization for real-world applications.
What's included
1 video8 readings1 assignment
In this section, we explore methods for evaluating LLMs using quantitative metrics, human-in-the-loop protocols, and ethical bias analysis to ensure reliable and responsible model performance.
What's included
1 video7 readings1 assignment
In this section, we explore deploying LLMs in production, focusing on scalability, security, and maintenance to ensure reliable and efficient real-world performance.
What's included
1 video7 readings1 assignment
In this section, we examine strategies for integrating LLMs into existing systems, focusing on compatibility, security, and practical implementation techniques.
What's included
1 video8 readings1 assignment
In this section, we explore quantization, pruning, and knowledge distillation to optimize LLMs for efficiency and performance in real-world applications.
What's included
1 video7 readings1 assignment
In this section, we cover hardware acceleration, data optimization, and cost-performance balance for LLM deployment.
What's included
1 video5 readings1 assignment
In this section, we examine LLM vulnerabilities, bias mitigation strategies, and legal compliance challenges, emphasizing responsible AI deployment and ethical decision-making.
What's included
1 video7 readings1 assignment
In this section, we explore the use of LLMs in customer service, marketing, and operations, highlighting their role in improving efficiency, optimizing strategies, and delivering measurable ROI through automation and data analysis.
What's included
1 video5 readings1 assignment
In this section, we examine the selection and integration of LLM tools, comparing open source and proprietary options, and highlight the role of cloud services in NLP workflows.
What's included
1 video6 readings1 assignment
In this section, we cover GPT-5 readiness, contextual understanding, and strategic planning for future LLM advancements.
What's included
1 video6 readings1 assignment
In this section, we review key insights and explore the future of LLMs and AI learning opportunities.
What's included
1 video3 readings1 assignment
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
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