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If you’re eager to learn about LLMs but don’t know where to start, check out this free 10-week Applied LLMs Mastery course:

Professional Training Course on Large Language Models (LLMs)

🗓️ Week 1: Introduction to LLMs – Principles and Applications

Applied LLM Foundations: Understand the theoretical underpinnings and basic concepts of Large Language Models.
Participants will be able to explain the core mechanisms and functionalities of LLMs.

Real World LLM Use Cases: Explore how LLMs are applied across different industries and problem domains.
Participants will identify potential use cases relevant to their fields of interest or work.

Domain and Task Adaptation Methods: Learn the techniques to adapt LLMs to specific domains and tasks.
Participants will be equipped to customize models to enhance relevance and performance in targeted applications.

🗓️ Week 2: Mastering Prompting and Prompt Engineering

Basic Prompting Principles: Grasp the fundamentals of how prompts are used to guide LLM outputs.
Participants will craft effective prompts that align with desired model responses.

Types of Prompting: Distinguish between different prompting methods and their use cases.
Participants will select and apply appropriate prompting techniques based on specific requirements.

Applications, Risks, and Advanced Prompting: Evaluate the implications of prompting methods and learn advanced strategies for complex interactions.
Participants will implement advanced prompts and mitigate associated risks in LLM applications.

🗓️ Week 3: Techniques for LLM Fine-Tuning

Basics of Fine-Tuning: Introduction to the methods for customizing pre-trained LLMs.
Participants will understand the foundational approaches to model fine-tuning.

Types of Fine-Tuning: Examine various fine-tuning techniques and their applications.
Participants will apply different fine-tuning strategies to optimize LLM performance.

Fine-Tuning Challenges: Address common challenges in fine-tuning and strategies to overcome them.
Participants will troubleshoot and solve typical fine-tuning issues.

🗓️ Week 4: Implementing Retrieval-Augmented Generation

Understanding the Concept of RAG in LLMs: Comprehend how retrieval-augmented generation enhances LLM capabilities.
Participants will explain the integration of retrieval mechanisms in LLM workflows.

Key Components of RAG: Identify the essential elements that constitute a RAG system.
Participants will design and evaluate RAG components.

Advanced RAG Methods: Explore sophisticated techniques for improving and scaling RAG systems.
Participants will implement advanced RAG solutions in LLM projects.

🗓️ Week 5: Tools for Building LLM Applications

Introduction to Fine-Tuning Tools: Discover tools and platforms for effective model fine-tuning.
Participants will utilize specific tools to fine-tune LLMs for tailored applications.

Exploring RAG Tools: Gain proficiency in tools designed for implementing RAG.
Participants will integrate RAG tools into their development workflow.

Comprehensive Overview of LLM Tools: Explore a broad array of tools for observability, prompting, serving, and vector search in LLM applications.
Participants will apply these tools in real-world scenarios to enhance model performance and reliability.

🗓️ Week 6: Evaluation Techniques for LLMs

Understanding LLM Evaluation Types: Differentiate between behavioural and performance evaluations for LLMs.
Participants will choose appropriate evaluation strategies for their model assessments.

Common Evaluation Benchmarks: Familiarize with standard benchmarks used in the evaluation of LLMs.
Participants will employ these benchmarks to assess model quality and fitness for purpose.

Utilization of Common Metrics: Master the use of prevalent metrics in LLM evaluation.
Participants will effectively measure and interpret the performance of their LLMs using industry-standard metrics.

🗓️ Week 7: Building Your Own LLM Application

Overview of LLM Application Components: Understand the architectural elements from basic to advanced that comprise an LLM application.
Participants will construct a blueprint for their LLM application.

End-to-End LLM Application Development: Develop a complete LLM application using freely available resources.
Participants will build and deploy a functional LLM application by integrating diverse LLM components and tools.

🗓️ Week 8: Advanced Features and Deployment of LLMs

Lifecycle Management and LLM Operations (LLMOps): Study the lifecycle of an LLM and the operational practices involved in maintaining and scaling these models.
Participants will implement LLMOps practices to ensure model reliability and scalability.

Advanced Deployment Features for LLMs: Delve into advanced deployment strategies that enhance LLM functionality and user experience.
Participants will deploy LLMs with features that support robust, scalable, and secure applications.

🗓️ Week 9: Addressing Challenges with LLMs

Scaling Challenges: Tackle the technical and strategic challenges in scaling LLMs.
Participants will devise solutions to enhance model scalability and manage resource consumption.

Behavioural Challenges: Address specific behavioural issues like hallucinations, alignment discrepancies, and susceptibility to adversarial attacks.
Participants will implement strategies to mitigate these issues and improve model integrity.

Deployment Challenges: Overcome obstacles related to memory demands, scalability, and security in LLM deployment.
Participants will ensure secure, efficient, and scalable deployment of LLM applications.

🗓️ Week 10: Emerging Research Trends in LLMs

Exploring Multimodal and Domain-Specific LLMs: Investigate the integration of multiple data types and domain-specific training in LLMs.
Participants will design models that leverage diverse inputs and are tailored to specific domains.

Open Source Models and New Architectures: Gain insights into the latest open-source models and innovative architectures like Mixture of Experts (MoE) and Mamba.
Participants will evaluate and potentially integrate cutting-edge technologies into their LLM projects.

• 🗓️ Week 11 Bonus: Reinforcing LLM Foundations
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Generative Models Foundations: Solidify understanding of the principles behind generative models.
Participants will distinguish between various types of generative models and their applications.

Neural Networks for Language: Deepen knowledge of neural network architectures used in LLMs, such as RNNs, LSTMs, and Sequence-to-Sequence models.
Participants will apply these architectures in designing effective LLMs.

Self-Attention and Transformers: Explore the mechanism of self-attention and its pivotal role in transformers, the backbone of modern LLMs.

• Participants will implement transformer-based models to enhance their LLM applications.