All courses
Josh StarmerDeepLearning.AI
Dive deep into the attention mechanism that revolutionized AI with transformers. This course, taught by Josh Starmer, covers the evolution of attention, the role of Query, Key, and Value matrices, and the differences between self-attention and masked self-attention. Gain hands-on experience coding these concepts in PyTorch, and understand how they contribute to building scalable AI applications.
Amit SanganiDeepLearning.AI
In this course, you will explore the Llama 4 models, learning how to utilize their Mixture-of-Experts architecture for efficient application development. You'll gain practical experience with image reasoning, image grounding, and long-context text processing. The course also covers prompt optimization and synthetic data creation, equipping you with the skills to build production-ready features that span text, images, and massive context. By the end, you'll be able to confidently choose and implement the right Llama 4 model for your projects.
Bernease HermanDeepLearning.AI
In this course, you will explore new metrics and best practices to monitor your LLM systems and ensure safety and quality. You will learn to identify hallucinations, detect jailbreaks, and identify data leakage. Additionally, you will build your own monitoring system to evaluate app safety and security over time. By the end of the course, you will be able to identify common security concerns in LLM-based applications and customize your safety and security evaluation tools.
Josh StarmerDeepLearning.AI
In this course, you will delve into the attention mechanism, a key component of transformers, and learn how to implement it using PyTorch. You'll explore the relationships between word embeddings, positional embeddings, and attention, and understand the roles of Query, Key, and Value matrices. The course covers self-attention, masked self-attention, and cross-attention, providing a comprehensive understanding of how these concepts are incorporated into transformers. By the end, you'll be equipped with the knowledge to build reliable and scalable AI applications.
Dr. Marily NikaMaven
In this course, you will learn how to run teams that integrate AI into your product strategy, work with engineering and ML Ops teams, and navigate the ethical and regulatory issues surrounding AI. The course provides hands-on experience through case studies and practical exercises, simulating real-world scenarios. You will also gain insights from guest speakers who are industry leaders in AI.
Ofer MendelevitchDeepLearning.AI
This course offers a comprehensive exploration of embedding models, focusing on their architecture and implementation. Participants will learn about word and sentence embeddings, dual encoder models, and the use of contrastive loss to enhance question-answer retrieval applications. The course provides a hands-on approach to understanding the technical concepts behind embedding models, making it ideal for data scientists, machine learning engineers, and NLP enthusiasts.
Heidi CulbertsonMaven
In this course, you will learn how to design world-class bots by understanding conversation across the lifespan. You will explore how human communication evolves as we grow up and how to apply this knowledge to create usable conversational products. The course covers developing use cases, applying linguistics and cognition knowledge, training bots, crafting responses, prototyping, and usability testing. By the end of the course, you will have actionable knowledge, frameworks, and techniques to design better bots.
João MouraDeepLearning.AI
In this course, you will learn key principles of designing effective AI agents and organizing a team of AI agents to perform complex, multi-step tasks. You will apply these concepts to automate six common business processes. By the end of the course, you will have designed several multi-agent systems to assist you in common business processes and studied the key principles of AI agent systems.
Isa FulfordDeepLearning.AI
In Building Systems with the ChatGPT API, you will learn how to automate complex workflows using chain calls to a large language model. You will build chains of prompts that interact with the completions of prior prompts, systems where Python code interacts with both completions and new prompts, and a customer service chatbot using all the techniques from this course. This course is beginner-friendly and includes hands-on examples with built-in Jupyter notebooks for seamless experimentation.
Miguel AmigotedX
In this course, you'll learn how to use ChatGPT for content creation, grading, and more. You'll discover how to create personalized learning experiences for students and automate grading and feedback. We'll also cover ethical and legal considerations for using ChatGPT in education, and explore advanced techniques for customization and integration with other educational tools. By the end of the course, you'll be able to unlock the limitless potential of ChatGPT in education, and significantly improve your efficiency and access to information. Join us in this exciting journey and explore how AI and language models can transform the way we teach and learn.
Sean BarnesDeepLearning.AI
This comprehensive program equips you with the skills to manage the entire data lifecycle, from defining problems to delivering insights. You'll learn foundational analysis and visualization, statistical techniques, Python for automation, and data extraction from various sources. The course also covers AI integration in analytics, preparing you for the growing demand in data-centric roles.
Nicolas BoucherMaven
In this course, you will learn how to give advanced instructions to Generative AI, use it for financial analysis, solve confidential data issues, and build your own GPT for finance. You will also learn to use Python for financial analysis without needing to code. The course includes live sessions, self-paced videos, and a comprehensive set of resources to help you master these skills.
Jacob LeeDeepLearning.AI
This course will show web developers how to expand their toolkits with LangChain.js, a popular JavaScript framework for building with LLMs. It covers useful concepts for creating powerful, context-aware applications. By taking this course, you will learn to use LangChain’s underlying abstractions to build your own JavaScript apps, understand the basics of retrieval augmented generation (RAG), and have the structure of a basic conversational retrieval system that you can use for building your own chatbots.
Amit SanganiDeepLearning.AI
Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of AI Partner Engineering at Meta, about the latest additions to the Llama models 3.1 and 3.2. Discover the new vision capabilities, tool-calling, and Llama Stack, an open-source orchestration layer for building on top of the Llama family of models. Start building exciting applications on Llama!
Sebastian WitalecDeepLearning.AI
In this course, you will gain the knowledge to make informed decisions about when to apply vector databases to your applications. You will explore how to use vector databases and LLMs to gain deeper insights into your data, build labs that show how to form embeddings, and use several search techniques to find similar embeddings. Additionally, you will explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.
Anshul RamachandranDeepLearning.AI
In this course, you'll explore the evolution of AI coding assistants and learn how to use Windsurf to code and build several applications. You'll gain insights into the challenges of AI coding agents, such as code search and discovery, and learn to manage unexpected AI behavior. By the end, you'll understand agentic coding in-depth and be able to use it to make your development process more efficient.
