Data

Vasudev LalDeepLearning.AI

In this course, you will delve into the fascinating realm of Multimodal RAG, learning how to effectively chat with videos. The course offers a comprehensive understanding of generative AI, supported by practical code examples and video lessons. Designed for intermediate learners, it provides a unique opportunity to expand your AI knowledge and skills.

Doug TurnbullMaven

In this course, you'll learn how to enhance your search engine's relevance using Large Language Models (LLMs). The course covers query and document understanding, deploying LLMs to production, and making unstructured search structured. You'll gain insights into modern search techniques, helping your team achieve better outcomes faster.

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.

Bryan D. Eldridge, M.Ed.Maven

This course will enable life science practitioners with no prior experience in building AI-driven applications to leverage their subject matter expertise and existing capabilities around data and statistical models - in tandem with easily accessible support and resources - to design and execute their first AI project. You will understand how AI has impacted the traditional data lifecycle, master the basics of the AI lifecycle, develop a comprehensive understanding of the contemporary AI landscape, and explain how each major type of AI model is applicable to life sciences.

Manisha AroraMaven

This course offers a dynamic blend of technical expertise and real-world business challenges. Through a series of interactive sessions, discussions, and hands-on projects, you will learn how to scope machine learning projects effectively, lead discussions with stakeholders, navigate the entire data science workflow from data exploration to model deployment, and communicate project insights and business impact to stakeholders.

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.

Sharon ZhouDeepLearning.AI

In this specialization, you will learn to understand GAN components, build basic and advanced GANs using PyTorch, and control your GAN to build conditional GANs. You will compare generative models, use the FID method to assess GAN fidelity and diversity, and learn to detect bias in GANs. Additionally, you will explore GAN applications in data augmentation and privacy preservation, and implement Pix2Pix and CycleGAN for image translation.

Andreas KolleggerDeepLearning.AI

In this course, you will learn the basics of how knowledge graphs store data using nodes and edges, use Neo4j’s query language Cypher to retrieve information, add a vector index to a knowledge graph, and build a knowledge graph of text documents from scratch. You will also explore advanced techniques for connecting multiple knowledge graphs and using complex queries for comprehensive data retrieval. By the end of the course, you will be well-equipped to use knowledge graphs to uncover deeper insights in your data and enhance the performance of LLMs with structured, relevant context.