Data

Andrew NgDeepLearning.AI

In this Machine Learning in Production course, you will build intuition about designing a production ML system end-to-end: project scoping, data needs, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production like establishing a model baseline, addressing concept drift, and performing error analysis. You’ll follow a framework for developing, deploying, and continuously improving a productionized ML application.

Carey PhelpsDeepLearning.AI

In this course, you will learn to instrument a Jupyter notebook, manage hyperparameter configurations, log run metrics, collect artifacts for dataset and model versioning, and log experiment results. Additionally, you will learn to trace prompts and responses to LLMs over time in complex interactions. By the end of the course, you will have a systematic workflow to boost your productivity and accelerate your journey toward breakthrough results.

Sebastian WitalecDeepLearning.AI

This course provides a comprehensive understanding of building multimodal search and RAG systems, focusing on the integration of various data types like text, images, and video into AI models. You will learn about contrastive learning for training multimodal models, visual instruction tuning, and the implementation of multi-vector recommender systems that enhance the capabilities of AI applications in industry settings.

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.

Pranav RajpurkarDeepLearning.AI

In this Specialization, you will learn to diagnose diseases from x-rays and 3D MRI brain images, predict patient survival rates using tree-based models, estimate treatment effects using data from randomized trials, and automate the labeling of medical datasets using natural language processing. The course covers model interpretation, image segmentation, natural language extraction, and more.

Amit RawalMaven

By the end of this course, you'll be adept at using AI to transform complex data into compelling stories that drive action and impact. You'll learn how AI can augment your analytical capabilities, automate tedious tasks, and provide deeper insights. The course offers step-by-step guidance and over 50 prompts to help you craft impactful narratives using data.

Emmanuel AcheampongMaven

In this bootcamp, you will go beyond fundamental AI knowledge and apply hands-on technical skills to real-world data problems. You will develop three distinct applications that showcase your diverse skillset in Data Analytics, Computer Vision, and Natural Language Processing. The course includes interactive live sessions, lifetime access to course materials, guided feedback, and a private community of peers.

Luis SerranoDeepLearning.AI

In this specialization, you will gain a deep understanding of the mathematical principles that underpin machine learning algorithms. You will learn how to apply statistical techniques to enhance your data analysis, and acquire skills that are highly sought after by employers. By the end of the course, you will be able to implement and optimize machine learning algorithms, making you a valuable asset in the field of AI and data science.