Data

Richmond AlakeDeepLearning.AI

In this course, you will learn how to use pre-filtering, post-filtering, and projection techniques to optimize query processing and output. You will also explore prompt compression techniques to handle long and expensive prompts in large-scale applications. Additionally, you will learn to combine vector search capabilities with traditional database operations to build efficient and cost-effective RAG applications. This course is ideal for Python developers, database enthusiasts, and AI practitioners looking to enhance their skills in these areas.

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.

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.

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.

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.

Adrian Gonzalez SanchezDeepLearning.AI

In Building Your Own Database Agent, you will learn to interact with tabular data and SQL databases using natural language, enabling more efficient and accessible data analysis. You will gain hands-on experience with the Azure OpenAI Service, implementing techniques like Retrieval Augmented Generation (RAG) and function calling. By the end of the course, you’ll be equipped with the technical knowledge and practical experience to implement similar systems in your own projects or organizations, enabling more efficient and accessible data interaction and analysis.

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.

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.