DeepLearning.AI

Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you and your work with generative AI. Andrew will guide you through how generative AI works and what it can (and can’t) do. It includes hands-on exercises where you'll learn to use generative [...]

JavaScript is the world’s most popular programming language, and now developers can program in JavaScript to build powerful LLM apps. This course will show webdevs how to expand their toolkits with LangChain.js, a popular JavaScript framework for building with LLMs, and will cover useful concepts for creating powerful, context-aware [...]

LangChain: Chat With Your Data delves into two main topics: (1) Retrieval Augmented Generation (RAG), a common LLM application that retrieves contextual documents from an external dataset, and (2) a guide to building a chatbot that responds to queries based on the content of your documents, rather than the [...]

In Course 2: AI and Climate Change, you will begin by learning the basics of anthropogenic climate change — why it is happening, its projected impacts, and how it is already driving extreme weather around the globe. You will then learn how machine learning techniques can lessen climate change’s [...]

In Course 3, AI and Disaster Management, you will begin by learning how natural disasters create both short and long-term impacts on the economy, environment, and community. You will learn how the disaster management cycle can be used to reduce the impacts of a disaster and guidelines for how [...]

The AI and Public Health course is designed to introduce learners to the concept of using artificial intelligence to address social and environmental issues. We'll start by defining AI for Good, and explore various examples of AI for Good projects. You will get hands-on with a real-world concern, air [...]

Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning.

After completing this course, learners will be able to: represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.; apply common vector and matrix algebra operations like dot product, inverse, and determinants; express certain types of matrix operations as linear [...]

After completing this course, learners will be able to: analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients; approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods; visually interpret [...]

In the third course of the Machine Learning Specialization, you will: Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection; Build recommender systems with a collaborative filtering approach and a content-based deep learning method; Build a deep reinforcement learning [...]