Ryota Kawamura

Mathematics-for-Machine-Learning-and-Data-Science-Specialization. Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

Generative-AI-with-LLMs. In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

LangChain-for-LLM-Application-Development. In LangChain for LLM Application Development, you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework.

How-Diffusion-Models-Work. In How Diffusion Models Work, you will gain a deep familiarity with the diffusion process and the models which carry it out. More than simply pulling in a pre-built model or using an API, this course will teach you to build a diffusion model from scratch.

Building-Systems-with-the-ChatGPT-API. In Building Systems With The ChatGPT API, you will learn how to automate complex workflows using chain calls to a large language model.

Generative-AI-for-Everyone. You’ll get insights into what generative AI can do, its potential, and its limitations. You’ll delve into real-world applications and learn common use cases.

LangChain-Chat-with-Your-Data. Start building practical applications that allow you to interact with data using LangChain and LLMs.

ChatGPT-Prompt-Engineering-for-Developers. In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications.

AI-for-Good-Specialization. Learn AI's role in addressing complex challenges. Build skills combining human and machine intelligence for positive real-world impact using AI

Functions-Tools-and-Agents-with-LangChain. You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called LangChain Expression Language (LCEL) for tasks like tagging, extraction, tool selection, and routing.

Evaluating-and-Debugging-Generative-AI. Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task.

AI-for-Everyone. AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone – especially your non-technical colleagues – to take.

Ryota-Kawamura.