インフラン - ライフタイムキャリアプラットフォーム

A complete summary of approximately 130 ML/AI engineer interview questions and answers learned through approximately 20 interviews

This course is a hands-on curriculum for instructors who are new to AI coding education. You will learn step by step from Python basics to simple AI models and prompt engineering.

In this lecture, we will cover UX principles and practical know-how essential for planning and designing AI products. We will explore user-value-centric AI utilization methods, UX planning and design methods leveraging AI, core UX principles and theories learned through everyday design cases, and UX research and data analysis methods to deliver better experiences.

Through this lecture, you'll be able to learn about the AI graduate school admission process and preparation methods from start to finish! I'll also share some great tips to increase your chances of getting accepted!

<How to Use Chat GPT 200%> is a hands-on content focused on ‘How to ask Chat GPT to get the answer you want?’ This course uses the free version of Chat GPT as the base and, depending on the content, also practices some paid versions, so that learners can practice in an environment that suits their needs. Through this course, learners will learn how to ask good questions to Chat GPT and use various extension programs to increase their work efficiency.

This is a machine learning lecture written like a story in easy-to-understand language without formulas, tailored to the level of liberal arts students!

You will understand the present and future of AI technology, experience the testing process of AI models such as machine learning, and learn the role of a tester. You will comprehensively learn everything from establishing AI-based system test strategies to designing and executing test cases, and even methods for utilizing AI to support testing. This course is conducted based on ISTQB's AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.

An Artificial Intelligence introductory course tailored to the level of planners and PMs! From data to generative AI, core concepts for successful AI planning are easily organized. The goal is practical understanding to the level of being able to ask questions and explain, rather than creating AI.

AI that judges and adapts autonomously – now you can build it yourself. Learn the core of reinforcement learning with intuitive explanations and code, not complex theories, and grow into a 'hands-on developer' through finance and industry projects.

AI Design Secrets to Create High-Quality Work Start Designing Right Away with Midjourney & Photoshop, Accessible to Everyone

**"AI Philosophy - Ethics and Challenges in the Age of Artificial Intelligence"** is a topic that deals with the ethical and philosophical exploration of the development of artificial intelligence (AI) and its impact on human society and culture. This topic goes beyond simple technological development and delves into how AI affects human life and what ethical decisions we should make accordingly.

Deep learning is a technology that learns data through neural networks composed of combinations of complex functions. In this lecture, we will mathematically understand the core concepts of deep learning and analyze them from the perspective of matrix operations. In particular, utilizing Python's NumPy library, we will visually examine how parameters are updated by directly implementing the forward and backward propagation processes of deep learning. Even the seemingly complex neural network structure becomes clear when analyzed with matrix operations. This lecture focuses more on understanding concepts than coding and is suitable for students who wish to intuitively grasp the principles of deep learning mathematically.

We will create an AI judge by combining CrewAI, knowledge graphs, and inference systems, enabling LLM-based multi-agent technology.