Awesome AI Agents › Courses and Tutorials
The Machine Learning Interviews repository is a comprehensive guide designed to help candidates prepare for technical interviews in the field of Machine Learning and Artificial Intelligence, particularly targeting roles at major technology companies such as those in the FAANG group. The guide is curated based on the author's personal experience and successful interview processes at top companies including Meta, Google, Amazon, Apple, and Roku. It covers a broad spectrum of interview topics that are commonly encountered in machine learning engineering roles. The repository is structured into several chapters, each focusing on a critical area of interview preparation. These include general coding skills emphasizing algorithms and data structures, machine learning-specific coding challenges, foundational machine learning concepts, and system design tailored to machine learning applications. Additionally, it includes behavioral interview preparation and a new section on Agentic AI Systems, reflecting the latest trends in AI engineering and system design as of 2025. The guide acknowledges the variability in interview formats across companies but highlights commonalities in the core components tested. It is primarily aimed at machine learning engineers and applied scientists, though it also offers value to related roles such as data scientists and research scientists. The repository is actively maintained and encourages community contributions to enhance its content. Supplementary resources are linked, such as a related repository on production-level deep learning, providing deeper insights into deploying deep learning systems in real-world scenarios. Overall, this repository serves as a valuable resource for anyone looking to excel in machine learning technical interviews by offering structured, experience-based preparation materials and up-to-date industry knowledge.
https://github.com/alirezadir/Machine-Learning-Interviews
A comprehensive guide and resource hub for prompt engineering, context engineering, retrieval-augmented generation, and AI agents, offering tutorials, papers, tools, and courses to optimize the use of large language models.
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