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arXiv - Free AI Research Papers

Cornell University

arXiv - Free AI Research Papers is a comprehensive advanced-level resource offered by Cornell University, focused on building practical skills in artificial intelligence and machine learning. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a comprehensive text-based learning resource — ideal for learners who prefer reading and reference-style learning over videos. The advantage of text-based resources is that you can easily search for specific topics, bookmark important sections, copy code snippets, and revisit concepts quickly without scrubbing through video timelines. Many working professionals prefer this format as it's easier to learn in short bursts during breaks. This resource covers topics essential for success in artificial intelligence and machine learning, including machine learning algorithms, deep learning, NLP, computer vision, and model deployment. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Understand the core concepts and theoretical foundations Apply your knowledge through hands-on exercises and small projects Build the practical skills employers actually screen for Develop the problem-solving approach used by working professionals Duration: Estimated duration: 10 hours of content, designed to be completed in 1-2 weeks at a comfortable pace. This is an advanced resource meant for learners who already have solid fundamentals. You should have at least 6 months of hands-on experience or have completed intermediate-level courses in this area. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into artificial intelligence and machine learning Freelancers wanting to add new services to their portfolio Self-learners passionate about artificial intelligence and machine learning and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as ML Engineer, AI Engineer, Data Scientist, Research Scientist. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 8-15 LPA Mid-level / 2-5 years: Rs 18-35 LPA Senior / 5+ years: Rs 40-80 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include Google, Microsoft, OpenAI, Indian AI startups, research labs. India is the second-largest AI talent pool globally, and the demand far exceeds supply. The Indian AI market is expected to reach $17 billion by 2027. Every major Indian tech company — from Infosys to Reliance to Jio — is investing heavily in AI capabilities. The emergence of generative AI has created entirely new job categories that didn't exist two years ago. ML engineers with LLM experience are commanding Rs 30-60 LPA even at early career stages. Cornell University is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.

Cornell University

arXiv - Free AI Research Papers

Applications of Machine Learning in Plant Science

Relativity and Astrophysics (Copy - 9-2020)

Sharks! Global Biodiversity, Biology, and Conservation

An Introduction to Evidence-Based Undergraduate STEM Teaching

(Summer 2023) An Introduction to Evidence-Based Undergraduate STEM Teaching

(Fall 2022) An Introduction to Evidence-Based Undergraduate STEM Teaching

(Fall 2023) An Introduction to Evidence-Based Undergraduate STEM Teaching

Advancing Learning Through Evidence-Based STEM Teaching

(Spring 2023) Advancing Learning Through Evidence-Based STEM Teaching

CIRTL: Advancing Learning Through Evidence-Based STEM Teaching

Structuring Successful Business Deals

Sharks

A Hands-on Introduction to Engineering Simulations

Obsolete -A Hands-on Introduction to Engineering Simulations

Finite Element Analysis for Static Structural Applications

Industrial Applications of Finite Element Analysis

Industrial Applications of Computational Fluid Dynamics

The Computing Technology Inside Your Smartphone

Wiretaps to Big Data

Reclaiming Broken Places: Introduction to Civic Ecology

The Science and Politics of the GMO

American Capitalism: A History

Introduction to Global Hospitality Management

Networks, Crowds and Markets

The Ethics of Eating

Sandbox

Sandbox2

Teaching & Learning in the Diverse Classroom

Teaching & Learning in the Diverse Classroom, Self-Paced

Teaching & Learning in the Diverse Classroom - Updated