Paper Key : IRJ************581
Author: Sreenadh Payyambally
Date Published: 03 Mar 2025
Abstract
This article outlines a thorough, step-by-step methodology for learning Artificial Intelligence (AI) from scratch. It begins by clarifying objectives and scope, then reviews fundamental AI literature to establish essential concepts and frameworks. A comparative analysis between conventional programming and AI highlights key distinctions in instructions, data usage, adaptability, and decision-making. Next, the article synthesizes critical terminology, explaining how methods like Supervised and Unsupervised Learning, Deep Learning, and Natural Language Processing fit into the broader AI ecosystem. Recognizing challenges such as data security, transparency, and bias, it outlines specialized techniquesprompt engineering, prompt tuning, retrieval-augmented generation, and fine-tuning large language modelsto help students and practitioners address complex tasks efficiently. Additionally, the text explores AI agents, leveraging autonomy and learning capabilities to transform customer service and decision-making in various sectors. Practical best practices and real-world examples guide newcomers in crafting effective prompts, managing computational resources, and aligning AI tools with organizational objectives. Ultimately, readers learn to navigate and implement AI responsibly by considering performance needs, data quality, and ethical constraints. This structured, incremental approach ensures a solid foundation for understanding AIs evolving landscape, positioning learners for future advancements in the field. By following these steps meticulously, learners gain confidence in building AI solutions
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