Data Science Academy

    Generative AI & LLMs Foundations: From Basics to Application

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    Generative AI · Online Course

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    The Generative AI & LLMs Foundations course by Data Science Academy offers a comprehensive pathway to understanding the revolutionary paradigm shift in modern artificial intelligence. Over an intensive eight-week curriculum, participants transition from basic machine learning concepts to complex large language model architectures, including Transformers, attention mechanisms, and prompt engineering parameters. This masterclass bridges the gap between theoretical data science knowledge and practical execution, ensuring learners do not just understand how these complex neural networks function, but can actively deploy them. It is designed for software developers, data analysts, system architects, and tech-forward business professionals who want to lead AI integration projects within their organizations. Students will explore practical model deployment strategies, API integrations with leading platforms like OpenAI and Anthropic, and the subtle nuances of fine-tuning open-source models for bespoke corporate needs. Beyond the technical specifics, the syllabus places a heavy emphasis on AI safety, ethical alignment, governance, and computational efficiency. By demystifying the black box of LLMs, this foundational program empowers you to harness generative tools for automated content curation, intelligent code generation, and complex workflow automation safely, efficiently, and responsibly.

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    Muhammad Usman Mallick, representing the Data Science Academy, is an expert educator and technical practitioner specializing in cutting-edge artificial intelligence, generative models, and advanced automated workflows. With a deep commitment to demystifying complex technologies, Muhammad guides students through the practical aspects of modern AI development. His training programs focus heavily on immediate application, taking learners beyond theoretical machine learning concepts into hands-on implementation of technologies like Claude Code, Model Context Protocol (MCP) systems, OpenAI, and Anthropic APIs. Muhammad's teaching philosophy revolves around project-based execution. He designs his curriculum to ensure that engineers, developers, and tech enthusiasts can build concrete tools,…Show more

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    Program Overview

    Online Course

    Learning format

    Generative AI

    Subcategory

    $19.99

    Price

    Price may change · updated within 1–2 weeks

    English

    Course language

    What You'll Learn

    • Explain the architectural mechanics of Large Language Models (LLMs), including the transformer architecture, self-attention mechanisms, and tokenization processes.
    • Apply advanced prompt engineering techniques such as few-shot prompting, chain-of-thought reasoning, and system role definition to guide model outputs accurately.
    • Implement API integrations using popular open-source and proprietary platforms to inject Generative AI capabilities into web and mobile applications.
    • Understand the core methodologies of fine-tuning pre-trained models on specialized domain datasets while managing computational resource constraints.
    • Mitigate risks associated with LLMs by implementing content moderation, data privacy controls, and addressing bias or hallucination patterns.
    • Design and evaluate retrieval-augmented generation (RAG) pipelines to combine external knowledge databases with generative models for up-to-date query answering.

    Best For

    • Software developers looking to integrate LLM capabilities into existing production applications.
    • Data analysts who want to move beyond traditional modeling into generative AI pipelines.
    • System architects responsible for designing and deploying scalable AI-driven infrastructure.
    • Tech-forward business professionals aiming to lead organizational AI integration strategies.

    Not For

    • Absolute beginners with no prior exposure to basic machine learning concepts or Python programming.
    • Non-technical managers who are only interested in high-level conceptual summaries without practical implementation.
    • Researchers seeking a strictly theoretical or mathematical deep dive into neural network topology.

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