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Generative AI Foundational Certificate Course

School of AI

4(5.3K students)

Self-paced

All Levels

About this course

The Generative AI Foundational Certificate Course is your entry point into the world of Generative AI—one of the most transformative technologies of our time. Designed for beginners and professionals alike, this course provides a practical, non-technical introduction to the tools, techniques, and use cases shaping the future of work. You’ll start by learning the fundamentals of generative AI, including how tools like ChatGPT, DALL·E, and Midjourney turn simple text prompts into powerful content.

Through hands-on examples, you’ll master the basics of prompt engineering—a critical skill that helps you generate better outputs in writing, design, research, and coding tasks. Explore how AI tools for writing, image generation, voice synthesis, and code generation are being used across industries like marketing, education, healthcare, and law. You’ll gain insights into how professionals are leveraging AI to boost productivity, accelerate creativity, and reduce repetitive work.

Beyond usage, the course also covers the ethical dimensions of generative AI—including privacy, misinformation, bias, and intellectual property. You’ll learn how to use AI responsibly and strategically, whether you’re adopting it in your role or leading change in your organization. By the end, you’ll not only understand how generative AI works, but also how to use it confidently in real-world scenarios.

Upon completion, you’ll receive a recognized certificate to showcase your skills and stay competitive in an AI-driven economy. Whether you’re a marketer, teacher, entrepreneur, or simply curious about AI, this course equips you with the foundational knowledge to get started—fast.

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Level: All Levels

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Duration: Self-paced

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Instructor: School of AI

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This course contains the use of artificial intelligence.Modern enterprise software development demands more than generating code from a prompt. Large repositories contain multiple applications, shared packages, service boundaries, legacy components, security requirements, validation workflows, ownership rules, and organizational knowledge that an AI coding assistant must understand before making safe changes.Claude Code for Enterprise Software Development teaches you how to transform Claude Code from a general coding assistant into a structured, repeatable, and governed AI coding harness for real engineering teams. Throughout the course, you will work with an enterprise commerce platform and progressively build the context, controls, workflows, and integrations Claude Code needs to operate effectively inside a complex codebase.You will begin with the fundamentals of Claude Code, repository navigation, prompting, and codebase exploration. You will then learn how to reduce context noise by creating a practical CODEBASE_MAPmd, defining global project rules with CLAUDEmd, and adding layered service-level instruction files. These techniques help Claude understand architecture, ownership, dependencies, commands, boundaries, and expectations before changing code.The course goes beyond prompt engineering. You will design clear validation commands, establish a measurable definition of done, and require Claude to report test, lint, type-check, and build results honestly. You will also configure safer tool usage by protecting secrets, excluding generated files, reviewing terminal commands, and applying least-privilege permissions.Every major concept is reinforced through hands-on implementation. You will install and configure Claude Code, map a multi-service repository, write global and nested instruction files, define validation workflows, create permission rules, automate checks with hooks, build reusable engineering skills, configure code intelligence, develop an internal MCP server, delegate investigation to subagents, and package the completed harness for team adoption. Rather than studying isolated features, you will see how each component works together as part of a production-oriented engineering system designed for consistency, traceability, maintainability, and controlled execution.Next, you will automate engineering workflows using Claude Code hooks, package repeatable procedures as Agent Skills, and improve large-codebase navigation through code intelligence and language-server capabilities. You will connect Claude Code to internal architecture, ownership, and engineering knowledge through the Model Context Protocol (MCP) while applying appropriate security and access controls.For more advanced workflows, you will create specialized subagents for read-only investigation, security review, testing analysis, and structured reporting. You will then package CLAUDEmd files, skills, hooks, MCP integrations, and review practices into reusable Claude Code plugins that can be versioned, tested, distributed, and maintained across teams.The final sections focus on enterprise AI governance, adoption strategy, human review, privacy, data handling, effectiveness metrics, incident tracking, configuration ownership, and organization-wide rollout. In the capstone, you will use the completed harness to implement a refund feature and compare Claude Code’s performance before and after the enterprise controls are introduced.This practical, project-based course is designed for developers, architects, platform engineers, technical leads, and engineering managers who want to use AI-assisted software development responsibly at scale. By the end, you will have a complete blueprint for creating a safer, more accurate, and more productive Claude Code environment for large repositories and enterprise engineering teams.

Master BI, SSIS, SSRS, SSAS & MDX - Zero to Hero

Nasir Arslan

If mastering MSBI is your goal, you've arrived at the definitive tutorial, I Mean it.Do you aspire to conquer MSBI and create a strong foundation in data integration, analysis, and reporting, this tutorial is for you. Engage in a detailed guideline that not only covers MSBI but will also transform you from a beginner to an accomplished professional in no time by this course. This module serves as a compass point for all those who wish to command the inside workings of MSBI, containing special sections about SQL Server Integration Services (SSIS), Analysis Services (SSAS), and Reporting Services (SSRS), to obtain a complete image of business intelligence.Unlock the Power of MSBI: Let’s dive into the heart of this field of study and steer you through many real-world labs, which demonstrate the skills and knowledge you will need to be proficient with. Along the way the exercises explore how to add your touch; from setting an environment to building and deploying BI solutions, every aspect of preparation is addressed appropriately.Master SSIS with Ease: Obtain skillfulness in ETL procedures through SSIS tools. Study to be able to carry out the process of problem-solving with data. Develop data integration processes based on expert advanced functionalities e.g. conditional splits, data transformation, and deploying of packages.Advance with SSAS: Plumb the depths of SSAS in a detailed manner: Learn to build OLAP cubes that are multidimensional and see the inner workings of time series and the subtleties of hierarchical dimensions. Decipher the contingency of KPIs, aggregations, and data quality management, which is instrumental in building up data analytics models and the process of better decision-making.Elevate Reporting with SSRS: From an SSRS novice to an expert SSRS guru who will enrich your reports by including dynamic and interactive components. This course covers all the basic knowledge of creating reports to go further to learn the report matrices and tabulation reports, all this advanced visualization includes the Bar charts, gauges, indicators, and much more.Students will learn how to :SSIS - SQL Server· ETL-Dataflow-Introduction· Conditional Split and Data Conversion· Setting Pipeline Dynamic - Loops – Debugging· Production Server Deployment Types· Different Type of Joins, Sort and Merge· Pivot Unpivot and Aggregate· Schema Design and Database Relationship Diagram· SCD Unicode and Lookup Concepts with Project· Transactional Grouping and Checkpoints Logging· Data Quality Check Using Profiling Component· Instrumentation at Production Server – Debugging· SQL Execute Task - Store Procedure Creation· Data Quality Services and DQS Component· CDC (Change Data Capture)- Enabling CDC - Bulk Load - Incremental Load· Script Component-Data Transformation· Buffer Size Optimization and Component Types· Sequential and Parallel Processing (Multithreading)· Pages Indexes and Reducing Page Splits for improved ETL LoadSSAS – SQL Server· Setting Up Cube from Scratch· Time Intelligence for SSAS and External BI tools Connection· Key Performance Indicator Setup (KPIs) and Dimension Relationships· Partitions and Storage Modes (MOLAP-ROLAP)· Perspectives and Reginal Language Setting· Measure Formula Types - Processing Cube in Production· Aggregations in Cube· Multidimension expressions (MDX) Query Language· Tabular Cube Setup and Difference between Tabular and MultidimensionSSRS – SQL Server· Introduction to Report Building and SSRS· Drilldown Report Building· Deploying Reports in Production· Creating Simple Dashboard in SSRS

Mastering LLM Evaluation: Build Reliable Scalable AI Systems

School of AI

Unlock the power of LLM evaluation and build AI applications that are not only intelligent—but also reliable, efficient, and cost-effective. This comprehensive course teaches you how to evaluate large language model outputs across the entire development lifecycle—from prototype to production. Whether you're an AI engineer, product manager, or ML ops specialist, this program gives you the tools to drive real impact with LLM-driven systems.Modern LLM applications are powerful, but they're also prone to hallucinations, inconsistencies, and unexpected behavior. That’s why evaluation is not a nice-to-have—it's the backbone of any scalable AI product. In this hands-on course, you'll learn how to design, implement, and operationalize robust evaluation frameworks for LLMs. We’ll walk you through common failure modes, annotation strategies, synthetic data generation, and how to create automated evaluation pipelines. You’ll also master error analysis, observability instrumentation, and cost optimization through smart routing and monitoring.What sets this course apart is its focus on practical labs, real-world tools, and enterprise-ready templates. You won’t just learn the theory of evaluation—you’ll build test suites for RAG systems, multi-modal agents, and multi-step LLM pipelines. You’ll explore how to monitor models in production using CI/CD gates, A/B testing, and safety guardrails. You’ll also implement human-in-the-loop (HITL) evaluation and continuous feedback loops that keep your system learning and improving over time.You’ll gain skills in annotation taxonomy, inter-annotator agreement, and how to build collaborative evaluation workflows across teams. We’ll even show you how to tie evaluation metrics back to business KPIs like CSAT, conversion rates, or time-to-resolution—so you can measure not just model performance, but actual ROI.As AI becomes mission-critical in every industry, the ability to run scalable, automated, and cost-efficient LLM evaluations will be your edge. By the end of this course, you’ll be equipped to design high-quality evaluation workflows, troubleshoot LLM failures, and deploy production-grade monitoring systems that align with your company’s risk tolerance, quality thresholds, and cost constraints.This course is perfect for:AI engineers building or maintaining LLM-based systemsProduct managers responsible for AI quality and safetyMLOps and platform teams looking to scale evaluation processesData scientists focused on AI reliability and error analysisJoin now and learn how to build trustable, measurable, and scalable LLM applications—from the inside out.

4.3•4.3K•Self-paced

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Generative AI Foundational Certificate Course

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Generative AI Foundational Certificate Course

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Skills you'll gain

Available Coupons

Course Information

This course includes:

Share this course

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