How to Write and Execute Subqueries in SQL Server

Are you ready to unlock the power of data and propel your career to new heights? In today's data-driven world, understanding cloud data platforms is no longer a luxury – it's a necessity. This comprehensive course, "Mastering Cloud Data Platform Fundamentals for Beginners", will equip you with the essential knowledge and skills to thrive in this exciting field, even if you're starting from scratch!Forget complex jargon and confusing theories. This course is designed to be practical, engaging, and immediately applicable. Through practical advice and real-world examples, you'll dive deep into the basic core concepts of cloud data platforms, mastering the foundations of data wrangling, pipeline development, and leveraging modern applications to transform raw data into actionable insights.What You'll Learn:Cloud Data Platform Essentials: Demystify the fundamental building blocks of cloud data platforms and understand their role in today's data landscape.Data Wrangling Mastery: Learn the basics of cleaning, transforming, and preparing data for analysis using industry-standard techniques. Say goodbye to messy data and hello to reliable insights!Pipeline Power: Discover how designing, building, and managing efficient data pipelines work so that you can start to automate the flow of data, saving you time and effort.Modern Applications: Get informed about the latest tools and technologies used in conjunction with cloud data platforms, and understand how they're revolutionizing industries.Real-World Success: Solidify your understanding and prepare yourself for real-world challenges.Why Take This Course?Beginner-Friendly: No prior experience with cloud data platforms? No problem! This course starts with the basics and gradually builds your k

BigML is a leading cloud-based, no-code machine learning platform designed for ease, scalability, and automation. Whether you're a data analyst, business intelligence professional, or product owner with little to no coding experience, this course will help you understand and apply machine learning with BigML’s intuitive GUI, REST APIs, and automation tools like AutoML, WhizzML, Deepnets, and OptiML.We’ve crafted this course around 12 key modules that reflect both the technical depth and interview-oriented focus you need to demonstrate confidence in BigML-based solutions. With 350+ concept-based and scenario-based Q&A, you will develop the readiness to handle practical, business-driven machine learning problems using BigML.Course Syllabus (Structured with Modules)1. Introduction to BigMLUnderstand the BigML ecosystem, no-code ML capabilities, and key use cases like customer segmentation, demand forecasting, and fraud detection.Compare BigML with other platforms like AWS SageMaker and Azure ML.2. BigML ArchitectureLearn the core elements: data sources, datasets, models, evaluations, predictions.Master the BigML workflow—from data ingestion to real-time predictions and WhizzML automation.Explore deployment models (cloud vs. on-premise).3. Data PreparationUpload and transform data using BigML.Perform feature engineering, handle missing values, and apply built-in preprocessing steps.Use interactive visualizations to explore and understand data.4. Model CreationBuild supervised models (classification and regression).Apply unsupervised models like clustering and anomaly detection.Learn time-series forecasting for trend analysis and ARIMA predictions.5. Feature Engineering and SelectionEvaluate feature importance.<

This course contains the use of Artificial Intelligence." Unofficial Course "In today’s fast-paced software industry, building reliable, maintainable, and scalable software requires more than simply writing code—it requires adopting disciplined engineering practices that prioritize quality from the beginning. This course is designed to provide a comprehensive understanding of Test-Driven Development (TDD) as both a technical methodology and a software engineering mindset that transforms the way modern systems are designed, developed, and maintained.Throughout this course, you will explore the historical evolution of software quality assurance and understand why Test-Driven Development has become a central practice in modern software engineering environments. You will learn the philosophical foundations behind TDD, examine the Red-Green-Refactor cycle in depth, and understand how test-first thinking changes the architecture and maintainability of software systems.The course goes beyond basic testing concepts by exploring what makes tests effective, maintainable, and valuable over time. You will study the structural principles behind testable code, learn how dependency management influences software design, and understand how concepts such as test doubles, mocks, stubs, and fakes contribute to building isolated and reliable test environments. Additionally, you will explore how TDD aligns with widely accepted design principles such as SOLID and supports cleaner, more modular architectures.You will also gain a deep understanding of the relationship between Test-Driven Development and Behavior-Driven Development (BDD). The course explains how business requirements can be translated into technical specifications, how shared communication models improve collaboration across teams, and how modern development organizations bridge the gap between technical implementation and stakeholder expectations.Advanced top

AI agents are how software gets built next. This course teaches you to build them — from scratch, in Python, using the Claude Agent SDK.The Claude Agent SDK is the same technology that powers Claude Code — Anthropic's AI coding assistant used by hundreds of thousands of developers worldwide. This course gives you programmatic access to that same engine, so you can build autonomous agents that read files, run commands, search the web, ask users questions, and complete complex multi-step tasks — without writing a single line of tool execution logic yourself.What You Will BuildBy the end of this course you will have built three complete agents from scratch:File Explorer Agent — autonomously explores a directory, reads every file, and produces a clean summary report. Your first complete autonomous agent.Codebase TODO Summarizer Agent — searches an entire codebase for TODO comments, filters results by priority based on your preferences, and produces a structured report. Uses Glob, Grep, Read, and AskUserQuestion working together.Safe Code Review Agent — the course capstone. Reviews code for quality issues and security vulnerabilities — hardcoded credentials, SQL injection, poor error handling — without being able to modify a single file. Three layers of production safety built in.What You Will LearnSection 1 — The Big Picture Understand exactly how the Claude Agent SDK works under the hood before writing a single line of code. The four components, the agent loop, pipe communication between the SDK and Claude Code, and session management. This mental model makes everything else in the course click.Section 2 — Your First Agent Everything runs in Google Colab — no local setup required. Write your first query() call, master async/await in Python, read and filter the agent message stream, and build your firs

FastAPI is one of the fastest-growing Python frameworks for building modern, high-performance APIs. If you want to create production-ready REST APIs using Python with clean code and automatic documentation, this course is for you.In this beginner-friendly course, you’ll learn FastAPI from scratch, even if you have never built an API before. We’ll start with the fundamentals and gradually move toward building real-world, scalable APIs.What you’ll learnUnderstand what FastAPI is and why it’s faster than traditional frameworksSet up FastAPI with Python and virtual environmentsCreate REST APIs using GET, POST, PUT, and DELETEUse Pydantic models for request validation and response schemasWork with path parameters, query parameters, and request bodiesImplement CRUD operationsBuild asynchronous APIs using async and awaitAdd authentication and authorizationHandle errors and validations properlyAuto-generate API documentation using Swagger UI & ReDocStructure FastAPI projects using best practicesBuild a real-world API project step by stepWho this course is forBeginners who want to learn API development with PythonPython developers looking to upgrade to FastAPIBackend developers exploring modern frameworksStudents preparing for backend or full-stack rolesRequirementsBasic understanding of PythonNo prior API or FastAPI experience requiredBy the end of this course, you’ll have the confidence to build, test, and deploy modern APIs using FastAPI

This course explores the rapid shift from traditional software-driven work to an AI-native world where intelligent systems actively perform tasks. It begins by reframing AI from a simple tool into a layer of “digital labor,” introducing the transition from a token-based usage model to a reasoning-driven economy where value depends on outcomes, not prompts.You will learn how modern AI systems are built, including when to use Small Language Models (SLMs) for cost-efficient, domain-specific applications. The course also covers the evolution of retrieval systems, moving from basic vector search to structured, context-aware approaches like knowledge graphs (RAG 3.0), enabling more accurate and reliable outputs.A key focus is solving real-world challenges such as hallucinations and system reliability. You will learn how to design verification layers, validation pipelines, and multi-step reasoning systems to ensure trustworthy AI outputs. Security is also addressed through practical insights into adversarial threats like prompt injection and how to defend against them.Beyond technology, the course examines how AI is reshaping business models. You will explore the rise of Labor-as-a-Service (LaaS), where AI agents act as scalable digital workers across functions. It also introduces hyper-personalized synthetic media and the concept of digital twins, allowing individuals and organizations to extend their presence and capabilities.The course further discusses evolving ideas around copyright, contribution, and ownership in the AI era. It highlights the shift from human-in-the-loop to human-on-the-loop systems, requiring leaders to manage hybrid teams of humans and AI agents.Finally, it prepares you for changing career dynamics, including the decline of entry-level coding roles and the rise of the “product architect.” By the end, you will understand how to design, deploy, and lead AI-driven systems in a rapidly transforming world.