Learn To Create An Online Multiplayer Game In Unity

This video course will help you get familiar with Jupyter Notebook and all of its features to perform various data science tasks in Python. Jupyter Notebook is a powerful tool for interactive data exploration and visualization and has become the standard tool among data scientists. In the course, we will start from basic data analysis tasks in Jupyter Notebook and work our way up to learn some common scientific Python tools such as pandas, matplotlib, and plotly. We will work with real datasets, such as crime and traffic accidents in New York City, to explore common issues such as data scraping and cleaning. We will create insightful visualizations, showing time-stamped and spatial data.By the end of the course, you will feel confident about approaching a new dataset, cleaning it up, exploring it, and analyzing it in Jupyter Notebook to extract useful information in the form of interactive reports and information-dense data visualizations.This course uses Jupyter 5.4.1, while not the latest version available, it provides relevant and informative content for data science enthusiasts.About the AuthorDražen Lucanin is a developer, data analyst, and the founder of Punk Rock Dev, an indie web development studio. He's been building web applications and doing data analysis in Python, JavaScript, and other technologies professionally since 2009. In the past, Dražen worked as a research assistant and did a PhD in computer science at the Vienna University of Technology. There he studied the energy efficiency of geographically distributed datacenters and worked on optimizing VM scheduling based on real-time electricity prices and weather conditions. He also worked as an external associate at the Ruder Boškovic Institute, researching machine learning methods for forecasting financial crises. During Dražen's scientific work Python, Jupyter Notebook (back then still IPython Notebook), Matplotlib, and Pandas were his best friends over many nights of interactive ma

هذه الدورة مخصصة لتعليمك برمجة تطبيقات الايفون والايباد من الصفر والى الاحتراف لعمل تطبيقات اكثر من رائعة والبدئ بالعمل كمبرمج حر مستقل من المنزل حيث يتم الدفع لك وانت في غرفة منزلك على حاسوبك. اذا كنت تريد ان تغير حياتك للافضل فهنا فرصتك بهذه الدورة التي تتجاوز ال 60 ساعة متواصلة من الشروحات الرائعة لتعليمك اكبر قدر ممكن من المعلومات. البرمجة قد غيرت حياة الكثيرين فكن انت واحدا منهم لاي استفسار لا تتردد في ارساله الى DirectingZoNe@Gmail.com تحياتنا لك فريق عمل دورة برمجة تطبيقات الايفون والايباد

Are your LLM applications running blind in production?You've deployed an AI agent, a RAG pipeline, or an LLM-powered chatbot. But can you answer these questions?How much did that runaway agent loop cost before someone noticed?Why did hallucination rates spike last Tuesday?Which step in your RAG pipeline is returning irrelevant documents? How do you prove to compliance that you're protecting customer PII in LLM conversations? If you can't answer these questions with data, you have a production problem.Traditional APM tools see your LLM as a black box. They measure latency and error rates, but they can't show you token flows, prompt effectiveness, or quality degradation. LLMs are fundamentally different—non-deterministic, multi-step, token-priced, and quality-sensitive. You need LLM-native observability.Introducing Datadog LLM ObservabilityThis course is the definitive guide to Datadog's LLM Observability platform for enterprise teams. If you're already using Datadog for APM, infrastructure, or security, this integrates directly into your existing stack—no new tools to learn, no separate dashboards to monitor. What you'll build: Throughout this course, you'll instrument a production-grade Customer Support AI Agent with:Multi-turn conversation tracingTool integration (order lookup, refund processing) Custom quality evaluations Cost monitoring dashboard PII scrubbing compliance This isn't a toy example—it's the architecture real enterprise teams deploy.

Over the past few years, the way we build software has changed. We went from searching docs and stitching together snippets to collaborating with AI for planning, scaffolding, refactoring, and debugging. That speed is incredible, but without a process, it can also create brittle code and confusing architectures. My goal for this course is to teach you a repeatable AI-assisted workflow for building real projects: how to scope features, write better prompts, provide useful context, review AI output, and ship with confidence. You’ll apply it by building DevStash—a knowledge hub for snippets, prompts, commands, notes, files, images, and links—using Next.js App Router with TypeScript, modern data patterns, and deployment best practices.We'll also cover testing and code review with AI, plus the core SaaS building blocks like auth, database workflows, file storage, and payments. In addition, you'll build DevStash end-to-end with search, organization, and AI features—while keeping the codebase clean, maintainable, and production-ready.What You Will Learn Summarized:A repeatable AI-assisted workflow — from feature planning and context setup to implementation, testing, and deploymentHow to write effective prompts that produce consistent, high-quality code instead of random trial and errorStructure project context files and specifications so AI understands your codebase and follows your standardsBuild custom skills, subagents, and MCP server integrations to automate repetitive development tasksReview, test, and audit AI-generated code so you ship with confidence, not just hopeBuild DevStash end-to-end — a full-stack SaaS app with auth, database workflows, file storage, search, payments, and AI featuresModern full-stack patterns with Next.js App Router, TypeScript, Prisma, Tailwind CSS v4, and server actionsProduction deployment, envi

Merupakan modul pengembangan kemampuan kepemimpinan dasar dan peningkatan keterampilan mengelola bawahan yang dirancang untuk para leader dan calon leader, dalam rangka menjalankan tugas tugas unit organisasi yang dipimpin dengan baik melalui dan bersama sama dengan bawahan. Atau dengan kata lain “How to get things done effectively and efficiently through and with other people”.Trainer:Ahmad MaduTrainer dan Penulis Buku Bukan Asal Cuap

Unlock the power of Artificial Intelligence, Machine Learning, Data Science, and Cloud Computing with this comprehensive hands-on course designed for beginners, students, software developers, data analysts, and IT professionals. Whether you are starting your AI journey or looking to enhance your existing skills, this course provides a step-by-step learning path from the fundamentals to advanced real-world applications.You will begin by learning Python programming for data science and machine learning, followed by data preprocessing, exploratory data analysis (EDA), feature engineering, data visualization, and statistical concepts. As you progress, you will implement popular machine learning algorithms for regression, classification, clustering, recommendation systems, anomaly detection, and dimensionality reduction using industry-standard Python libraries.The course also introduces modern Artificial Intelligence concepts, including deep learning, neural networks, computer vision, and natural language processing (NLP). Through practical coding exercises and real-world projects, you will gain the confidence to build intelligent applications that solve business problems.In addition to AI and machine learning, you will learn how to leverage Amazon Web Services (AWS) to build scalable, cloud-based AI solutions. You'll understand cloud fundamentals, data storage, model deployment, scalable inference, monitoring, and best practices for deploying machine learning models in production environments.Throughout the course, you will work on multiple hands-on projects that reinforce every concept and help you build a professional portfolio. By the end of the course, you will have the practical skills needed to analyze data, develop machine learning models, build AI-powered applications, and deploy them on AWS using industry best practices.Whether your goal is to become a Data Scientist, Machine Learning Engineer, AI Engineer, Python Developer, or Cloud AI Professional, this course provides