Yapay Zekanın Temelleri kursu, katılımcılara yapay zeka (AI) alanına kapsamlı bir giriş sağlamak için tasarlanmıştır. Teorik kavramların ve pratik uygulamaların birleşimi sayesinde katılımcılar, yapay zeka ve makine öğrenimi, doğal dil işleme, bilgisayarlı görme ve robotik dahil olmak üzere çeşitli alt alanlarında sağlam bir temel kazanacaklar. Yapay zekanın endüstride, özellikle Telekomünikasyon ve ilgili sektörlerde kullanımını araştırıyoruz. Kursun sonunda katılımcılar yapay zeka kavramlarını, tekniklerini ve bunların özellikle telekomünikasyon alanındaki gerçek dünyadaki uygulamalarını güçlü bir şekilde kavrayacaklardır. Yapay zeka konusunda daha ileri çalışmalar yürütebilecek veya bilgilerini yapay zekanın önemli bir rol oynadığı çeşitli alanlara uygulayabilecek donanıma sahip olacaklar. Önkoşullar Bu ders için özel bir önkoşul bulunmamaktadır. Ancak programlama kavramlarının temel düzeyde anlaşılması yararlı olacaktır. Ders İçerikleri Yapay Zekaya Giriş Makine Öğrenimi Doğal Dil İşleme (NLP) Bilgisayarla Görme Robotik ve Otonom Sistemler Endüstride Yapay Zeka Etik ve Sorumlu Yapay Zeka

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Yapay Zekanın Temelleri kursu, katılımcılara yapay zeka (AI) alanına kapsamlı bir giriş sağlamak için tasarlanmıştır. Teorik kavramların ve pratik uygulamaların bir kombinasyonu sayesinde katılımcılar, yapay zeka ve makine öğrenimi, doğal dil işleme, bilgisayarlı görme ve robotik dahil olmak üzere çeşitli alt alanlarında sağlam bir temel kazanacaklar. Yapay zekanın endüstride, özellikle Telekomünikasyon ve ilgili sektörlerde kullanımını araştırıyoruz. Kursun sonunda katılımcılar, yapay zeka kavramları, teknikleri ve bunların gerçek dünyadaki uygulamaları (özellikle telekomünikasyon alanında) hakkında güçlü bir kavrayışa sahip olacaklar. Yapay zeka konusunda daha ileri çalışmalar yapmak veya bilgilerini yapay zekanın önemli bir rol oynadığı çeşitli alanlara uygulamak için gerekli donanıma sahip olacaklar. Önkoşullar Bu ders için özel bir önkoşul bulunmamaktadır. Ancak programlama kavramlarının temel düzeyde anlaşılması yararlı olacaktır. Ders İçeriği Yapay Zekaya Giriş Makine Öğrenimi Doğal Dil İşleme (NLP) Bilgisayarla Görme Robotik ve Otonom Sistemler Endüstride Yapay Zeka Etik ve Sorumlu Yapay Zeka

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This course introduces cloud computing and containerisation for telecommunications professionals. It explains the main cloud service and deployment models, the relationship between compute, storage and networking, and the importance of scalability, automation and availability in telecoms cloud environments. Participants explore how containers differ from virtual machines and examine the roles of container images, runtimes and registries. The course introduces LXC/LXD, Docker and Kubernetes, explaining how these technologies support application deployment, isolation and orchestration. The course relates these concepts to the evolution from Virtual Network Functions (VNFs) to Cloud Native Network Functions (CNFs), including the roles of NFV infrastructure and management and orchestration. Guided practical activities introduce container platforms, networking, Docker and a lightweight Kubernetes environment. Course Modules Cloud Computing Concepts for Telecom Engineers Introduction to Containers Introduction to Docker Kubernetes Basics Cloud Native in Telecom Prerequisites Participants should have a basic understanding of telecommunications networks and IP networking. Familiarity with mobile core network functions will be helpful when considering the telecoms examples. No prior experience of cloud platforms, Docker or Kubernetes is required. Basic familiarity with Linux and command-line operations will be beneficial for the practical activities. Who the Course Is For This course is intended for telecoms engineers, core network engineers, technical support and operations specialists, and infrastructure professionals who need an introduction to cloud computing and containerisation. It is particularly relevant to those moving from traditional telecommunications platforms towards virtualised and cloud-native environments, or working alongside teams deploying and supporting containerised network functions. Learning Objectives By the end of the course, participants will be able to: Describe cloud service and deployment models used in telecommunications. Explain core cloud concepts, including compute, storage, networking, scalability and automation. Distinguish containers from virtual machines and explain containerisation benefits. Describe the roles of container images, runtimes and registries. Explain the purpose and architecture of Docker and LXC/LXD. Describe how namespaces and cgroups provide isolation and resource control. Explain Kubernetes architecture and the roles of control plane and worker nodes. Distinguish Virtual Network Functions (VNFs) from Cloud-native Network Functions (CNFs). Describe the roles of NFV infrastructure and MANO in telecoms environments. Apply container deployment, networking and resilience concepts in practical lab scenarios.

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This course explores the principles, architectures and technologies that underpin private cloud networking, with a particular focus on telecommunications environments. It explains how private cloud differs from traditional data centres and public cloud, and how virtual networks provide connectivity, isolation and resilience for cloud-hosted workloads. Participants examine Virtual Private Cloud (VPC) design, including addressing, subnetting, routing, security controls and connectivity between networks. The course then explores the underlying networking technologies, including VXLAN, Geneve and BGP EVPN, alongside IPsec VPNs for secure connectivity. Design considerations and common troubleshooting challenges help connect these technologies to their operational use. Building on these foundations, the course considers the requirements of telecoms workloads across central, regional and edge cloud deployments. Topics include the GSMA NG.126 Cloud Infrastructure Reference Model, network separation, performance, security and resilience. Practical laboratory exercises complement the taught content, helping participants develop their understanding of private cloud networking and its application in telecoms. Course Modules What is Private Cloud? Virtual Private Cloud VPC Networking Technologies and Protocols Private Cloud Networking in Telecom Prerequisites Participants should have a working understanding of IP networking, including IPv4 addressing and subnetting, switching, routing and basic network security. Familiarity with VLANs, NAT and general telecommunications network concepts will be beneficial. An awareness of virtualisation and cloud computing is helpful, but prior experience of designing or operating private cloud networks is not required. Who the Course Is For This course is intended for network engineers, telecoms engineers, cloud and infrastructure engineers, technical architects, and operations or support specialists involved in planning, deploying or maintaining private cloud connectivity. It is particularly relevant to technical professionals moving from traditional IP or telecoms networks into cloud infrastructure roles, and those who need to understand how virtual networking supports telecoms workloads. Learning Objectives By the end of the course, participants will be able to: Explain private cloud principles and how they differ from public cloud and traditional data centres. Describe VPC components for connectivity, routing, isolation and security. Design a VPC using addressing, subnetting, routing and high availability principles. Compare VPC interconnection methods, including peering, transit and shared services. Explain underlay and overlay networking, including VXLAN and Geneve. Describe how BGP EVPN supports VXLAN-based networks. Explain how IPsec and IKEv2 provide secure connectivity. Identify common VPC, overlay and VPN troubleshooting considerations. Describe the GSMA NG.126 Cloud Infrastructure Reference Model. Explain how performance, security and resilience requirements shape telecoms cloud deployments.

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This one-day course explores how zero-touch operations can transform the management of 5G and cloud-native telecommunications networks. It examines the progression from manual processes to automated, intent-driven operations, with a focus on improving efficiency, reducing recovery times and ensuring operational consistency. Participants explore how business and operational requirements are translated into policies and technical actions through orchestration and closed-loop control. Use cases include automated configuration management, service activation, self-healing, scaling and incident handling across RAN, core, transport and cloud environments. The course also considers how AI and analytics support prediction, root cause analysis and proactive assurance. Throughout, emphasis is placed on the governance, verification and safety controls needed to introduce automation responsibly, retain appropriate human oversight and align with existing operational processes. Course Modules Introduction to Zero Touch Operations (ZTO) Intent Driven Operations & Policy Automation Automating Operational Processes Across Network Domains AI Assisted Digital Operations Operational Governance, Safety & Trust Controls Prerequisites Participants should have a working understanding of telecommunications networks and basic familiarity with 5G and cloud computing concepts. Experience of network operations, service assurance, incident management or change management will be beneficial. No prior experience of AI, machine learning or programming is required. Who the Course Is For This course is intended for network operations and service assurance engineers, NOC specialists, technical architects, automation and orchestration professionals, and operational team leaders involved in improving telecommunications service delivery. It is particularly relevant to those responsible for provisioning, configuration, incident resolution, performance optimisation or operational governance in 5G and cloud-native networks. Learning Outcomes Explain the evolution from manual operations to automated and intent-driven network management. Describe the roles of orchestration, assurance, closed-loop control and AI in network operations. Explain how intents are translated into policies and automated actions.Identify automation opportunities for provisioning, scaling, healing and service management. Describe automated remediation workflows, from detection to verification.Explain how automated ticketing and closed-loop processes improve operational efficiency. Describe how AI and analytics support prediction, anomaly detection and root cause analysis.Explain the role of NWDAF and analytics platforms in digital operations. Distinguish between human-led and autonomous operational tasks.Identify key governance, safety and control mechanisms for automation. Explain how zero-touch operations integrate with NOC/SOC and change management processes.

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This one-day course explores how artificial intelligence and machine learning can enhance network automation in 5G and cloud-native telecommunications environments. It explains the progression from configuration automation to orchestration, assurance and autonomy, and distinguishes rule-based automation from AI-assisted approaches. Participants examine practical applications across RAN, core, transport and cloud domains, including load prediction, anomaly detection, path optimisation and automated scaling. The course introduces the machine learning concepts, data requirements and operational constraints that underpin these applications. The course also explores how AI supports closed-loop and intent-based automation, including interaction with orchestration and assurance systems. Participants consider telemetry, model deployment and integration with existing operational systems, alongside the controls needed to ensure reliable and safe automated actions. Course Contents Foundations of Network Automation in 5G/Cloud Environments AI & ML Concepts for Telecom Networks AI-Enhanced Automation Use Cases Across Network Domains Integrating AI into Closed Loop and Intent-Based Automation Data, Telemetry & Model Deployment Architecture Who the Course Is For This course is intended for network engineers, technical architects, network operations and assurance specialists, and professionals involved in network automation, orchestration or operational support systems. It is particularly relevant to those working across RAN, core, transport and cloud domains who need to understand how AI and machine learning can improve network operations and support the progression towards autonomous networks. Learning Objectives By the end of the course, participants will be able to: Explain the evolution from automation to orchestration, assurance and autonomous networks. Differentiate rule-based and AI-assisted automation and their appropriate use cases. Describe supervised, unsupervised and reinforcement learning in telecoms. Explain the machine learning lifecycle from data preparation to deployment and retraining. Identify AI-driven automation opportunities across RAN, core, transport, cloud and enterprise networks. Explain predictive and reactive closed-loop automation. Describe how intent is translated into policies and automated network actions. Identify the telemetry, metrics, logs and traces required for network AI. Compare edge and central cloud AI deployment models. Explain key considerations for AI governance, accuracy and OSS integration.  

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Yapay zekâ (YZ), telekomünikasyon sektöründe devrim yaratıyor ve bağlantılı inovasyonu yeni zirvelere taşıyor. Bu kapsamlı kurs, yapay zekânın telekomünikasyondaki rolünü ele alıyor ve makine öğreniminden doğal dil işleme, bilgisayarlı görme ve robotiğe kadar çeşitli uygulamalarını inceliyor. Katılımcılar, yapay zekânın telekomünikasyon ağlarını daha verimli ve sürdürülebilir hale getirmek için nasıl yeniden şekillendirdiği ve Metaverse, Akıllı Ulaşım ve Otonom Sürüş gibi en son kullanım örneklerini desteklemek için gelişen mimarilere nasıl entegre edildiği konusunda sağlam bir anlayış kazanacaklar. Ayrıca, kurs, AIoT'nin (Nesnelerin Yapay Zekası) güçlü konseptini oluşturan Yapay Zeka ve Nesnelerin İnterneti (IoT) arasındaki sinerjiyi vurgulamaktadır. Katılımcılar, yapay zekanın sektörler genelinde inovasyonu nasıl hızlandırdığını, daha akıllı şehirler, daha verimli üretim süreçleri ve kişiselleştirilmiş sağlık çözümleri sağladığını keşfedecekler. Programın sonunda katılımcılar, yapay zeka kavram ve tekniklerine hakim olacak ve bilgilerini telekomünikasyon ve ötesinde uygulamaya hazır olacaklar. İster yapay zeka alanında ileri çalışmalar yapmak, ister farklı alanlardaki uzmanlıklarından yararlanmak isteyin, katılımcılar yapay zeka ve telekomünikasyon inovasyonunun heyecan verici kesişiminde yol almak için donanımlı olacaklar. Hızla gelişen bir dijital ortamda, telekomünikasyon sektöründeki profesyoneller için yapay zekanın temellerini anlamak hayati önem taşımaktadır. Bu kurs, katılımcılara yapay zeka konusunda pratik beceriler kazandırmakla kalmaz, aynı zamanda etik hususlar ve yapay zeka teknolojilerinin sorumlu bir şekilde uygulanması konusunda derin bir anlayış kazandırır. Gerçek dünya uygulamalarına ve sektörle ilgili konulara odaklanan bu kurs, katılımcılara yapay zekayı kullanarak inovasyonu yönlendirme, verimliliği artırma ve telekomünikasyon ve bağlantılı inovasyonun dinamik dünyasında yeni olanakların kilidini açma olanağı sunar. Ders İçeriği               Yapay Zeka'ya Giriş Makine Öğrenmesi Makine Öğrenimini Yapay Zeka'ya Uygulamak Doğal Dil İşleme (NLP) Bilgisayarlı Görüntü İşleme Robotik ve Otonom Sistemler Endüstride Yapay Zeka AIoT (Nesnelerin Yapay Zekası) Etik ve Sorumlu Yapay Zeka

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