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AWS Certified Solutions Architect Associate (SAA-C03) (2026)

Kayla Morgan

3(341 students)

Self-paced

Advanced

About this course

Build a strong understanding of cloud architecture and learn how modern organizations design scalable, secure, and highly available systems using Amazon Web Services. This course is designed to help learners develop practical knowledge of cloud computing concepts while exploring the powerful ecosystem of AWS services used by professionals worldwide. You will begin by understanding the foundations of cloud computing, including deployment models, service models, and the advantages of moving from traditional infrastructure to cloud-based environments.

The course then explores AWS global infrastructure, helping you understand how regions, availability zones, and distributed systems enable reliability and fault tolerance at scale. Security and access management are essential in any cloud environment. You will learn how to manage identities, permissions, and authentication mechanisms to ensure secure access to resources.

Networking concepts are also covered in depth, including virtual private clouds, load balancing, DNS management, and content delivery strategies that support high-performance applications. The learning journey continues with cloud storage solutions and data management strategies. You will explore different storage options and understand how to select the right service depending on performance, scalability, and cost requirements.

Compute services are also examined to demonstrate how applications can run efficiently using virtual machines, containers, and serverless technologies. Database services and application integration tools are introduced to help you design modern, data-driven cloud systems. You will also gain insight into monitoring, logging, governance, and infrastructure management practices that help maintain operational efficiency in complex environments.

Finally, the course covers architectural best practices used to design resilient, high-performing, and cost-optimized solutions. By the end of this learning experience, you will have a clear understanding of how to design and manage cloud architectures that align with real-world business requirements while following industry best practices.

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Level: Advanced

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Duration: Self-paced

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Instructor: Kayla Morgan

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This course contains the use of artificial intelligence.AI in Action: Automate Workflows Using Google Apps Script, n8n & Makedotcom is a comprehensive, hands-on course designed to equip you with the skills to streamline repetitive tasks and build intelligent, automated workflows that save time and increase productivity. Throughout the course, you will learn how to harness the power of Google Apps Script to automate tasks within Google Workspace, such as sending emails, managing spreadsheets, and scheduling calendar events. You will also master n8n and Makedotcom, two powerful no-code automation platforms, to design workflows that connect multiple applications and services without writing extensive code.In addition to building traditional automations, the course emphasizes integrating AI capabilities into your workflows, enabling systems to make smarter decisions, process data efficiently, and adapt to changing conditions. By combining scripting, no-code automation, and AI tools, you will gain the ability to create end-to-end solutions that reduce manual work, eliminate errors, and improve efficiency across personal projects or business operations.A key focus of the course is integrating machine learning and AI capabilities into your automations. You will learn how to train simple predictive models, use pre-trained AI APIs, and apply data-driven decision-making within workflows. This allows your automations to not only execute tasks but also analyze data, detect patterns, make predictions, and optimize processes. By combining scripting, no-code automation, and machine learning, you will gain the ability to create end-to-end intelligent solutions that reduce manual work, eliminate errors, and improve efficiency across personal projects or business operations.Whether you are a beginner looking to start your automation journey or a professional seeking to implement AI-powered solutions in your organization, this course provides practical, real-world examples, exercises, and projects. By the end, you will be able to confidently design, implement, and manage intelligent automation workflows that drive productivity and innovation in any environment.

CISCO ENARSI 300-410 — 1500 Certified Exam Questions

Grow and Succed Academy

In modern global networks, where large companies, cloud systems, banks, and service providers depend on constant connectivity, routing systems are what keep everything working without interruption. Cisco enterprise routing technologies provide the foundation for reliable communication, secure data transfer, and stable network performance across complex infrastructures.Today’s networks are highly dynamic and distributed. They connect multiple locations, cloud platforms, and remote users, and they must handle constant changes in traffic, failures, and security requirements. Network engineers are expected to understand how routing behaves, how problems occur, and how to restore stability quickly and correctly.This course is designed as a certification-focused ENARSI practice test system that helps you build real operational skills through scenario-based questions. Instead of memorizing theory, you will train your ability to think like a network engineer working in real enterprise environments.The course includes 1,500 practice questions, organized into 6 sections with 250 questions each, and includes unlimited retakes so you can practice repeatedly, track progress, and improve weak areas over time.This certification covers the following areas:In the first section, you will focus on advanced routing inside enterprise networks, including OSPF and EIGRP neighbor relationships, convergence behavior, metric calculation, route optimization, and troubleshooting routing issues.In the second section, you will work with routing between different organizations and networks, focusing on BGP path selection, route control, policy rules, traffic control, and understanding how external routing decisions are made.In the third section, you will study route redistribution and routing control, learning how different routing protocols share information, how routing policies affect traffic flow, and how to avoid routing problems in mixed environments.In the fourth section, you will explore secure network connections, including VPN technologies such as DMVPN, GRE, IPsec, and FlexVPN, with focus on tunnel setup, encryption, reliability, and troubleshooting.In the fifth section, you will cover core network services, including NAT, DHCP, NTP, SNMP, QoS, redundancy systems, and other services that keep enterprise networks stable and efficient.In the sixth section, you will focus on advanced troubleshooting, analyzing routing failures, VPN issues, service problems, performance degradation, and applying structured methods to identify and fix network issues.Every question is designed with realistic scenarios, multiple answer choices, correct solutions, and clear explanations that help you understand how real enterprise networks behave in production.By completing this course, you will be prepared for Cisco ENARSI certification and also gain practical skills in routing, VPN technologies, network services, and troubleshooting used in real enterprise environments.

Complete 5+ Deep Learning Projects: AI & ML Hands-On Project

ARUNNACHALAM SHANMUGARAAJAN

Hands-on Deep Learning Project Series | Build 5+ Real Deep Learning Projects from Scratch | Complete Deep Learning Project CourseCourse Description:Welcome to the Deep Learning Project course – your ultimate hands-on guide to mastering real-world AI and machine learning through 5+ complete Deep Learning Projects.In this course, you will work on multiple Deep Learning Projects covering diverse applications such as image classification, object detection, face recognition, emotion detection, and more. Whether you're a beginner or an intermediate learner, this course is designed to help you practically understand how to implement each Deep Learning Project from scratch.Every Deep Learning Project is built step-by-step using modern libraries like TensorFlow, Keras, and PyTorch. You will learn how to preprocess data, build neural networks, train models, evaluate results, and deploy each Deep Learning Project in a real-world context.What You Will Learn:Introduction to Facial Recognition and Emotion Detection:Understand the significance of facial recognition and emotion detection in computer vision applications and their real-world use cases.Setting Up the Project Environment:Learn how to set up the project environment, including the installation of necessary tools and libraries for implementing YOLOv7 for facial recognition and emotion detection.Data Collection and Preprocessing:Explore the process of collecting and preprocessing datasets for both facial recognition and emotion detection, ensuring the data is optimized for training a YOLOv7 model.Annotation of Facial Images and Emotion Labels:Dive into the annotation process, marking facial features on images for recognition and labeling emotions for detection. Train YOLOv7 models for accurate and robust performance.Integration with Roboflow:Understand how to integrate Roboflow into the project workflow, leveraging its features for efficient dataset management, augmentation, and optimization for both facial recognition and emotion detection.Training YOLOv7 Models:Explore the end-to-end training workflow of YOLOv7 using the annotated and preprocessed datasets, adjusting parameters, and monitoring model performance for both applications.Model Evaluation and Fine-Tuning:Learn techniques for evaluating the trained models, fine-tuning parameters for optimal performance, and ensuring robust facial recognition and emotion detection.Deployment of the Models:Understand how to deploy the trained YOLOv7 models for real-world applications, making them ready for integration into diverse scenarios such as security systems or human-computer interaction.Ethical Considerations in Computer Vision:Engage in discussions about ethical considerations in computer vision, focusing on privacy, consent, and responsible use of biometric data in facial recognition and emotion detection.By the end of this course, you’ll have a strong portfolio of Deep Learning Projects that showcase your AI and ML skills to employers or clients. Enroll now & build real-world AI applications with Deep Learning!

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AWS Certified Solutions Architect Associate (SAA-C03) (2026)

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AWS Certified Solutions Architect Associate (SAA-C03) (2026)

About this course

Skills you'll gain

Available Coupons

Course Information

This course includes:

Share this course

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