4763 Model courses delivered Online

We will teach you a model for leading and managing the four stages of change transition. Each stage will have very specific tasks and steps associated to ensure you achieve your desired result. Understand where you are currently and where you want to be in the future. Create a plan for reaching your goal based on what's needed and wanted. Your plan will be tested on this journey and you will need to be ready to respond to any situation. At the end you will need to evaluate the outcome. Learning Objectives Apply the four stages of change transitions for leading change, Implement tasks associated with each stage of managing change Target Audience Managers, Team Leaders, Young Professionals, Sales Professionals, Customer Service Teams

Course Description Get instant knowledge from this bite-sized Blended Learning Course for Teachers Part - 1 course. This course is very short and you can complete it within a very short time. In this Blended Learning Course for Teachers Part - 1 course you will get fundamental ideas of blended learning, the key understanding of working with the school, its policies and so on. Enrol in this course today and start your instant first step towards learning about blended learning. Learn faster for instant implementation. Learning Outcome Familiarise with blended learning Understand problems and opportunities Know how to choose the model Understand working with the school and its policies Learn about the use of technology How Much Do Teachers Earn? Senior - £50,000 (Apprx.) Average - £32,000 (Apprx.) Starting - £22,000 (Apprx.) Requirement Our Blended Learning Course for Teachers Part - 1 is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation. Blended Learning Course for Teachers Part - 1 Module 01: Introduction to Blended Learning 00:19:00 Module 02: Problems and Opportunities 00:18:00 Module 03: Choose the Model 00:28:00 Module 04: Use of Technology - Part I 00:27:00 Module 05: Use of Technology - Part II 00:20:00 Module 06: Working with the School and Its Policies 00:25:00 Assignment Assignment - Blended Learning Course for Teachers Part - 1 00:00:00

Managing Agile and Waterfall Projects: A Hybrid Approach With the growing interest in Agile approaches, how can we take a measured approach? Organizations can't just simply drop everything to become 100% Agile. Not only are hybrid approaches acceptable, they are common in transitioning organizations. We need to understand the strengths and weaknesses of both the traditional and Agile methods to find the best combination that gives us the best of both worlds. This presentation will cover how to combine both approaches into a hybrid model, and help you understand the general criteria of how one approach would be chosen over another.Learning Objectives This presentation will cover how to combine both approaches into a hybrid model, and help you understand the general criteria of how one approach would be chosen over another. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

Duration 3 Days 18 CPD hours This course is intended for System administrators and operators who are operating in the AWS Cloud Informational technology workers who want to increase the system operations knowledge. Overview Identify the AWS services that support the different phases of Operational Excellence, an AWS Well-Architected Framework pillar Manage access to AWS resources using AWS accounts and organizations and AWS Identity and Access Management (IAM) Maintain an inventory of in-use AWS resources by using AWS services, such as AWS Systems Manager, AWS CloudTrail, and AWS Config Develop a resource deployment strategy using metadata tags, Amazon Machine Images (AMIs), and AWS Control Tower to deploy and maintain an AWS cloud environment Automate resource deployment by using AWS services, such as AWS CloudFormation and AWS Service Catalog Use AWS services to manage AWS resources through CloudOps lifecycle processes, such as deployments and patches Configure a highly available cloud environment that uses AWS services, such as Amazon Route 53 and Elastic Load Balancing, to route traffic for optimal latency and performance Configure AWS Auto Scaling and Amazon EC2 Auto Scaling to scale out your cloud environment based on demand Use Amazon CloudWatch and associated features, such as alarms, dashboards, and widgets, to monitor your cloud environment Manage permissions and track activity in your cloud environment by using AWS services, such as AWS CloudTrail and AWS Config Deploy your resources to an Amazon Virtual Private Cloud (Amazon VPC), establish necessary connectivity to your Amazon VPC, and protect your resources from disruptions of service State the purpose, benefits, and appropriate use cases for mountable storage in your AWS Cloud environment Explain the operational characteristics of object storage in the AWS Cloud, including Amazon Simple Storage Service (Amazon S3) and Amazon S3 Glacier Build a comprehensive cost model to help gather, optimize, and predict your cloud costs by using services such as AWS Cost Explorer and the AWS Cost & Usage Report This course teaches systems operators and anyone performing cloud operations functions how to manage and operate automatable and repeatable deployments of networks and systems on AWS. You will learn about cloud operations functions, such as installing, configuring, automating, monitoring, securing, maintaining, and troubleshooting these services, networks, and systems. The course also covers specific AWS features, tools, and best practices related to these functions. Prerequisites Successfully completed the AWS Technical Essentials course Background in either software development or systems administration Proficiency in maintaining operating systems at the command line, such as shell scripting in Linux environments or cmd/PowerShell in Windows Basic knowledge of networking protocols (TCP/IP, HTTP) 1 - Introduction to Cloud Operations on AWS What is Cloud Operations AWS Well-Architected Framework AWS Well-Architected Tool 2 - Access Management AWS Identity and Access Management (IAM) Resources, accounts, and AWS Organizations 3 - System Discovery Methods to interact with AWS services Tools for automating resource discovery Inventory with AWS Systems Manager and AWS Config Hands-On Lab: Auditing AWS Resources with AWS Systems Manager and AWS Config 4 - Deploy and Update Resources Cloud Operations in deployments Tagging strategies Deployment using Amazon Machine Images (AMIs) Deployment using AWS Control Tower 5 - Automate Resource Deployment Deployment using AWS CloudFormation Deployment using AWS Service Catalog Hands-On Lab: Infrastructure as Code 6 - Manage Resources AWS Systems Manager Hands-On Lab: Operations as Code 7 - Configure Highly Available Systems Distributing traffic with Elastic Load Balancing Amazon Route 53 8 - Automate Scaling Scaling with AWS Auto Scaling Scaling with Spot Instances Managing licenses with AWS License Manager 9 - Monitor and Maintain System Health Monitoring and maintaining healthy workloads Monitoring AWS infrastructure Monitoring applications Hands-On Lab: Monitor Applications and Infrastructure 10 - Data Security and System Auditing Maintaining a strong identity and access foundation Implementing detection mechanisms Automating incident remediation 11 - Operate Secure and Resilient Networks Building a secure Amazon Virtual Private Cloud (Amazon VPC) Networking beyond the VPC 12 - Mountable Storage Configuring Amazon Elastic Block Store (Amazon EBS) Sizing Amazon EBS volumes for performance Using Amazon EBS snapshots Using Amazon Data Lifecycle Manager to manage your AWS resources Creating backup and data recovery plans Configuring shared file system storage Hands-On Lab: Automating with AWS Backup for Archiving and Recovery 13 - Object Storage Deploying Amazon Simple Storage Service (Amazon S3) Managing storage lifecycles on Amazon S3 14 - Cost Reporting, Alerts, and Optimization Gaining AWS cost awareness Using control mechanisms for cost management Optimizing your AWS spend and usage Hands-On Lab: Capstone lab for CloudOps Additional course details: Nexus Humans Cloud Operations on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cloud Operations on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Pricing can determine whether your business succeeds or fails – yet too many entrepreneurs rely on guesswork or back-of-the-napkin calculations to set prices. This learning stream reveals a proven methodology for developing a pricing strategy, as well as ways to increase customer perceptions of value so you can justify higher prices and boost margins. As part of the process, you’ll learn how investors evaluate pricing strategies, the advantages and drawbacks of different pricing models, how to calculate margins, average margins for specific industries and retail categories, how to gauge price sensitivity, and how to build a pricing profile specific to your offering based on eight key variables. Tips and examples are included for both product- and service-based businesses, as well as businesses that contend with seasonal pricing variations. The session offers guidance on bundling, discounting, and other pricing techniques, which you can apply in a workshop session that walks through the pricing strategy framework step by step.

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Duration 1 Days 6 CPD hours This course is intended for This course is intended for: A technical audience at an intermediate level Overview Using Amazon SageMaker, this course teaches you how to: Prepare a dataset for training. Train and evaluate a machine learning model. Automatically tune a machine learning model. Prepare a machine learning model for production. Think critically about machine learning model results In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. Day 1 Business problem: Churn prediction Load and display the dataset Assess features and determine which Amazon SageMaker algorithm to use Use Amazon Sagemaker to train, evaluate, and automatically tune the model Deploy the model Assess relative cost of errors Additional course details: Nexus Humans Practical Data Science with Amazon SageMaker training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Practical Data Science with Amazon SageMaker course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

This course is for tax professionals, students of ADIT (sitting the Banking module) and other persons interested in gaining enhanced knowledge of tax issues in the banking and finance sector. Persons on this course will learn about global and regional tax policies and how they relate to investment banking, capital markets, global markets, asset financing, asset management, private banking and wealth management. Learning: Self Paced Mode of assessment: 50 MCQs (80% Pass mark) Award : MOJITAX certificate of knowledge, and ADIT Module 3.05 (exam preparation). Author: MojiTax Start date: NA Duration: Self Paced ADIT/MOJITAX Blended Syllabus The curriculum of the course encompasses the syllabus of the Chartered Institute of Taxation's Advanced Diploma in International Taxation, Module 3.05. Additionally, practical concerns for tax practitioners are also covered. Upon completion of the module, participants are expected to have solid understanding of Banking taxation and confidently pass the ADIT Module 3.05 Banking option in either June or December. Professional Exam Focused At MojiTax, we understand that our students want to be well-prepared for their Advanced Diploma in International Taxation (ADIT) professional exam. That's why our Banking training is exam-focused. Our course is structured to cover all the topics and concepts needed for success on the exam. We also align our training with the ADIT syllabus, ensuring that each section of our program corresponds to the exam's content.  How we support our students MojiTax supports students on the course in several ways. Firstly, the course is self-paced, meaning students can work through the material at their own pace and have access to it 24/7. Secondly, the course is designed to be exam-focused, ensuring that students are well-prepared to take the ADIT professional exam. Finally, MojiTax aims to respond to all inquiries from students within 24 working hours, ensuring that students receive prompt support and assistance when needed. Our resources Our students have access to a range of training materials and assessments designed to support their learning and progress. These include: Presentations: E-Textbook: Intergovernmental Materials: Access to relevant intergovernmental materials, such as tax treaties, OECD guidelines, and other relevant publications. Multiple-Choice Questions: ADIT Revision Questions: MojiTax Exam 01 Introduction Introduction to Mojitax Banking TaxationIntroduction to MojiTax Banking TaxationADIT/CIOT websiteADIT Syllabus: Banking 02 Part 1: Fundamental tax issues - 10% Presentation: Fundamental tax issuesChapter 1: Fundamental tax issuesQuiz 1: Test your knowledge 03 Part 2: Banking regulations and tax implications of bank operating models and capital/funding allocations - 20% Presentation: Banking regulations and tax implications of bank operating models and capital/funding allocationsChapter 2: Banking regulations and tax implications of bank operating models and capital/funding allocationsQuiz 2: Test your knowledgeADIT Style Questions with Model Answers 04 Part 3: Tax implications for banking activities - 20% Presentation: Tax implications for banking activitiesChapter 3: Tax implications for banking activitiesQuiz 3: Test your knowledgeADIT Style Questions with Model Answers 05 Part 4: Transaction taxes and withholding taxes - 15% Presentation: Transaction taxes and withholding taxesChapter 4: Transaction taxes and withholding taxesQuiz 4: Test your knowledgeADIT Style Questions with Model Answers 06 Part 5: Special topics - 20% Presentation: Special topicsChapter 5: Special topicsQuiz 5: Test your knowledgeADIT Style Questions with Model Answers 07 Part 6: The OECD and EU context - 15% Presentation: OECD and EU contextChapter 6: OECD and EU contextQuiz 6: Test your knowledgeADIT Style Questions with Model Answers 08 Examination & Certificate Assessment GuidanceAssessment & Certificate PortalModule Feedback

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the The Machine Learning Pipeline on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.