683 Practitioner courses in Manchester delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

This Reiki Master Teacher Course has been created for Reiki practitioners who want to share their knowledge and experience of practising Reiki and who want to learn how to teach others. This course is designed to teach you how to become a Reiki Master Teacher, so that you can learn how to prepare Reiki training courses and teach and attune others. You will be shown how to plan, design and create your courses, how to deliver them and what length they should be for each level of Reiki.

This Reiki Master Teacher Course has been created for Reiki practitioners who want to share their knowledge and experience of practising Reiki and who want to learn how to teach others. This course is designed to teach you how to become a Reiki Master Teacher, so that you can learn how to prepare Reiki training courses and teach and attune others. You will be shown how to plan, design and create your courses, how to deliver them and what length they should be for each level of Reiki.

This Reiki Master Teacher Course has been created for Reiki practitioners who want to share their knowledge and experience of practising Reiki and who want to learn how to teach others. This course is designed to teach you how to become a Reiki Master Teacher, so that you can learn how to prepare Reiki training courses and teach and attune others. You will be shown how to plan, design and create your courses, how to deliver them and what length they should be for each level of Reiki.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for solutions architects, solution-design engineers, developers seeking an understanding of AWS architecting and individuals seeking the AWS Solutions Architect-Associate certification. Overview Identify AWS architecting basic practices. Explore using the AWS management tools: The AWS Console, Command Line Interface (CLI), and CloudFormation in a lab environment. Examine the enforcement of accounts security using policies. Identify the elements that build an elastic, secure, virtual network that includes private and public subnets. Practice building an AWS core networking infrastructure. Determine strategies for a layered security approach to Virtual Private Cloud (VPC) subnets. Identify strategies to select the appropriate compute resources based on business use-cases. Practice building a VPC and adding an Elastic Cloud Compute (EC2) instance in a lab environment. Practice installing an Amazon Relational Database Service (RDS) instance and an Application Load Balancer (ALB) in the VPC you created. Compare and contrast AWS storage products and services, based on business scenarios. Compare and contrast the different types of AWS database services based on business needs. Practice building a highly available, auto-scaling database layer in a lab. Explore the business value of AWS monitoring solutions. Identify the role of monitoring, event driven load balancing, and auto scaling responses, based on usage and needs. Identify and discuss AWS automation tools that will help you build, maintain and evolve your infrastructure. Discuss network peering, VPC endpoints, gateway and routing solutions based on use-cases. Discuss hybrid networking configurations to extend and secure your infrastructure. Discuss the benefits of microservices as an effective decoupling strategy to power highly available applications at scale. Explore AWS container services for the rapid implementation of an infrastructure-agnostic, portable application environment. Identify the business and security benefits of AWS serverless services based on business examples. Practice building a serverless infrastructure in a lab environment. Discuss the ways in which AWS edge services address latency and security. Practice building a CloudFront deployment with an S3 backend in a lab environment. Explore AWS backup, recovery solutions, and best practices to ensure resiliency and business continuity. Build a highly available and secure cloud architecture based on a business problem, in a project-based facilitator-guided lab. Architecting on AWS is for solutions architects, solution-design engineers, and developers seeking an understanding of AWS architecting. In this course, you will learn to identify services and features to build resilient, secure and highly available IT solutions on the AWS Cloud. Architectural solutions differ depending on industry, types of applications, and business size. AWS Authorized Instructors emphasize best practices using the AWS Well-Architected Framework, and guide you through the process of designing optimal IT solutions, based on real-life scenarios. The modules focus on account security, networking, compute, storage, databases, monitoring, automation, containers, serverless architecture, edge services, and backup and recovery. At the end of the course, you will practice building a solution and apply what you have learned with confidence. Prerequisites AWS Cloud Practitioner Essentials classroom or digital training, or Working knowledge of distributed systems Familiarity with general networking concepts Familiarity with IP addressing Working knowledge of multi-tier architectures Familiarity with cloud computing concepts 0 - Introductions & Course Map review Welcome and course outcomes 1 - Architecting Fundamentals Review AWS Services and Infrastructure Infrastructure Models AWS API Tools Securing your infrastructure The Well-Architected Framework Hands-on lab: Explore Using the AWS API Tools to Deploy an EC2 Instance 2 - Account Security Security Principals Identity and Resource-Based Policies Account Federation Introduction to Managing Multiple Accounts 3 - Networking, Part 1 IP Addressing Amazon Virtual Private Cloud (VPC), Patterns and Quotas Routing Internet Access Network Access Control Lists (NACLs) Security Groups 4 - Compute Amazon Elastic Cloud Compute (EC2) EC2 Instances and Instance Selection High Performance Computing on AWS Lambda and EC2, When to Use Which Hands-On Lab: Build Your Amazon VPC Infrastructure 5 - Storage Amazon S3, Security, Versioning and Storage Classes Shared File Systems Data Migration Tools 6 - Database Services AWS Database Solutions Amazon Relational Database Services (RDS) DynamoDB, Features and Use Cases Redshift, Features, Use Cases and Comparison with RDS Caching and Migrating Data Hands-on Lab: Create a Database Layer in Your Amazon VPC Infrastructure 7 - Monitoring and Scaling Monitoring: CloudWatch, CloudTrail, and VPC Flow Logs Invoking Events 8 - Automation CloudFormation AWS Systems Manager 9 - Containers Microservices Monitoring Microservices with X-Ray Containers 10 - Networking Part 2 VPC Peering & Endpoints Transit Gateway Hybrid Networking Route 53 11 - Serverless Architecture Amazon API Gateway Amazon SQS, Amazon SNS Amazon Kinesis Data Streams & Kinesis Firehose Step Functions Hands-on Lab: Build a Serverless Architecture 12 - Edge Services Edge Fundamentals Amazon CloudFront AWS Global Accelerator AWS Web Application Firewall (WAF), DDoS and Firewall Manager AWS Outposts Hands-On Lab: Configure an Amazon CloudFront Distribution with an Amazon S3 Origin 13 - Backup and Recovery Planning for Disaster Recovery AWS Backup Recovery Strategie Additional course details: Nexus Humans Architecting 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 Architecting 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.

CRRUK equips professionals with the concepts, skills and tools to build conscious, intentional relationships, and to coach relationship systems of any size.

Management of Risk (M_o_R®) Foundation: In-House Training This M_o_R® Foundation course prepares learners to demonstrate knowledge and comprehension of the four elements of the M_o_R framework: Principles, Approach, Processes, Embedding and Reviewing and how these elements support corporate governance. The M_o_R Foundation Course is also a prerequisite for the M_o_R Practitioner qualification. What you will Learn At the end of the M_o_R Foundation course, participants will gain competencies in and be able to: Describe the key characteristics of risk and the benefits of risk management List the eight M_o_R Principles List and describe the use of the key M_o_R Approach documents Create Probability and Impact scales Define and distinguish between risks and issues Create a Risk Register Create a Stakeholder map Identify the key roles in risk management Use the key techniques and describe specialisms in risk management Undertake the M_o_R Foundation examination Introduction Introduction to the M_o_R course What is a risk? What is risk management? Why is risk management so important? Basic risk definitions The development of knowledge about risk management Corporate governance and internal control Where and when should risk management be applied? M_o_R Principles The purpose of M_o_R principles Aligns with objectives Fits the context Engages stakeholders Provides clear guidance Informs decision-making Facilitates continual improvement Creates a supportive culture Achieves measurable value Risk management maturity models M_o_R Approach Relationship between the documents Risk management policy Risk management process guide Risk management strategy Risk register Issue register Risk response plan Risk improvement plan Risk communications plan M_o_R Process Common process barriers Identify contexts Identify the risks Assess estimate Assess evaluate Plan Implement Communication throughout the process M_o_R Perspectives Strategic perspective Program perspective Project perspective Operational perspective Risk Specialisms Business continuity management Incident and crisis management Health and Safety management Financial risk management Environmental risk management Reputational risk management Contract risk management

Management of Risk (M_o_R®) Foundation: Virtual In-House Training This M_o_R® Foundation course prepares learners to demonstrate knowledge and comprehension of the four elements of the M_o_R framework: Principles, Approach, Processes, Embedding and Reviewing and how these elements support corporate governance. The M_o_R Foundation Course is also a prerequisite for the M_o_R Practitioner qualification. What you will Learn At the end of the M_o_R Foundation course, participants will gain competencies in and be able to: Describe the key characteristics of risk and the benefits of risk management List the eight M_o_R Principles List and describe the use of the key M_o_R Approach documents Create Probability and Impact scales Define and distinguish between risks and issues Create a Risk Register Create a Stakeholder map Identify the key roles in risk management Use the key techniques and describe specialisms in risk management Undertake the M_o_R Foundation examination Introduction Introduction to the M_o_R course What is a risk? What is risk management? Why is risk management so important? Basic risk definitions The development of knowledge about risk management Corporate governance and internal control Where and when should risk management be applied? M_o_R Principles The purpose of M_o_R principles Aligns with objectives Fits the context Engages stakeholders Provides clear guidance Informs decision-making Facilitates continual improvement Creates a supportive culture Achieves measurable value Risk management maturity models M_o_R Approach Relationship between the documents Risk management policy Risk management process guide Risk management strategy Risk register Issue register Risk response plan Risk improvement plan Risk communications plan M_o_R Process Common process barriers Identify contexts Identify the risks Assess estimate Assess evaluate Plan Implement Communication throughout the process M_o_R Perspectives Strategic perspective Program perspective Project perspective Operational perspective Risk Specialisms Business continuity management Incident and crisis management Health and Safety management Financial risk management Environmental risk management Reputational risk management Contract risk management

Second Degree Usui Reiki Course You will be taught how to conduct a healing session; protocols, etc and how to perform distance healings. This course is comprehensive and detailed. You will have all the tools you need to start using Reiki on others and their pets. Upon completion of this comprehensive and insightful Reiki course, you can go on to get insurance to set up as a Reiki Practitioner and start seeing clients.

The course is relevant to anyone requiring an understanding of the use of Agile or looking to adopt it. This includes, but is not limited to, organisational leaders and managers, marketing executives and managers, and/or all professionals working in an Agile environment, including software sesters, developers, business analysts, UX designers, project management office (PMO), project support and project coordinators.