2188 Developer courses in Leeds delivered Online

Duration 3 Days 18 CPD hours Overview VMware vSphere: What's New [V8] https://lms.nhcms.neYou will learn essential Terraform concepts including: Writing Terraform HCL code Deploying into common clouds such as AWS, Azure, Docker, Kubernetes, and VMWare Where Terraform fits in the Enterprise CI/CD model Differences between Terraform and Ansible As enterprises seek to deploy and maintain increasingly complex cloud infrastructure, there is a necessity to use ?Infrastructure as Code? (IaC) tools, like Terraform. An open-source, state management tool developed by HashiCorp, Terraform allows developers to use a common coding interface to work through their various clouds safely and efficiently. Attendees will leave being able to write and understand Terraform code (HCL), have a clear understanding of Terraform?s various components and supporting tools, as well as when to reach for Terraform over another IaC tool, such as Ansible. This class prepares you for Terraform Certifcation. Up and Running with Terraform Terraform Overview Defining ?declarative? How to think about Terraform (versus Ansible) Reviewing the Terraform Configuration Running the Terraform Configuration Provisioners Syntax Low Level? HCL syntax Style Conventions Comments Blocks Arguments JSON Configuration Syntax Resources Meta-Arguments depends_on count for_each provider lifecycle Data Sources Variables and Output Input Variables Output Values Local Values Functions String Collection Numeric Encoding Filesystem Date and Time Hash and Crypto IP Network Type Conversion Modules Module Blocks Module Sources Meta Arguments Terraform Templates templatefile Function Template Demonstration Introducing Data Sources Creating an External Data Source Building tftpl template files Expressions Types and Values Strings and Templates Reference to Values Operators Function Calls Conditionals For Expressions Splat Expressions Dynamic Blocks Type Constraints Version Constraints State Understanding the importants of states state storage and locking importing existing resources Remote State What to do when local state is lost CICD Piplines with Terraform Terraform and GitLab pipelines Terraform and Jenkins pipelines Enterprise Case Studies Terraform and Docker Terraform and Kubernetes Terraform and Amazon AWS Terraform and Azure Terraform and VMWare Understanding how to apply Terraform to your unique infrastructure Beyond Basics Intro to Go Programming Terraform Cloud Additional HashiCorp Offerings Backends Secrets Additional course details: Nexus Humans Terraform 101 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 Terraform 101 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.

Duration 2 Days 12 CPD hours This course is intended for People working in an organization aiming to improve performance, especially in response to digital transformation or disruption. Any roles involved in the creation and delivery of products or services: Leadership and CXO, especially CIO, CTO, CPO, and CVO Transformation and evolution leads and change agents Value stream architects, managers, engineers Scrum Masters, agile and DevOps coaches and facilitators Portfolio, product and project managers, and owners Business analysts Architects, developers, and engineers Release and environment managers IT Ops, service and support desk workers Customer experience and success professionals Overview After completing this course, students will be able to: Describe the origins of value stream management and key concepts such as flow, value, and delivery Describe what value stream management is, why it's needed and the business benefits of its practice Describe how lean, agile, DevOps, and ITSM principles contribute to value stream management Identify and describe value streams, where they start and end, and how they interconnect Identify value stream roles and responsibilities Express value streams visually using mapping techniques, define current and target states and hypothesis backlog Write value stream flow and realization optimization hypotheses and experiments Apply metrics such as touch/processing time, wait/idle time, and cycle time to value streams Understand flow metrics and how to access the data to support data-driven conversations and decisions Examine value realization metrics and aligning to business outcomes, and how to sense and respond to them (outcomes versus outputs) Architect a DevOps toolchain alongside a value stream and data connection points Design a continuous inspection and adaptation approach for organizational evolution The Value Stream Management Foundation course from Value Stream Management Consortium, and offered in partnership with DevOps Institute, is an introductory course taking learners through a value stream management implementation journey. It considers the human, process, and technology aspects of this way of working and explores how optimizing value streams for flow and realization positively impacts organizational performance. History and Evolution of Value Stream Management and its Application Value stream management?s origins Definitions of value stream management Flow Lean and systems thinking and practices Agile, DevOps and other frameworks Research and analysis Identifying Value Streams What is a value stream? Identifying value streams Choosing a value stream Digital value streams Value stream thinking Mapping Value Streams Types of maps Value stream mapping The fuzzy front end Artifacts 10 steps to value stream mapping Mapping and management VSM investment case Limitations of value stream mapping Connecting DevOps Toolchains CICD and the DevOps toolchain Value stream management processes Value stream management platforms DevOps tool categories Building an end-to-end DevOps toolchain Common data model and tools integrations Value Stream Metrics The duality of VSM Downtime in technology Lean, DORA and Flow metrics Definition of Done Value metrics Benefits hypotheses Value streams as profit centers KPIs and OKRs Inspecting the Value Stream 3 Pillars of Empiricism Organizational performance Visibility When to inspect Data and discovery Insights and trends Organizing as Value Streams Value stream alignment Team types and topologies Project to product Hierarchy to autonomy Target Operating Model Value stream people Value stream roles Value stream funding Evolving Value Streams Why now? Transitions VSM capability matrix VSM culture iceberg Learning Making local discoveries global improvements Managing value stream interdependencies

Duration 3 Days 18 CPD hours This course is intended for This course is designed for project leaders, scrum masters, coaches, product owners, and team members who intend on taking the PMI-ACP© exam. This course provides participants with a foundation of the PMI-ACP© exam. Participants will be introduced to PMI Agile concepts and practices with banks of sample questions. PMI-ACP© Exam Preperation PMI-ACP© Exam Particulars Overview PMI-ACP© Exam Particulars PMI-ACP© Candidate Requirements PMI-ACP© Candidate Fees PMI-ACP© Exam Application Process Core Agile Concepts Core Agile Concepts Overview Traditional Project Management Methodologies Drawbacks of Waterfall Methodologies Agile Approach Empirical Process Control Agile and Traditional Project Management Choice of Methodologies/Frameworks The Agile Manifesto The Agile Manifesto Overview Manifesto Contributors Manifesto Values Manifesto Principles Common Agile Methodology Elements Common Agile Methodology Elements Overview Project (Product; Release) Initiation Agile Planning Iteration Planning and Executing Project Initiation Project Initiation Overview Determine Project Justifications and Metrics Provide Value-Driven Delivery Write Project Vision Statement Create Project Charter Identify Stakeholders and Leader/Coach Form Project Team Agile Teams and Team Space Agile Teams and Team Space Overview Scrum Master/Coach Product Owner/Customer Team Members/Developers (XP) Trackers and Testers Other Roles Team Space Physical Space Recommendations Agile Planning Agile Planning Overview Develop Epics and Stories Create Stories Non-Customer Facing Stories Personas and Extreme Personas Story Maps Estimating Stories Prioritizing Stories Create Product Backlog Create Product Roadmap Conduct Release Planning Create Parking Lot Iterations/Sprints Iterations/Sprints Overview Velocity Determination Iteration Planning Meeting Iteration Planning Guidelines Development Testing Daily Standup Meetings Progress Tracking Velocity Tracking Interpersonal Aspects of Agile Interpersonal Aspects of Agile Overview Methodologies and Uncertainty Coach/Scrum Master Team Motivation Soft Skills Emotional Intelligence Collaboration Negotiations Active listening Conflict Resolution Speed Leas? Model of Group Conflict Conducting Retrospectives Mindsets of Agile Coaches Leadership Stages Key Coaching Responsibilities Agile Methodologies Agile Methodologies Overview XP and Scrum Terms XP Terms and Concepts XP Primary Practices XP Corollary Practices Scrum Lean Software Development Seven Principles of Lean Seven Types of Muda Responsibilities Core Beliefs of Lean-Agile Software Development Other Principles of Lean-Agile Software Development Value Stream Mapping Lean-Agile Software Development Portfolio Management Additional course details: Nexus Humans PMI Agile Certified Practitioner (PMI-ACP) Exam Preparation 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 PMI Agile Certified Practitioner (PMI-ACP) Exam Preparation 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.

Duration 2 Days 12 CPD hours This course is intended for Experienced system administrators, system integrators, and developers responsible for designing and implementing vRealize Log Insight Overview By the end of the course, you should be able to meet the following objectives: Identify features and benefits of vRealize Log Insight Determine the vRealize Log Insight cluster that meets your monitoring requirements Describe the vRealize Log Insight architecture and use cases Deploy and configure a vRealize Log Insight cluster Use the Interactive Analytics tab to get a deep understanding of log data Create and manage queries Managing vRealize Agents and Agent Groups Create various custom dashboards Describe and use the vRealize Log Insight widgets Extend the capabilities of vRealize Log Insight by adding content packs and configuring solutions This two-day course focuses on deploying, using, and managing VMware vRealize© Log Insight? product. It provides you with the knowledge and skills to deploy a vRealize Log Insight cluster that meets the monitoring requirements of your environment. This course covers the configuration and use of vRealize Log Insight to collect and manage logs from a variety of VMware and third-party products. This course discusses interfacing vRealize Log Insight with other systems to monitor, troubleshoot, and secure a data center. Course Introduction Introductions and course logistics Course objectives Introduction to vRealize Log Insight Describe a cloud management platform Explain the importance of efficient log management Describe the requirements for a log analytics solution Describe the key benefits of vRealize Log Insight Describe the key features of vRealize Log Insight Describe the various stages of log processing vRealize Log Insight Architecture and Deployment Identify the minimum requirements for deploying vRealize Log Insight Using vRealize Log Insight Sizing Calculator Describe vRealize Log Insight compatibility with other VMware products Describe the vRealize Log Insight architecture Determine the correct vRealize Log Insight deployment for an environment Install the vRealize Log Insight virtual appliance vRealize Log Insight Queries and Dashboards Describe the primary functions of the vRealize Log Insight UI Use the Interactive Analytics tab to get a deep understanding of log data Create and Manage queries Create vRealize Log Insight custom dashboards Describe and use the vRealize Log Insight widgets Administering vRealize Log Insight Describe User Access Control in vRealize Log Insight Describe User Management Access the vRealize Log Insight Administration page Configure vRealize Log Insight settings Manage certificates Configure system notifications Upgrade vRealize Log Insight Describe the vRealize Log Insight Importer vRealize Log Insight Integrations Describe how to integrate vRealize Operations with vRealize Log Insight Describe the advantages of integrating vRealize Operations with vRealize Log Insight Describe the advantages of using the vSAN content pack Install NSX content packs Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware vRealize Log Insight: Deploy and Manage [V8.4] 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 VMware vRealize Log Insight: Deploy and Manage [V8.4] 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.

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.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Applied AI: Building Recommendation Systems with Python (TTAI2360) 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 Applied AI: Building Recommendation Systems with Python (TTAI2360) 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.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for technology leaders, solution developers, project managers, organizational decision makers, and other individuals seeking to demonstrate a vendor-neutral, cross-industry understanding of ethics in emerging data-driven technologies, such as AI, robotics, IoT, and data science. This course is also designed for professionals who want to pursue the CertNexus Certification Exam CET-110: Certified Ethical Emerging Technologies. Overview In this course, you will incorporate ethics into data-driven technologies such as AI, IoT, and data science. You will: Describe general concepts, theories, and challenges related to ethics and emerging technologies. Identify ethical risks. Practice ethical reasoning. Identify and mitigate safety and security risks. Identify and mitigate privacy risks. Identify and mitigate fairness and bias risks. Identify and mitigate transparency and explainability risks. Identify and mitigate accountability risks. Build an ethical organization. Develop ethical systems in technology-focused organizations. Mutually reinforcing innovations in computing and engineering are catapulting advances in technological production. From blockchain and artificial intelligence (AI) to gene editing and the Internet of Things (IoT), these advances come with tremendous opportunities for improvement in productivity, efficiency, and human well-being. But as scandals increasingly demonstrate, these advances also introduce new and serious risks of conflict and harm.Technology professionals now face growing demands to identify and mitigate ethical risks to human rights and the environment, as well as to navigate ethical tradeoffs between qualities such as privacy and accuracy, fairness and utility, and safety and accountability. This course provides the tools to identify and manage common ethical risks in the development of emerging data-driven technologies. It distills ethical theory, public regulations, and industry best practices into concrete skills and guidelines needed for the responsible development of digital products and services. By following the course's practical, problems-based approach, learners will become adept at applying theories, principles, frameworks, and techniques in their own roles and organizations. Introduction to Ethics of Emerging Technologies Topic A: What?s at Stake Topic B: Ethics and Why It Matters Topic C: Ethical Decision-Making in Practice Topic D: Causes of Ethical Failures Identifying Ethical Risks Topic A: Ethical Reasons Topic B: Stumbling Blocks for Ethical Reasoning Topic C: Identify Ethical Risks in Product Development Topic D: Tools for Identifying Ethical Risks Topic E: Use Regulations, Standards, and Human Rights to Identify Ethical Risks Ethical Reasoning in Practice Topic A: Ethical Theories Topic B: Use Ethical Decision-Making Frameworks Topic C: Select Options for Action Topic D: Avoid Problems in Ethical Decision-Making Identifying and Mitigating Security Risks Topic A: What Is Security? Topic B: Identify Security Risks Topic C: Security Tradeoffs Topic D: Mitigate Security Risks Identifying and Mitigating Privacy Risks Topic A: What Is Privacy? Topic B: Identify Privacy Risks Topic C: Privacy Tradeoffs Topic D: Mitigate Privacy Risks Identifying and Mitigating Fairness and Bias Risks Topic A: What Are Fairness and Bias? Topic B: Identify Bias Risks Topic C: Fairness Tradeoffs Topic D: Mitigate Bias Risks Identifying and Mitigating Transparency and Explainability Risks Topic A: What Are Transparency and Explainability? Topic B: Identify Transparency and Explainability Risks Topic C: Transparency and Explainability Tradeoffs Topic D: Mitigate Transparency and Explainability Risks Identifying and Mitigating Accountability Risks Topic A: What Is Accountability? Topic B: Identify Accountability Risks Topic C: Accountability Tradeoffs Topic D: Mitigate Accountability Risks Building an Ethical Organization Topic A: What Are Ethical Organizations? Topic B: Organizational Purpose Topic C: Ethics Awareness Topic D: Develop Professional Ethics within Organizations Developing Ethical Systems in Technology-Focused Organizations Topic A: Policy and Compliance Topic B: Metrics and Monitoring Topic C: Communication and Stakeholder Engagement Topic D: Ethical Leadership

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview This skills-focused combines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Building Recommendation Systems with Python (TTAI2360) 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 Building Recommendation Systems with Python (TTAI2360) 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.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for Information workers, IT Professionals and Developers. Students should have an existing working knowledge of either Microsoft Dynamics 365 or Microsoft Dynamics CRM. Overview Understand the features and tools that exist in Microsoft Dynamics 365 for Customizers Be aware of integrating complimenting Microsoft products such as SharePoint, Skpe for Business and Exchange Undertake and carry out the initial setup and configuration required in a Microsoft Dynamics 365 deployment Design and configure a comprehensive Security model using the inbuilt tools in Microsoft Dynamics 365 Customize the Dynamics 365 schema by creating custom Entities, Fields and Relationships Design custom Information Forms, Quick View Forms, Quick Create Forms and System Views Create System Charts, Dashboards and Interactive Experience Dashboards Create and manage Business Rules using the Business Rule Designer Plan, design and implement best practice Workflow, Business Process Flows and Custom Actions Be able to apply best practice methodology using Unmanaged and Managed Solutions to deploy Microsoft Dynamics 365 customizations and patches This course provides students with a detailed hands-on experience of setting up, customizing, configuring and maintaining the CRM components of Microsoft Dynamics 365. Attendees of this course will gain an in-depth understanding of the Dynamics 365 security model, learn how to customize the Dynamics 365 framework, create and maintain powerful workflows and business process flows and use solutions to package and deploy customizations across multiple Dynamics 365 environments. The course applies to both Business and Enterprise Editions of Dynamics 365 as well as Online and On-premise deployments. Introduction Getting familiar with the versions of Microsoft Dynamics CRM\365 Get acquainted with the Dynamics 365 framework Review the Dynamics 365 interfaces, devices and apps Understand the tools for Dynamics 365 customizers A brief overview of Solutions Understand the differences between Dynamics 365 organisations and environments Review further reading and resources Set up the lab environment - Acme Enterprises Event Management Solution Initial Setup and Configuration An introduction to Dynamics 365 online setup An introduction to Dynamics 365 on premise setup Review the System Settings area Understand how to configure Auto Save Settings Understand how to configure Format Settings Understand how to configure Email Settings Understand how to configure Skype Integration Understand how to configure SharePoint Integration Security Design and configure Business Units Configure Security Roles Manage Users and Teams Implement Access Teams Configure Hierarchy Security Creating and Managing Entities Introduction to the Dynamics 365 schema Review the different Entity Types Create new Custom Entities Managing Entity Ownership Managing Entity Properties Custom Entity Security Review Entities and Solutions Customizing Fields Introduction to Field Customization Understand the different Field Types Review Field Formats Create a new Field Review Fields and Solutions Implement a Calculated Field Configure Field Level Security Customizing Relationships and Mappings Introduction to Relationships Review the different Relationship Types Create a Relationship Review Relationships and Solutions Understand Relationship Behavior Implement a Hierarchy Relationship Configure Field Mappings Customizing Forms, Views and Visualizations The process to create a new Form Review the different Form types Using the Form Designer Customizing the Main, Quick View and Quick Create Forms Configure Form Security Review the different View types Customizing System Views Customizing System Charts and Dashboards Workflows, Business Process Flows and Custom Actions Introduction to Processes Workflow Business Process Flows Custom Actions Solution Management An introduction to Solution Management How to add and administer components in a Solution The differences between unmanaged and managed Solutions How to export and import a Solution How to set Managed Properties for a Solution What happens when you delete a Solution How to Clone a Solution Patch How to Clone a Solution

Duration 1.875 Days 11.25 CPD hours This course is intended for The job roles best suited to the material in this course are: team leaders, project managers, managers of scrum teams, teams transitioning to scrum, professionals intending to pursue the scrum master certification. Overview How to use the Scrum Framework to deliver products and services faster and with higher quality. How to leverage lean principles to identify waste in a system, process, or organization. Techniques and metics Scrum Masters use to improve. team happiness and performance. The patterns and practices of high-performing teams. How the Scrum Master role scales in an Agile implementation. This course is an adaptive, repeatable process that equips individuals and organizations in how to thrive in a world where change is the only constant. From Fortune 100 companies (Google, Amazon, Apple, etc) to nonprofits. Scrum has a proven record of reducing burnout, doubling throughput in half the time, and increase employee happiness. Core Scrum The Scrum Framework The Origins of Scrum (Optional) The Scrum Team Developers Scrum Master Leadership/Management Scrum Events The Sprint Product Backlog Re1nement Estimation Sprint Planning Sprint Review Sprint Retrospective Daily Scrum Scrum Artifacts Lean Principles Describe a Kaizen mindset and explain how small, iterative changes can lead to revolutionary leaps. Describe the three pillars of Scrum ? Transparency, Inspection, and Adaptation,? which implement the work of Ogunnaike and Ray. Explain the importance of reducing and eliminating waste in the system. Perform a root-cause analysis (e.g., using the ?5 Whys? technique). Assess the Process EZciency of their Scrum Team and recall that the de1nition of Lean is a Process EZciency of 25% or higher. Explain how the work of Takeuchi and Nonaka on Lean and the Toyota Production System paved the way for Scrum. Describe the origins of the name ?Scrum? from Takeuchi and Nonaka?s ?New New Product Development Game. Recognize that a Lean mindset suggests that you address a defect immediately after it is identi1ed as opposed to a mindset where defects are stored to be 1xed later. Agile Manifesto Recognize the four values of the Agile Manifesto and their signi1cance in the context of complex adaptive systems. Identify the 12 principles of the Agile Manifesto and describe their function in guiding practices that support teams in implementing and executing with agility. Explain that Scrum is one of the driving forces that gave rise to the Agile movement and predates the Agile Manifesto. Explain why the majority of ?Agile? teams are late, over-budget, and with unhappy customers (i.e., not agile) and explain what needs to be done to 1x that. Patterns of High Performing Teams Yesterday?s Weather Happiness Metric Teams that Finish Early Accelerate Faster Stable Teams Swarming Interrupt Buffer Good Housekeeping (formerly Daily Clean Code) Scrum Emergency Procedure Scrum@Scale Descaling Scaling the Scrum Master Registered Scrum Master Credential Access and complete the Registered Scrum Master by Scrum Inc. exam. Download their Registered Scrum Master Credential (upon successful completion of the exam). Be Recognized in the International Registry of Agile ProfesstionalsTM State the renewal process. Additional course details: Nexus Humans Agile Scrum 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 Agile Scrum 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.