766 Modeling courses in Leicester delivered Online

This course covers incident response methods and procedures are taught in alignment with industry frameworks such as US-CERT's NCISP (National Cyber Incident Response Plan), and Presidential Policy Directive (PPD) 41 on Cyber Incident Coordination Policy.

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Description This PMI Risk Management Professional (PMI-RMP) Exam Preparation Course is designed to prepare you for the certification exam. This

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Duration 3 Days 18 CPD hours This course is intended for This course is designed for anyone currently working on Agile-based projects or having experience with other forms of project management experience and want to add Agile PM skills and knowledge to their portfolio. Anyone with any experience in project-based work, either from the customer or supplier side can benefit from this course, including but not limited to: project manager, team leaders and managers or project employees. Overview AgilePM ™ certification is the result of collaboration between APMG-International and the DSDM Consortium. DSDM (Dynamic Systems Development Method) is the longest existing Agile method and the only Agile method aimed at managing Agile projects. It Has evolved over the years into a Project Framework, and AgilePM is a subset tailored to the Agile project manager. Students will be explained how the model is set up, how the different project activities and project roles are connected and how AgilePM handles project management. This course provides preparation for the Foundation exam of APMG. Training Day 1 - AgilePM Foundation Topics What is Agile? Choosing an appropriate Agile approach philosophy, principles and project variables preparing for success The DSDM Process Training Day 2 - AgilePM Foundation Topics The People ? DSDM Roles and Responsibilities The DSDM Products Key practices ? prioritization and timeboxes Training Day 3 - AgilePM Foundation Topics Planning and control throughout the lifecycle Other practices: facilitated workshops, modeling and iterative development

In this course, you will quickly learn how to build DNNs (Deep Neural Networks) and how to train them. This learning-by-doing course will also help you master the elementary concepts and methodology with Python. You need to have a basic knowledge of python to get the best out of this course.

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Duration 4 Days 24 CPD hours This course is intended for The App Maker builds solutions to simplify, automate, and transform tasks and processes for themselves and their team where they have deep expertise in the solution business domain. They have basic data modeling, user experience design, requirements analysis, and process analysis skills. The App Maker creates and enforces business processes, structures digital collection of information, improves efficiency of repeatable tasks, and automates business processes. The App Maker uses the Maker tools of Power Platform to solve business problems. They may use advanced features of Microsoft apps and third-party productivity tools. The App Maker is aware of the capabilities and limitations of available tools and understands how to apply them. The App Maker is self-directed, and solution focused. They may not have formal IT training but are comfortable using technology to solve business problems with a personal growth mindset. They understand the operational need and have a vision of the desired outcome. They approach problems with phased and iterative strategies. This course will teach you how to build apps with low-code techniques to simplify, automate, and transform business tasks and processes using Microsoft Power Platform. This course contains a 1-day Applied Workshop. This workshop will allow you to practice your App Maker skills by creating an end-to-end solution to solve a problem for a fictitious company. The solution will include a Microsoft Dataverse database, Power Apps canvas app, and Power Automate flows. 1 - Get started with Microsoft Power Platform for app makers Identify components Create apps Get started with Microsoft Dataverse Work with Copilot in Microsoft Power Platform 2 - Create tables in Dataverse Table characteristics Table relationships Dataverse logic and security Dataverse auditing Dual-write vs. virtual tables 3 - Create and manage columns within a table in Dataverse Define columns in Microsoft Dataverse Column types in Microsoft Dataverse Add a column to a table Primary name column Restrictions that apply to columns in a table Create an auto numbering column Create an alternate key 4 - Load/export data and create data views in Dataverse View data in a table Create or edit views of data in a table Dataverse data import options Load data into a table Dataverse data export options Export Add, update, or delete data in a table by using Excel Import data using Power Query 5 - Export data from Dataverse and use Microsoft Excel to edit records Export data to Excel Edit and update data in Excel 6 - Get started with Microsoft Dataverse for Teams Dataverse for Teams vs Dataverse Provision your first Dataverse environment Create your first table to store data 7 - Build your first app with Power Apps and Dataverse for Teams Create your first app with the hero template Customize your app with Power Apps Studio Publish your app Install template apps 8 - Build your first workflow with Power Automate and Dataverse for Teams Types of workflows that Power Automate can build in Dataverse Schedule a flow 9 - Create reports with Power BI and Dataverse for Teams Connect to and transform Dataverse for Teams data from Power BI Create a Power BI report Publish the report Share the data 10 - Get started building with Power BI Use Power BI Building blocks of Power BI Tour and use the Power BI service 11 - Explore what Power BI can do for you What can I do with the Power BI service as a consumer? View content in the Power BI service Collaborate and share in Power BI Find and view dashboards and reports 12 - Create and manage workspaces in Power BI Distribute a report or dashboard Monitor usage and performance Recommend a development life cycle strategy Troubleshoot data by viewing its lineage Configure data protection 13 - Manage semantic models in Power BI Use a Power BI gateway to connect to on-premises data sources Configure a semantic model scheduled refresh Configure incremental refresh settings Manage and promote semantic models Troubleshoot service connectivity Boost performance with query caching (Premium) 14 - Create dashboards in Power BI Configure data alerts Explore data by asking questions Review Quick insights Add a dashboard theme Pin a live report page to a dashboard Configure a real-time dashboard Set mobile view 15 - Implement row-level security Configure row-level security with the static method Configure row-level security with the dynamic method 16 - Create dashboards in Power BI Configure data alerts Explore data by asking questions Review Quick insights Add a dashboard theme Pin a live report page to a dashboard Configure a real-time dashboard Set mobile view 17 - Secure, publish, and share data in Power BI Share and use reports in Power BI Row-level security Publish a report to a Power BI workspace Share reports and user experience Protect data in Power BI Data refresh and alerts 18 - Embed Power BI content Embed Power BI reports Embed other Power BI content types Optimize the embedding experience 19 - How to build your first model-driven app with Dataverse Model-driven apps, powered by Microsoft Dataverse Explore sample apps 20 - Get started with model-driven apps in Power Apps Introducing model-driven apps Components of model-driven apps Design model-driven apps Incorporate business process flows 21 - Configure forms, charts, and dashboards in model-driven apps Forms overview Form elements Configure multiple forms Use specialized form components Configure views overview Configure grids Create and edit views Configure charts overview Dashboards overview Use interactive streams and tiles 22 - Manage Dynamics 365 model-driven app settings and security Configure role-based security Manage teams and business units Explore settings and customizations 23 - Use specialized components in a model-driven form Create business process flows Embed a canvas app in a model-driven form Add a timeline in a model-driven form Create a report in a model-driven form 24 - Get started with Power Apps canvas apps Start Power Apps Power Apps data sources Use Power Apps with Power Automate and Power BI Designing a Power Apps app 25 - Customize a canvas app in Power Apps Improve your app by making basic customizations Explore controls and screens in canvas apps 26 - How to build the User Interface in a canvas app in Power Apps Use themes to quickly change the appearance of your app Brand a control Icons Images Personalization Build for phones or tablets 27 - Navigation in a canvas app in Power Apps Understanding navigation The Navigate and Back functions More ways to use the Navigate function 28 - Manage apps in Power Apps Power Apps review 29 - Build a mobile-optimized app from Power Apps Learn about mobile-optimized apps Identify components to make a canvas app mobile-optimized Create a mobile-optimized app that uses responsive designs Identify performance considerations for a mobile-optimized canvas app 30 - Use and understand Controls in a canvas app in Power Apps Core properties of controls Entering and displaying data with text controls Additional controls for enhancing your app's usability Media Modern controls Work with component libraries 31 - Create formulas to change properties in a Power Apps canvas app Formulas overview Use a formula to modify the format of controls Use formulas to perform calculations Use a control to modify the property of other controls Conditional formatting Functions for validating data 32 - Use imperative development techniques for canvas apps in Power Apps Imperative versus declarative development The three types of variables in Power Apps Global variables Contextual variables Collections Additional variable concepts 33 - Manage apps in Power Apps Power Apps review 34 - Create formulas to change properties in a Power Apps canvas app Formulas overview Use a formula to modify the format of controls Use formulas to perform calculations Use a control to modify the property of other controls Conditional formatting Functions for validating data 35 - Create formulas to change behaviors in a Power Apps canvas app Formulas and functionality Understanding true and false Understanding control behaviors and actions Performing multiple actions in a formula Control the display mode through a formula Use controls and functions to create a dynamic formula 36 - Author a basic formula that uses tables and records in a Power Apps canvas app Records and tables Using the Table function Store a table Filter your table Use the lookup function to return a record Additional table functions 37 - Build a canvas app for a real estate solution with Copilot in Power Apps 38 - Get started with Power Automate Introducing Power Automate Create your first flow Troubleshoot flows 39 - Build approval flows with Power Automate Provide solutions to real-world scenarios. 40 - Build flows to manage user information 41 - Power Automate's deep integration across multiple data sources 42 - Use the Admin center to manage environments and data policies in Power Automate Administer flows Export and import flows Learn how to distribute button flows 43 - Use AI Builder in Power Automate AI Builder in Power Automate saves time Advanced usage of AI Builder in Power Automate 44 - Optimize your business process with process advisor Get familiar with process advisor Create your first recording Edit recordings and group actions Analyze recordings and interpret results Automation recommendations 45 - Optimize your business process with process advisor Get familiar with process advisor Create your first recording Edit recordings and group actions Analyze recordings and interpret results Automation recommendations 46 - Build flows for a real estate solution using Copilot in Power Automate When to use Copilot in Power Automate 47 - Use Dataverse triggers and actions in Power Automate Dataverse triggers Query data Create, update, delete, and relate actions 48 - Create tables in Dataverse Table characteristics Table relationships Dataverse logic and security Dataverse auditing Dual-write vs. virtual tables 49 - Create and manage columns within a table in Dataverse Define columns in Microsoft Dataverse Column types in Microsoft Dataverse Add a column to a table Primary name column Restrictions that apply to columns in a table Create an auto numbering column Create an alternate key 50 - Get started with Power Apps canvas apps Start Power Apps Power Apps data sources Use Power Apps with Power Automate and Power BI Designing a Power Apps app 51 - How to build the User Interface in a canvas app in Power Apps Use themes to quickly change the appearance of your app Brand a control Icons Images Personalization Build for phones or tablets 52 - Get started with Power Automate Introducing Power Automate Create your first flow Troubleshoot flows 53 - Challenge Project - Build a booking requests app with Power Apps and Power Automate Prepare

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Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.