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Register on the Learn DOM Manipulation with JavaScript today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get an e-certificate as proof of your course completion. The Learn DOM Manipulation with JavaScript is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Learn DOM Manipulation with JavaScript Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Learn DOM Manipulation with JavaScript, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.  Course Content Unit 01: Course Introduction What is the DOM? 00:02:00 Your first DOM update 00:05:00 Course Project: Code DOM Adventure 00:04:00 Unit 02: DOM Fundamentals HTML and the DOM 00:05:00 DOM standards 00:05:00 The BOM 00:04:00 The CSSOM 00:03:00 The tree (Data structure) 00:05:00 The DOM tree 00:11:00 The DOM and JavaScript 00:01:00 Unit 03: Code DOM Adventure App architecture 00:08:00 Challenge solution: The exit screen 00:04:00 App skeleton 00:05:00 The splash screen file 00:03:00 Download our asset kit now! 00:03:00 Including the assets 00:03:00 Unit 04: Creating elements Wiring the splash screen element 00:02:00 Creating elements at runtime 00:03:00 Appending HTML strings width append() 00:02:00 Appending nodes with append or append Child 00:03:00 Script order matters 00:04:00 Unit 05: Dynamic CSS Specifying classes to elements 00:04:00 Working with static styles 00:04:00 Defining styles at runtime 00:04:00 Querying the DOM to find elements 00:05:00 Unit 06: Profiling the pixel pipeline The pixel pipeline 00:02:00 Identifying bad practices 00:04:00 Profiling runtime performance 00:04:00 Batching DOM updates with document fragments 00:05:00 Too many nodes 00:04:00 Unit 07: Animation using DOM changes Removing nodes from the DOM 00:06:00 The animation frames 00:06:00 Our animate method 00:04:00 Our working animation! 00:06:00 Stopping the animation 00:08:00 Unit 08: Planning DOM changes with a state model Let's build the level! 00:02:00 2. Our state model to control the DOM from state. 00:10:00 Normalizing attributes 00:04:00 Our level class 00:04:00 Arrays, references and non-iterable empty slots 00:05:00 Building our state with an ugly oneliner 00:05:00 Module 04: The Language of Coaching 01:00:00 Write code for humans and normalize your code 00:05:00 Rendering the level element 00:05:00 Things are getting messy 00:06:00 Unit 09: Easy bundling Easy bundling 00:06:00 Bundle with the start script 00:01:00 Our dev server 00:02:00 Let's use DOMContentLoaded and ES Modules (ESM) 00:07:00 Dynamic style elements with CSS as ESM imports 00:04:00 Unit 10: DOM updates with basic state driven development Designing the shape of our state 00:02:00 Initializing our state in preparation to render DOM elements 00:05:00 DOM updates from state 00:08:00 Updates to state are reflected in the DOM 00:03:00 Modeling and render our chip walls 00:06:00 Unit 11: The player, Interacting with user input The player - Tech approach 00:02:00 Rendering the player with the DOM 00:07:00 Box model and global styles 00:06:00 Manipulating inline styles with the DOM 00:04:00 Moving the player by changing its state 00:06:00 DOM keyboard event listeners 00:07:00 Mapping and filtering DOM events data 00:04:00 Can the player move? - Tech approach 00:03:00 Preventing overlapping DOM elements 00:12:00 Prepare interactive frames 00:08:00 Resetting className and adding interactive frames on DOM events 00:06:00 Update frames without moving the element on DOM events 00:03:00 Unit 12: Interactive DOM, breaking walls Adding random DOM elements 00:06:00 DOM events when pressing the space key 00:05:00 Creating elements on DOM events 00:05:00 z-index manifest 00:04:00 Dynamic element IDs with the DOM 00:07:00 Interacting with other elements using the state model 00:06:00 Remove surrounding walls 00:04:00 Unit 13: Portal to exit the game Adding the portal to the screen 00:07:00 Random elements on the screen 00:05:00 Grouping inline CSS DOM updates 00:03:00 Exiting the game, when two elements cross paths 00:04:00 Challenge, your turn to build the exit screen 00:03:00 Challenge solution, my turn to build the exit screen 00:04:00 Hiding the portal behind a wall 00:05:00 Removing DOM event listeners 00:04:00 Unit 14: Animating all the things Rendering the splash screen 00:04:00 Swapping screens 00:02:00 Animating the portal 00:04:00 CSS kit - animations 00:03:00 Request animation frame and delaying animations 00:09:00 Animating with a parent css class 00:03:00 Old TV effect 00:02:00 Animating with delayed animation 00:11:00 Optimizing frames 00:03:00 Final frame optimizations 00:04:00 Unit 15: DOM Sound effects Dynamic audio elements 00:07:00 Interactive sound effects with DOM events 00:04:00 Delayed audio effects with callbacks and DOM events 00:04:00 Final lecture, final sound effect! exiting the game 00:03:00

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Course Overview Gain the capability to multi-skill in programming and make a full stack web GIS application by taking this course on creating Machine Learning for Predictive Maps in Python and Leaflet Level 4. This expertly compiled coaching will show you how to use the tools and functions of the most widely-used programming language in the world to make powerful forecasting applications. This comprehensive Python tutorial is the perfect way to learn how to create impressive and practical pieces of IT work. From basic installations to setting up unique predictive environments, you will be shown how to produce complex forecasting routines right from scratch and for all manner of uses. By mastering the foundational skills and knowledge of machine learning on the most popular platform, you will raise your profile to a higher level for employment opportunities and organisational value in the IT sector.  This best selling Machine Learning for Predictive Maps in Python and Leaflet Level 4 has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning for Predictive Maps in Python and Leaflet Level 4 is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Machine Learning for Predictive Maps in Python and Leaflet Level 4 is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Machine Learning for Predictive Maps in Python and Leaflet Level 4 is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Machine Learning for Predictive Maps in Python and Leaflet Level 4, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning for Predictive Maps in Python and Leaflet Level 4 will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Machine Learning for Predictive Maps in Python and Leaflet Level 4 to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

Duration 1 Days 6 CPD hours This course is intended for Learners taking this course are interested in employee experiences or Microsoft Viva and want to learn how to assess, plan, strategize, design, and manage digital employee experiences that use Microsoft Viva, Microsoft Teams, SharePoint, and Power Platform. A learner in this role will collaborate with multiple teams to scope, design, and implement new digital employee experiences, such as onboarding, career and skill development, rewards and recognition, employee wellbeing, and employee retention. Learners should have a foundational understanding of Microsoft technologies, including Microsoft 365, Teams, SharePoint, and a deep understanding of Microsoft Viva features and capabilities. They may have experience in one or more of the following disciplines: human resources, people development, change management, information technology, or culture development. Overview By the end of this module, you'll be able to: Evaluate existing systems and identify requirements Identify stakeholders and users Recommend employee experience solutions and strategies Describe the four experience areas of Connection, Growth, Purpose, and Insights supported by Viva. Explain what Microsoft Viva apps are. Identify resources needed to set up each Viva app. Create an adoption plan to use Viva to solve business scenarios for the four employee experience areas of Connection, Insight, Purpose, and Growth. Describe the main features of Viva Connections List technical requirements/prerequisites for Viva Connections implementation Explain the differences between desktop and mobile experiences Identify 2-3 business use cases for Viva Connections Identify key stakeholders for the deployment of Viva Connections Align and prioritize scenarios for Viva Connections Plan and design for the Dashboard, the Feed, and Resources by scenarios and audiences Consider how your organization will scale adoption Assess your organization's existing learning experiences. Plan and strategize for Viva Learning. Coordinate the implementation of Viva Learning. Recommend an adoption strategy for Viva Learning. In this course, you'll learn how to bring people together to create an optimal employee experience that enables your organization to improve productivity, develop empathetic leadership, and transform how employees feel about their work. In your organization today, are people being treated well, or are their needs neglected? Are your teams aligned on goals with a sense of purpose? Are you driving the business outcomes that you need? The Microsoft Viva employee experience platform provides the infrastructure to create the culture of trust, collaboration, well-being, and active listening that you envision. This training course will provide Microsoft Employee Experience Platform Specialists with a comprehensive overview of Microsoft Viva, as well as Microsoft 365, Teams, and SharePoint. It will cover how to identify requirements for designing experiences for employee onboarding, career and skill development, rewards and recognition, compensation and benefits, employee wellbeing, and employee retention. It will also cover how to design solutions to meet these requirements, and how to collaborate with senior executive leadership, human resources, IT, adoption and change management, and learning and organizational development departments. Finally, it will cover how to continuously improve employee experiences based on data-driven insights and feedback. Design digital employee experiences Introduction Case study - Tailwind Traders Evaluate current employee experiences Consider employee privacy and data requirements Assemble business stakeholders and define goals Explore Viva experience areas Understand Viva licensing Knowledge check Summary and resources Introduction to the Microsoft Viva suite Introduction to Microsoft Viva Understand Viva apps Get started with Microsoft Viva Use Viva to keep everyone informed, included, and inspired Use Viva to get actionable insights to foster well-being and productivity Use Viva to align people's work to team and organization goals Use Viva to help employees learn, grow, and succeed Knowledge check Summary Introduction to Viva Connections Introduction What do users experience? When to use Viva Connections? What technical requirements must be met to deploy Viva Connections? Knowledge check Summary Plan for Viva Connections Introduction Build your team and meet requirements Analyze tasks and scenarios for Viva Connections Plan for Viva Connections Dashboard, Feed and Resources Plan to announce, launch, and scale adoption Knowledge check Summary Design skilling and growth experiences with Viva Learning Introduction Case study - Tailwind Traders Plan for Viva Learning Assemble Viva Learning admins and stakeholders Understand content sources with Viva Learning Coordinate setup and configuration of Viva Learning Develop adoption strategies for Viva Learning Develop an org-wide learning culture Knowledge check Summary and resources Additional course details: Nexus Humans MS-080T00: Employee Experience Platform Specialist 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 MS-080T00: Employee Experience Platform Specialist 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.

Description Register on the Deep Learning & Neural Networks Python - Keras today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a certificate as proof of your course completion. The Deep Learning & Neural Networks Python - Keras course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablets, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With This Course Receive a digital certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment You need to attend an assessment right after the completion of this course to evaluate your progression. For passing the assessment, you need to score at least 60%. After submitting your assessment, you will get feedback from our experts immediately. Who Is This Course For The course is ideal for those who already work in this sector or are aspiring professionals. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.   Course Content Course Introduction And Table Of Contents Course Introduction and Table of Contents 00:11:00 Deep Learning Overview Deep Learning Overview - Theory Session - Part 1 00:06:00 Deep Learning Overview - Theory Session - Part 2 00:07:00 Choosing Between ML Or DL For The Next AI Project - Quick Theory Session Choosing Between ML or DL for the next AI project - Quick Theory Session 00:09:00 Preparing Your Computer Preparing Your Computer - Part 1 00:07:00 Preparing Your Computer - Part 2 00:06:00 Python Basics Python Basics - Assignment 00:09:00 Python Basics - Flow Control 00:09:00 Python Basics - Functions 00:04:00 Python Basics - Data Structures 00:12:00 Theano Library Installation And Sample Program To Test Theano Library Installation and Sample Program to Test 00:11:00 TensorFlow Library Installation And Sample Program To Test TensorFlow library Installation and Sample Program to Test 00:09:00 Keras Installation And Switching Theano And TensorFlow Backends Keras Installation and Switching Theano and TensorFlow Backends 00:10:00 Explaining Multi-Layer Perceptron Concepts Explaining Multi-Layer Perceptron Concepts 00:03:00 Explaining Neural Networks Steps And Terminology Explaining Neural Networks Steps and Terminology 00:10:00 First Neural Network With Keras - Understanding Pima Indian Diabetes Dataset First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset 00:07:00 Explaining Training And Evaluation Concepts Explaining Training and Evaluation Concepts 00:11:00 Pima Indian Model - Steps Explained Pima Indian Model - Steps Explained - Part 1 00:09:00 Pima Indian Model - Steps Explained - Part 2 00:07:00 Coding The Pima Indian Model Coding the Pima Indian Model - Part 1 00:11:00 Coding the Pima Indian Model - Part 2 00:09:00 Pima Indian Model - Performance Evaluation Pima Indian Model - Performance Evaluation - Automatic Verification 00:06:00 Pima Indian Model - Performance Evaluation - Manual Verification 00:08:00 Pima Indian Model - Performance Evaluation - K-Fold Validation - Keras Pima Indian Model - Performance Evaluation - k-fold Validation - Keras 00:10:00 Pima Indian Model - Performance Evaluation - Hyper Parameters Pima Indian Model - Performance Evaluation - Hyper Parameters 00:12:00 Understanding Iris Flower Multi-Class Dataset Understanding Iris Flower Multi-Class Dataset 00:08:00 Developing The Iris Flower Multi-Class Model Developing the Iris Flower Multi-Class Model - Part 1 00:09:00 Developing the Iris Flower Multi-Class Model - Part 2 00:06:00 Developing the Iris Flower Multi-Class Model - Part 3 00:09:00 Understanding The Sonar Returns Dataset Understanding the Sonar Returns Dataset 00:07:00 Developing The Sonar Returns Model Developing the Sonar Returns Model 00:10:00 Sonar Performance Improvement - Data Preparation - Standardization Sonar Performance Improvement - Data Preparation - Standardization 00:15:00 Sonar Performance Improvement - Data Preparation - Standardization Sonar Performance Improvement - Layer Tuning for Smaller Network 00:07:00 Sonar Performance Improvement - Layer Tuning For Larger Network Sonar Performance Improvement - Layer Tuning for Larger Network 00:06:00 Understanding The Boston Housing Regression Dataset Understanding the Boston Housing Regression Dataset 00:07:00 Developing The Boston Housing Baseline Model Developing the Boston Housing Baseline Model 00:08:00 Boston Performance Improvement By Standardization Boston Performance Improvement by Standardization 00:07:00 Boston Performance Improvement By Deeper Network Tuning Boston Performance Improvement by Deeper Network Tuning 00:05:00 Boston Performance Improvement By Wider Network Tuning Boston Performance Improvement by Wider Network Tuning 00:04:00 Save & Load The Trained Model As JSON File (Pima Indian Dataset) Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 1 00:09:00 Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 2 00:08:00 Save And Load Model As YAML File - Pima Indian Dataset Save and Load Model as YAML File - Pima Indian Dataset 00:05:00 Load And Predict Using The Pima Indian Diabetes Model Load and Predict using the Pima Indian Diabetes Model 00:09:00 Load And Predict Using The Iris Flower Multi-Class Model Load and Predict using the Iris Flower Multi-Class Model 00:08:00 Load And Predict Using The Sonar Returns Model Load and Predict using the Sonar Returns Model 00:10:00 Load And Predict Using The Boston Housing Regression Model Load and Predict using the Boston Housing Regression Model 00:08:00 An Introduction To Checkpointing An Introduction to Checkpointing 00:06:00 Checkpoint Neural Network Model Improvements Checkpoint Neural Network Model Improvements 00:10:00 Checkpoint Neural Network Best Model Checkpoint Neural Network Best Model 00:04:00 Loading The Saved Checkpoint Loading the Saved Checkpoint 00:05:00 Plotting Model Behavior History Plotting Model Behavior History - Introduction 00:06:00 Plotting Model Behavior History - Coding 00:08:00 Dropout Regularization - Visible Layer Dropout Regularization - Visible Layer - Part 1 00:11:00 Dropout Regularization - Visible Layer - Part 2 00:06:00 Dropout Regularization - Hidden Layer Dropout Regularization - Hidden Layer 00:06:00 Learning Rate Schedule Using Ionosphere Dataset - Intro Learning Rate Schedule using Ionosphere Dataset 00:06:00 Time Based Learning Rate Schedule Time Based Learning Rate Schedule - Part 1 00:07:00 Time Based Learning Rate Schedule - Part 2 00:12:00 Drop Based Learning Rate Schedule Drop Based Learning Rate Schedule - Part 1 00:07:00 Drop Based Learning Rate Schedule - Part 2 00:08:00 Convolutional Neural Networks - Introduction Convolutional Neural Networks - Part 1 00:11:00 Convolutional Neural Networks - Part 2 00:06:00 MNIST Handwritten Digit Recognition Dataset Introduction to MNIST Handwritten Digit Recognition Dataset 00:06:00 Downloading and Testing MNIST Handwritten Digit Recognition Dataset 00:10:00 MNIST Handwritten Digit Recognition Dataset MNIST Multi-Layer Perceptron Model Development - Part 1 00:11:00 MNIST Multi-Layer Perceptron Model Development - Part 2 00:06:00 Convolutional Neural Network Model Using MNIST Convolutional Neural Network Model using MNIST - Part 1 00:13:00 Convolutional Neural Network Model using MNIST - Part 2 00:12:00 Large CNN Using MNIST Large CNN using MNIST 00:09:00 Load And Predict Using The MNIST CNN Model Load and Predict using the MNIST CNN Model 00:14:00 Introduction To Image Augmentation Using Keras Introduction to Image Augmentation using Keras 00:11:00 Augmentation Using Sample Wise Standardization Augmentation using Sample Wise Standardization 00:10:00 Augmentation Using Feature Wise Standardization & ZCA Whitening Augmentation using Feature Wise Standardization & ZCA Whitening 00:04:00 Augmentation Using Rotation And Flipping Augmentation using Rotation and Flipping 00:04:00 Saving Augmentation Saving Augmentation 00:05:00 CIFAR-10 Object Recognition Dataset - Understanding And Loading CIFAR-10 Object Recognition Dataset - Understanding and Loading 00:12:00 Simple CNN Using CIFAR-10 Dataset Simple CNN using CIFAR-10 Dataset - Part 1 00:09:00 Simple CNN using CIFAR-10 Dataset - Part 2 00:06:00 Simple CNN using CIFAR-10 Dataset - Part 3 00:08:00 Train And Save CIFAR-10 Model Train and Save CIFAR-10 Model 00:08:00 Load And Predict Using CIFAR-10 CNN Model Load and Predict using CIFAR-10 CNN Model 00:16:00 RECOMENDED READINGS Recomended Readings 00:00:00