14623 Ear courses

About this Virtual Instructor Led Training (VILT) This Virtual Instructor Led Training (VILT) course presents advanced methodologies that implement demand response and energy conservation programs in light of the integration of new technologies, regulatory changes and the accelerated penetration of renewable energy resources. This VILT course provides examples and case studies from North American and European jurisdictions covering the operational flexibilities on the demand side including requirements for new building codes to achieve zero net energy. The course describes a public agency's goals and objectives for conserving and otherwise reducing energy consumption and managing its demand for energy. This course presents the demand response implemented for economics and system security such as system balancing and relieving transmission congestion, or for system adequacy. The course also presents the principal attributes of conservation programs and the associated success criteria. In a system with increased penetration of renewable resources, demand response provides flexibility to system operators, helping them to maintain the reliability and the security of supply. Demand response is presented as a competitive alternative to additional power sources, enhancing competition and liquidity in electricity markets. The unique characteristics are discussed from a local, consumer centric and also from a system perspective bringing to life the ever changing paradigm for delivery energy to customers. Interoperability aspects and standards are discussed, as well as the consumer centric paradigm of Transactive Energy with IOT enabled flexibilities at system level, distribution networks and microgrids. The VILT course introduces the blockchain as a new line of defense against cyber threats and its increasing application in P2P transactions and renewable certificates. Our trainer's industry experience spans three decades with one of the largest Canadian utilities where she led or contributed to large operational studies and energy policies and decades of work with IEEE, NSERC and CIGRE. Our key expert also approaches to the cross sectional, interdisciplinary state of the art methodologies brings real life experience of recent industry developments. Training Objectives Innovative Digital Technologies How systems Facilitate Operational Flexibility on the Demand Side The Ecosystem of Demand Side Management Programs Advanced Machine Learning techniques with examples from CAISO Regulatory Policy Context and how to reduce regulatory barriers Industry Examples from NERC and ENTSO Relevant Industry standards: IEEE and IEC Manage Congestion with Distributed Operational Flexibilities: Grid to Distribution Controls; examples from NERC (NA) and ENTSO (Europe) Grid solutions with IEC 61850 communication protocols Decentralized grid controls The New Grid with accelerated V2G and Microgrids How DSM is and will be applied in Your System: Examples and discussions Target Audience Regulators and government agencies advising on public energy conservation programs All professionals interested in expanding their expertise, or advancing their career, or take on management and leadership roles in the rapidly evolving energy sector Energy professionals implementing demand side management, particularly in power systems with increased renewable penetration, to allow the much needed operational flexibility paramount to maintaining the reliability and stability of the power system and in the same time offering all classes of customers flexible and economical choices Any utility professional interested in understanding the new developments in the power industry Course Level Basic or Foundation Training Methods The VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, with 2 x 10 minutes break per day, including time for lectures, discussion, quizzes and short classroom exercises. Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total). Trainer Your first expert course leader is a Utility Executive with extensive global experience in power system operation and planning, energy markets, enterprise risk and regulatory oversight. She consults on energy markets integrating renewable resources from planning to operation. She led complex projects in operations and conducted long term planning studies to support planning and operational reliability standards. Specializing in Smart Grids, Operational flexibilities, Renewable generation, Reliability, Financial Engineering, Energy Markets and Power System Integration, she was recently engaged by the Inter-American Development Bank/MHI in Guyana. She was the Operations Expert in the regulatory assessment in Oman. She is a registered member of the Professional Engineers of Ontario, Canada. She is also a contributing member to the IEEE Standards Association, WG Blockchain P2418.5. With over 25 years with Ontario Power Generation (Revenue $1.2 Billion CAD, I/S 16 GW), she served as Canadian representative in CIGRE, committee member in NSERC (Natural Sciences and Engineering Research Council of Canada), and Senior Member IEEE and Elsevier since the 90ties. Our key expert chaired international conferences, lectured on several continents, published a book on Reliability and Security of Nuclear Power Plants, contributed to IEEE and PMAPS and published in the Ontario Journal for Public Policy, Canada. She delivered seminars organized by the Power Engineering Society, IEEE plus seminars to power companies worldwide, including Oman, Thailand, Saudi Arabia, Malaysia, Indonesia, Portugal, South Africa, Japan, Romania, and Guyana. Your second expert course leader is the co-founder and Director of Research at Xesto Inc. Xesto is a spatial computing AI startup based in Toronto, Canada and it has been voted as Toronto's Best Tech Startup 2019 and was named one of the top 10 'Canadian AI Startups to Watch' as well as one of 6th International finalists for the VW Siemens Startup Challenge, resulting in a partnership. His latest app Xesto-Fit demonstrates how advanced AI and machine learning is applied to the e-commerce industry, as a result of which Xesto has been recently featured in TechCrunch. He specializes in both applied and theoretical machine learning and has extensive experience in both industrial and academic research. He is specialized in Artificial Intelligence with multiple industrial applications. At Xesto, he leads projects that focus on applying cutting edge research at the intersection of spatial analysis, differential geometry, optimization of deep neural networks, and statistics to build scalable rigorous and real time performing systems that will change the way humans interact with technology. In addition, he is a Ph.D candidate in the Mathematics department at UofT, focusing on applied mathematics. His academic research interests are in applying advanced mathematical methods to the computational and statistical sciences. He earned a Bachelor's and MSc in Mathematics, both at the University of Toronto. Having presented at research seminars as well as instructing engineers on various levels, he has the ability to distill advanced theoretical concept to diverse audiences on all levels. In addition to research, our key expert is also an avid traveler and plays the violin. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information about post training coaching support and fees applicable for this. Accreditions And Affliations

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Hypnotherapy Training Course

Earned Value Management: On-Demand: On-Demand Earned Value Management (EVM) incorporates a set of proven practices appropriate for project or program management methodologies. These include integration of program scope, schedule, and cost objectives, establishment of a baseline plan for accomplishment of program objectives and use of earned value techniques for performance measurement during the execution of a program. Earned Value Management (EVM) incorporates a set of proven practices appropriate for project or program management methodologies. These include integration of program scope, schedule, and cost objectives, establishment of a baseline plan for accomplishment of program objectives and use of earned value techniques for performance measurement during the execution of a program. EVM provides a solid platform for risk identification, corrective actions, and management re-planning as may be required over the life of a project or program. The course emphasis is on the latest EVM principles and concepts in accordance with changes and guidelines for Earned Value Management in The Guide to the Project Management Body of Knowledge (PMBOK® Guide) and The Practice Standard for Earned Value Management published by the Project Management Institute. What you Will Learn You'll learn how to: Develop a project baseline, using an effective WBS Record actual project performance Calculate EVM measures Evaluate project performance based on EVM measures Respond to project variances Integrate EVM and risk management Determine how EVM will add value to your organization Develop an EVM implementation plan for your organization Getting Started Introductions Course structure Course goals and objectives Expectations Foundation Concepts Introduction to Earned Value Management (EVM) Benefits of EVM EVM Process Overview Applications of EVM Creating a Work Breakdown Structure Reviewing WBS concepts Reviewing WBS development process (decomposition) Using a WBS to support EVM Building a Project Baseline Defining a project baseline Developing a project baseline Using a project baseline Recording Actuals Recording actuals overview Collecting data for actual project performance Determining earned value - various methods EVM Performance Measures Using current status measures Using forecasting measures Analyzing EVM measures EVM and Risk Management Integrating EVM and Risk Management Using EVM measures in the risk register Exploring how EVM can facilitate reserves management Drawing down contingency reserves Responding to Variances Introduction to variances Process for responding to variances Response options Reporting Project Performance EVM reporting overview Meeting EVM reporting needs Addressing EVM reporting challenges Implementing an EVMS Defining EVMS requirements EVM for Agile projects Tailoring the EVMS Summary and Next Steps Review of content Review of objectives / expectations Personal action plan

Level 5 Diploma in Education and Training is regulated by Ofqual and prepares trainee teachers to teach in a wide range of contexts, adult qualifications. Level 5: Diploma in Education and Training DET or DTLLS course is a QCF qualification which is full 120 QCF Credit value. Formerly this course used to called Level 5 Diploma in Teaching in the Life Long Learning (DTLLS) course.  ABOUT THIS QUALIFICATION: Level 5 Diploma in Education and Training DET/DTLLS Course is suitable for teachers, trainers and tutors who wish to work or already working in further education, Colleges, adult and community learning or work-based training within public, private, voluntary or community organisations. It prepares trainee teachers to teach in a wide range of contexts and requires observation and assessment of practice. All candidates, whether pre-service (Currently not working) or in-service (Currently working) must have access to the teaching (volunteer teaching, paid / unpaid teaching, part time / full time teaching). This qualification is suitable for those delivering education and training in any learning environment. COURSE OUTLINE: Chapter 1: Developing teaching, learning and assessment in Education and Training. Chapter 2: Teaching, learning and assessment in education and training. Chapter 3: Theories, Principles and models in education and Training. Chapter 4: Wider professional practice and development in Education and Training. Chapter 5: Action Research. Chapter 6: Developing, using and organising resources in a specialist area. Chapter 7: Managing behaviours in an earning environment. Chapter 8: Understanding inclusive practice. LEARNING OUTCOMES: • Teaching, learning and assessment in education and training • Theories, principles and models of education and training • Developing teaching learning and assessment in education and training • Wider professional practice and development in education and training WHO ARE THE QUALIFICATIONS FOR: For candidates who work or who want to work as teachers/trainers in the further education and skills sector. For candidates who have just started a teaching/training role. For teachers/trainers who are seeking career progression in their area of work. For candidates who work with learners on a one-to- one basis. For candidates who teach in industry. For candidates who have already achieved some Learning and Development units that can be carried forward into this qualification. For candidates who are assessors and wish to achieve a teaching/training qualification. For candidates with a CET who wish to top this up to a full level 5 teaching qualification applicable to the qualified teaching pay scales across FE institutes and apply for QTLS through the Society for Education and Training. STUDY METHOD: You can complete this course either through Distance Learning / Online or Classroom based. Level 5 DET distance learning course is done at your own pace with a Tutor’s support & guidance. All necessary materials will be sent to you by royal mail special delivery once you book the course. Once you book this course, you will be given up to 12 months to complete the assignments. COURSE ASSESSMENT: There is no formal examination is required, but at the end of the course you need to submit a portfolio assignment. HOW MUCH THIS COURSE COST? Level 5 DTLLS/DET course will cost for Online Distance Learning £1499.99, for Webinar Classroom based Course £1549.99. There is no any hidden fess/cost.

Certified Scrum Professional®-ScrumMaster® (CSP®-SM): In-House Training Certified Scrum Professionals challenge their teams to improve the way Scrum and Agile principles are applied. They have demonstrated experience, documented training, and proven knowledge in Scrum. Are you ready to take your knowledge and skillset in your role as Scrum Master to the next level? If so, it's time to elevate your career further by earning the Certified Scrum Professional®-ScrumMaster (CSP®-SM) certification. What you will Learn Learn to find practical solutions and improve your implementation of Scrum in the workplace. Aside from the pride gained and earning potential of attaining CSP® level, you can also: Attend exclusive CSP® events with other leaders in Scrum and Agile Attract more recruiters and command a higher rate of pay Establish a gateway and milestone toward becoming CST®, CEC, or CTC Receive a free premium subscription to the world's largest Agile assessment and continuous improvement platform, Comparative Agility®

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).