The Project Management Odyssey Project Management is composed of many elements. So many, in fact, that it is hard at times to get the big picture. There are more than 100,000 books on Amazon under Project Management. There are three main certification bodies and the latest version of the US standard, PMI's PMBOK® Guide is composed of 5 process groups, 10 knowledge areas and 47 steps. The complexity needs to be simplified. For this presentation, we have gone back to one of the original stories by Homer - the Odyssey. Odysseus and his men were tired after 10 years of fighting and looking forward the journey home. This idea of a journey is at the heart of each and every project. A journey to somewhere else, another place. A beginning, a middle, and an end. We will use this journey to illustrate the correct way to approach any project. In doing so we hope to assist you in retaining the key ingredients for doing projects better in your equally challenging environment. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies.
The Project Management Odyssey Project Management is composed of many elements. So many, in fact, that it is hard at times to get the big picture. There are more than 100,000 books on Amazon under Project Management. There are three main certification bodies and the latest version of the US standard, PMI's PMBOK® Guide is composed of 5 process groups, 10 knowledge areas and 47 steps. The complexity needs to be simplified. For this presentation, we have gone back to one of the original stories by Homer - the Odyssey. Odysseus and his men were tired after 10 years of fighting and looking forward the journey home. This idea of a journey is at the heart of each and every project. A journey to somewhere else, another place. A beginning, a middle, and an end. We will use this journey to illustrate the correct way to approach any project. In doing so we hope to assist you in retaining the key ingredients for doing projects better in your equally challenging environment. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies.
Let's Help Them to Help Themselves - Through Project Management This video illustrates the application of project management to a humanitarian program created to improve childhood conditions in one of the poorest countries in the world and teach children solidarity and respect for others. All of this is achieved through the diffusion of the PM methodologies, both in Congo and in Italy. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.
Accredited Project Management Diploma for Project Manager Embark on a comprehensive journey to mastery in project management with our Accredited Project Management Diploma. This course is meticulously crafted to equip you with a profound understanding of essential project management aspects, including setting up a Project Management Office, defining project goals and scope, conducting risk assessments, and effectively allocating resources. Learning Outcomes: Understand the role and structure of a Project Management Office in project management. Learn the process of defining clear and achievable project goals in project management. Master the art of conducting thorough risk assessments in project management. Develop the skill to define and manage project scope effectively in project management. Gain expertise in allocating and managing project resources efficiently in project management. More Benefits: LIFETIME access Device Compatibility Free Workplace Management Toolkit Key Modules from Accredited Project Management Diploma: Project Management Office: Explore the functions, benefits, and best practices for establishing and running a Project Management Office. Defining Project Goals: Learn how to set clear, measurable, and attainable goals for successful project outcomes in project management. Doing Risk Assessment: Master the techniques of identifying, analyzing, and mitigating risks in project management. Defining Project Scope: Understand the importance of accurately defining project scope to ensure project objectives are met within constraints in project management. Allocating Project Resources: Gain insights into effective resource allocation strategies, including budgeting, scheduling, and resource optimization in project management.
Course Overview: Search Engine Optimisation - CPD Certified In today's digital landscape, mastering Search Engine Optimization (SEO) is essential for anyone looking to succeed in online marketing and digital presence management. The 'Search Engine Optimisation - CPD Certified' course offers a comprehensive learning experience designed to equip participants with the knowledge and skills needed to navigate the intricacies of SEO effectively. Module 1: Search Engine Algorithms Understanding the fundamental principles behind search engine algorithms is crucial for optimizing online content. Module 1 delves into the intricate workings of search engine algorithms, exploring how they determine website rankings and visibility in search results. Participants will learn about the key factors that influence algorithmic decisions and strategies for aligning content with search engine requirements. Module 2: Search Engine Ranking and Optimization Module 2 focuses on practical techniques for improving search engine rankings and optimizing web content for maximum visibility. Participants will explore the importance of keyword research, on-page optimization, link building, and other SEO tactics aimed at enhancing website performance in search engine results pages (SERPs). Module 3: User Experience and Digital Marketing Integration User experience (UX) plays a pivotal role in SEO success and overall digital marketing strategy. Module 3 examines the relationship between user experience and SEO, emphasizing the importance of responsive design, site speed, and intuitive navigation. Additionally, participants will learn how to integrate SEO practices seamlessly into broader digital marketing initiatives to drive engagement and conversions. Module 4: Web Analytics and Ethical Considerations Module 4 introduces participants to web analytics tools and methodologies for tracking website performance and measuring the effectiveness of SEO efforts. Moreover, ethical considerations in SEO practices, such as black hat techniques and spammy tactics, will be discussed to ensure participants understand the importance of maintaining integrity and credibility in their SEO strategies. Module 5: Future Trends and Storytelling in Digital Marketing Anticipating future trends and adapting to evolving digital landscapes are essential for sustainable SEO success. In Module 5, participants will explore emerging trends in search engine optimization, such as voice search, artificial intelligence, and mobile optimization. Additionally, the module will emphasize the role of storytelling in digital marketing and its impact on audience engagement and brand visibility. Certification and Continuing Professional Development (CPD) Upon successful completion of the course, participants will receive a CPD certified certificate, recognizing their proficiency in Search Engine Optimization. This certification not only validates their expertise but also demonstrates their commitment to continuous learning and professional development in the field of digital marketing. In conclusion, the 'Search Engine Optimisation - CPD Certified' course offers a comprehensive curriculum designed to empower participants with the knowledge, skills, and ethical considerations necessary to succeed in the dynamic world of search engine optimization and digital marketing. Whether you're a seasoned marketing professional or a newcomer to the field, this course provides invaluable insights and practical strategies for driving online visibility and achieving marketing objectives in today's competitive digital landscape. Course Curriculum Module 1_ Search Engine Algorithms Search Engine Algorithms 00:00 Module 2_ Search Engine Ranking and Optimisation Search Engine Ranking and Optimisation 00:00 Module 3_ User Experience and Digital Marketing Integration User Experience and Digital Marketing Integration 00:00 Module 4_ Web Analytics and Ethical Considerations Web Analytics and Ethical Considerations 00:00 Module 5_ Future Trends and Storytelling in Digital Marketing Future Trends and Storytelling in Digital Marketing 00:00
Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brandnew version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative dversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions
Duration 4 Days 24 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm ? YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions
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