572 Courses

Duration 1 Days 6 CPD hours This course is intended for This course is designed for business leaders and decision makers, including C-level executives, project and product managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who have a vested interest in the representation of ethical values in technology solutions. Other individuals who want to know more about data ethics are also candidates for this course. This course is also designed to assist learners in preparing for the CertNexus DEBIZ™ (Exam DEB-110) credential. The power of extracting value from data utilizing Artificial Intelligence, Data Science and Machine Learning exposes the learning differences between humans and machines. Humans can apply ethical principles throughout the decision-making process to avoid discrimination, societal harm, and marginalization to maintain and even enhance acceptable norms. Machines make decisions autonomously. So how do we train them to apply ethical principles as they learn from decisions they make? This course provides business professionals and consumers of technology core concepts of ethical principles, how they can be applied to emerging data driven technologies and the impact to an organization which ignores ethical use of technology. Introduction to Data Ethics Defining Data Ethics The Case for Data Ethics Identifying Ethical Issues Improving Ethical Data Practices Ethical Principles Ethical Frameworks Data Privacy Accountability Transparency and Explainability Human-Centered Values and Fairness Inclusive Growth, Sustainable Development, and Well-Being Applying Ethical Principles to Emerging Technology Improving Ethical Data Practices Sources of Ethical Risk Mitigating Bias Mitigating Discrimination Safety and Security Mitigating Negative Outputs Data Surveillance Assessing Risk Ethical Risks in sharing data Applying professional critical judgement Business Considerations Data Legislation Impact of Social and Behavioral Effects Trustworthiness Impact on Business Reputation Organizational Values and the Data Value Chain Building a Data Ethics Culture/Code of Ethics Balancing organizational goals with Ethical Practice Additional course details: Nexus Humans CertNexus Data Ethics for Business Professionals (DEBIZ) 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 CertNexus Data Ethics for Business Professionals (DEBIZ) 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 This course is designed for network and software engineers who hold the following job roles: Network administrators Network operators Overview After taking this course, you should be able to: Explain the benefits of using Cisco DNA Center in a traditional, enterprise network Explain at a detailed level the Cisco DNA Center Assurance system architecture, functional components, features, and data-processing concepts Explain the health scores, metrics, and strategies that you use for monitoring network devices, clients, and applications with Cisco DNA Assurance Describe how Cisco DNA Center Assurance analyzes the streaming telemetry and collected data, correlates the data, performs root cause analysis, and displays detected issues, insights, and trends Describe the Cisco DNA Center Assurance troubleshooting tools, mechanisms, strategies, and scenarios to proactively detect and resolve wireless network, client, and application issues and pinpoint the root cause Deploy and configure Cisco DNA Center to use Assurance features for monitoring and troubleshooting network devices, clients, and applications The Leveraging Cisco Intent-Based Networking DNA Assurance (DNAAS) v2.1 course provides you with the skills to monitor and troubleshoot a traditional brownfield network infrastructure by using Cisco© Digital Network Architecture (Cisco DNA?) Assurance. The course focuses on highlighting issues rather than on monitoring data. The advanced artificial intelligence and machine learning features within Cisco DNA Assurance enable you to isolate the root cause of a problem and to take appropriate actions to quickly resolve issues. Cisco DNA Assurance can be used to perform the work of a Level 3 support engineer. Course Outline Introducing Cisco DNA Center Assurance Monitoring Health and Performance with Cisco DNA Center Assurance Troubleshooting Issues, Observing Insights and Trends Troubleshooting Wireless Issues with Cisco DNA Center Assurance Tools Additional course details: Nexus Humans Cisco Leveraging Cisco Intent-Based Networking DNA Assurance (DNAAS) v2.1 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 Cisco Leveraging Cisco Intent-Based Networking DNA Assurance (DNAAS) v2.1 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.

Overview This Deep Learning & Neural Networks Python - Keras: For Dummies course will unlock your full potential and will show you how to excel in a career in Deep Learning & Neural Networks Python - Keras: For Dummies. So upskill now and reach your full potential. Everything you need to get started in Deep Learning & Neural Networks Python - Keras: For Dummies is available in this course. Learning and progressing are the hallmarks of personal development. This Deep Learning & Neural Networks Python - Keras: For Dummies will quickly teach you the must-have skills needed to start in the relevant industry. In This Deep Learning & Neural Networks Python - Keras: For Dummies Course, You Will: Learn strategies to boost your workplace efficiency. Hone your Deep Learning & Neural Networks Python - Keras: For Dummies skills to help you advance your career. Acquire a comprehensive understanding of various Deep Learning & Neural Networks Python - Keras: For Dummies topics and tips from industry experts. Learn in-demand Deep Learning & Neural Networks Python - Keras: For Dummies skills that are in high demand among UK employers, which will help you to kickstart your career. This Deep Learning & Neural Networks Python - Keras: For Dummies course covers everything you must know to stand against the tough competition in the Deep Learning & Neural Networks Python - Keras: For Dummies field.  The future is truly yours to seize with this Deep Learning & Neural Networks Python - Keras: For Dummies. Enrol today and complete the course to achieve a Deep Learning & Neural Networks Python - Keras: For Dummies certificate that can change your professional career forever. Additional Perks of Buying a Course From Institute of Mental Health Study online - whenever and wherever you want. One-to-one support from a dedicated tutor throughout your course. Certificate immediately upon course completion 100% Money back guarantee Exclusive discounts on your next course purchase from Institute of Mental Health Enrolling in the Deep Learning & Neural Networks Python - Keras: For Dummies course can assist you in getting into your desired career quicker than you ever imagined. So without further ado, start now. Process of Evaluation After studying the Deep Learning & Neural Networks Python - Keras: For Dummies course, your skills and knowledge will be tested with a MCQ exam or assignment. You must get a score of 60% to pass the test and get your certificate.  Certificate of Achievement Upon successfully completing the Deep Learning & Neural Networks Python - Keras: For Dummies course, you will get your CPD accredited digital certificate immediately. And you can also claim the hardcopy certificate completely free of charge. All you have to do is pay a shipping charge of just £3.99. Who Is This Course for? This Deep Learning & Neural Networks Python - Keras: For Dummies is suitable for anyone aspiring to start a career in Deep Learning & Neural Networks Python - Keras: For Dummies; even if you are new to this and have no prior knowledge on Deep Learning & Neural Networks Python - Keras: For Dummies, this course is going to be very easy for you to understand.  And if you are already working in the Deep Learning & Neural Networks Python - Keras: For Dummies field, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  Taking this Deep Learning & Neural Networks Python - Keras: For Dummies course is a win-win for you in all aspects.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements This Deep Learning & Neural Networks Python - Keras: For Dummies course has no prerequisite.  You don't need any educational qualification or experience to enrol in the Deep Learning & Neural Networks Python - Keras: For Dummies course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online Deep Learning & Neural Networks Python - Keras: For Dummies course. Moreover, this course allows you to learn at your own pace while developing transferable and marketable skills. Course Curriculum 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 - Layer Tuning for Smaller Network 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 Multi-Layer Perceptron Model Development 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

Course Overview Learn about the functions of Microsoft Azure from this AZ-900 | Microsoft Azure Fundamentals Full Course course. The course will give you a clear understanding of the basics of Microsoft Azure and how you can use this cloud platform to grow and strengthen your online existence. In this AZ-900 | Microsoft Azure Fundamentals Full Course course, you will learn about the tools and basic functions of Microsoft Azure. You will be familiarized with the core Azure services, security, privacy and compliance policies. This course will teach you how you can secure your website and account using multi-factor authentication and protect data from hackers. This course will also help you to understand the supports Azure can offer you and get the best suitable one for you. Microsoft Azure is one of the most popular and safe cloud platforms. This AZ-900 | Microsoft Azure Fundamentals Full Course course will teach you the functions of Microsoft Azure from scratch. You don't need any prior knowledge or technical background to understand the lessons of this course. Learning Outcomes familiarize with the fundamentals of cloud services Understand the benefits of using cloud services Learn about the differences between capital expenditure and operational expenditure Be able to compare and contrast the IAAS, PAAS and SAAS service Learn about different cloud models and how they work Understand the core Azure architectural components Learn about the solutions you will get from Azure Learn about the management tools of Azure Get to know about the security and private privacy protocols of Microsoft Azure Understand how Azure identity services work Familiarize with role-based access control system Understand the policies and compliance standards in Azure Who is this course for? This comprehensive AZ-900 | Microsoft Azure Fundamentals Full Course is ideal for those who want to learn more about the functions of Microsoft Azure. You will learn about the application of Microsoft Azure and the career prospect from this course. Entry Requirement This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Certification After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24. PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry-leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognized accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path AZ-900 | Microsoft Azure Fundamentals Full Course is a useful qualification to possess and would be beneficial for any related profession or industry such as: Managers Managing Directors Management Executives Data Security Officers Programmers Microsoft Azure Developers Technicians Computer Operators Cloud Engineers Cloud Data Consultants Azure Consultants Data Scientists Course Introduction Introduction 00:04:00 Module 1 : Cloud Concepts What is Cloud Computing - I 00:05:00 What is Cloud Computing - II 00:06:00 Benefits of Cloud Computing 00:09:00 Key Concepts and Terminology 00:06:00 Economies of Scale 00:01:00 CapEx Vs OpEx 00:03:00 Cloud Models : What is Public Cloud 00:02:00 Cloud Models : Characteristics of Public Cloud 00:02:00 Cloud Models : What is Private Cloud 00:01:00 Cloud Models : Characteristics of Private Cloud 00:01:00 Cloud Models : Hybrid Cloud 00:01:00 Cloud Models : Characteristics of Hybrid Cloud 00:01:00 Review and What Next!! 00:01:00 What is IAAS 00:04:00 Use Cases of IAAS 00:02:00 What is PAAS ? 00:02:00 Use Cases of PAAS 00:04:00 What is SAAS ? 00:02:00 Cloud Models : Shared Responsibility Model 00:09:00 Module 2 : Core Azure Services Introduction 00:01:00 Azure Regions 00:01:00 Special Azure regions 00:01:00 Region pairs 00:01:00 Feature Availability Region Wise 00:01:00 Availability Zones 00:01:00 Availability Sets 00:02:00 What are Resource Groups? 00:02:00 Azure Resource Manager 00:01:00 What Next!! - Azure Core Services and Products 00:02:00 What is Azure Compute 00:01:00 Azure Virtual Machines - Audiocast Only 00:01:00 Azure Virtual Machines I - LAB 00:15:00 Azure Virtual Machines II - LAB 00:01:00 Azure Virtual Machines III - LAB 00:02:00 Azure Virtual Machines IV - LAB 00:04:00 Azure Virtual Machines V - LAB 00:03:00 Azure Virtual Machines VI - LAB 00:03:00 What are Containers? 00:04:00 Containers ( LAB Activity ) 00:07:00 Containers VS Virtual Machines 00:04:00 What Are Virtual Networks 00:01:00 Virtual Networks - LAB 00:15:00 Azure Load Balancer 00:01:00 VPN Gateway 00:01:00 Azure Application Gateway - I 00:02:00 Azure Application Gateway - II 00:01:00 Azure Content Delivery Networks (CDN's) 00:02:00 How CDN works ? 00:03:00 Azure CDN - Lab Activity 00:07:00 Azure Storage Services 00:01:00 Structured Data 00:01:00 Semi Structured Data 00:01:00 Unstructured Data 00:01:00 Azure Storage Account - Types 00:03:00 Azure Storage Account - Blob - Lab Activity - I 00:07:00 Azure Storage Account - Blog - Lab Activity - II 00:07:00 Azure Storage Account - Blob - Lab Activity - III 00:16:00 Azure Storage Account - Blog - Lab Activity - IV 00:09:00 Azure Storage Account - Blob - Lab Activity - V 00:04:00 Azure Storage Account - Blob - Lab Activity - VI 00:07:00 Azure Database Services 00:02:00 Azure SQL - Lab Demo 00:09:00 Azure MarketPlace 00:02:00 What is Internet of Things ( IOT ) - Intro 00:01:00 IOT Hub 00:01:00 IOT Hub Demo 00:09:00 Azure Big Data and Analytics 00:01:00 Azure SQL Data Warehouse 00:01:00 Azure HDInsights 00:01:00 Azure Data Lake Analytics 00:01:00 Machine Learning 00:02:00 Azure Machine Learning Services and Studio 00:02:00 What is Server less Computing ? 00:02:00 The concept of DevOps 00:03:00 Azure Management Tools 00:01:00 Creating Resources with Powershell - Lab Activity 00:05:00 Creating Resources with Azure CLI - Lab Activity 00:07:00 Provision Resources using Cloud Shell - Lab Activity 00:05:00 Deployment with JSON - Lab Activity 00:08:00 Azure Advisor 00:01:00 Module 2 : What did we learn 00:01:00 Module 3 Security, Privacy, Compliance and Trust What to expect in Module 3 00:01:00 Azure Firewalls 00:02:00 Azure Firewall - Lab Activity - notes 00:02:00 Azure Firewall - Lab Activity 00:19:00 Azure DDOS 00:02:00 Network Security Groups 00:03:00 Application Security Groups 00:02:00 Which Network Security Solution to choose from ? 00:04:00 AuthZ and AuthN 00:01:00 Azure Active Directory 00:02:00 Multi Factor Authentication 00:03:00 Azure Security Center 00:02:00 Azure Security center - LAB activity 00:08:00 Azure Key Vault 00:02:00 Azure Information Protection 00:02:00 Azure Advanced Threat Protection 00:04:00 What is Azure Policy 00:03:00 Azure Policy - Lab Activity 00:06:00 Azure Role Based Access Control ( RBAC ) 00:02:00 Azure Role Based Access Control ( RBAC ) - Lab Activity 00:07:00 Azure Locks 00:01:00 Azure Locks - Lab Activity 00:02:00 Azure Blueprints 00:01:00 Subscription Governance 00:02:00 Azure Tags 00:03:00 Azure Monitoring 00:02:00 Azure Monitor- Lab Activity 00:03:00 Azure Service Health 00:01:00 Monitoring Applications and Services 00:04:00 Compliance Terms and Requirements 00:02:00 Microsoft Privacy Statement 00:01:00 Microsoft Trust Center 00:01:00 Service Trust Portal 00:01:00 Azure Government Services 00:02:00 Azure Germany Services 00:01:00 Azure China 21Vianet 00:02:00 Module 4 : Azure Pricing and Support Module 4 Introduction : What tÌ¥o expect in this module 00:02:00 Azure Subscriptions 00:06:00 What are Management Groups 00:01:00 Purchase Azure Product & Services : Available Options 00:01:00 Usage Metrics 00:01:00 Factors Affecting Costs 00:02:00 The concept of Zones for Billing 00:02:00 Azure Pricing Calculator 00:04:00 Azure Total Cost of Ownership ( TCO ) 00:02:00 Ways to Minimize Costs in Azure 00:04:00 Azure Cost Management 00:02:00 Azure Support Plans 00:03:00 Alternative Support Options 00:02:00 Service Level Agreements ( SLA's ) 00:03:00 Composite SLA's 00:03:00 Improving Application SLA's 00:04:00 Public and Preview Features 00:01:00 Providing Feedback 00:01:00 General Availability 00:01:00 Azure Updates , Announcements and Roadmaps 00:01:00 Course Conclusion Course Conclusion 00:01:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Anti-Money Laundering (AML): This comprehensive Anti-Money Laundering (AML) course is designed for individuals interested in safeguarding organizations against financial crime. Through up-to-date knowledge and practical guidance, you will be trained in the essential skills required to identify and prevent suspicious activities within any organization. With step-by-step instructions on conducting client risk assessments, this course equips you with a strong foundation in AML procedures.

This comprehensive course on AWS Certified Cloud Practitioner (CLF-C01) empowers you to fast-track your IT career. Gain in-depth knowledge of cloud computing, AWS services, and architectural concepts. With hands-on labs, quizzes, and real practice exams, you will confidently build cost-effective, fault-tolerant IT solutions on the AWS Cloud.

This comprehensive course covers all Scrum principles and frameworks necessary to help participants understand how to guide a team and manage projects in a fast-paced agile environment. The course is meant for professionals who want to attain the certification of Scrum Master with deep insight into how AI can be utilized in increasing the effectiveness of agile practices. In addition to mastery of the core Scrum methodology, participants will be taken through state-of-the-art advancements in AI and machine learning in order to understand how these technologies can automate routine tasks, enhance decision-making, and continuous improvement. Real-world case studies and hands-on exercises will illustrate how to practically apply AI within Scrum to realize high efficiency and innovation for teams. Whether for enhancing one's career as a Scrum Master or the integration of AI into Agile practices, this course provides that ideal combination of conceptual theory and practical skills, assuring success in today's technology-driven world. Key Highlights: Certified Scrum Master training with AI applications Case studies in the real world about integrating AI in Scrum Hands-on projects to implement AI-driven tools and methodologies Workflow optimization techniques that ensure better collaboration of agile teams, with speeding up project delivery by the power of AI. Ideal for Scrum Masters, Agile Coaches, Product Owners, and tech pros looking to stay ahead.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Learn to effectively monitor the performance, health, and availability of Azure Services and your cloud resources

Duration 1 Days 6 CPD hours This course is intended for This course does not have any technical knowledge prerequisites for the learners, besides being proficient in using a computer and the Internet. IT and/or AI knowledge is a benefit but not a hard requirement. Given the rapid development of AI and the broad range of its applications in everyday life, it is crucial for anyone to attend this course to update their digital skills in an ever-changing world. It is expected that all learners have registered for a free account of OpenAI ChatGPT at https://chat.openai.com. Overview Discover how AI relates to other 4th industrial revolution technologies Learn about AI, ML, and associated cognitive services Overview of AI development frameworks, tools and services Evaluate the OpenAI ChatGPT4 / ChatGPT3.5 model features in more detail The core aim of this ?AI for beginners? course is to introduce its audience to Artificial Intelligence (AI) and Machine Learning (ML) technologies and allow them to understand the practical applications of AI in their everyday personal and professional life. Moreover, the course aims to provide a handful of demos and hands-on exercises to allow the learners to familiarize themselves with usage scenarios of OpenAI ChatGPT and other Generative AI (GenAI) models. The content of this course has been created primarily by using the OpenAI ChatGPT model. AI theoretical concepts. Introduction to AI, ML, and associated cognitive services (Computer vision, Natural language processing, Speech analysis, Decision making). How AI relates to other 4th industrial revolution technologies (cloud computing, edge computing, internet of things, blockchain, metaverse, robotics, quantum computing). AI model classification by utilizing mind maps and the distinctive role of Gen AI models. Introduction to the OpenAI ChatGPT model and alternative generative AI models. Familiarization with the basics of the ChatGPT interface (https://chat.openai.com). Talking about Responsible AI: Security, privacy, compliance, copyright, legal challenges, and ethical implications. AI practical applications Overview of AI development frameworks, tools and services. AI aggregators review. Hand-picked AI tool demos: a.Workplace productivity and the case of Microsoft 365 Copilot. b.The content creation industry. Create text, code, images, audio and video with Gen AI. c.Redefining the education sector with AI-powered learning. Evaluate the OpenAI ChatGPT4 / ChatGPT3.5 model features in more detail: a.Prompting and plugin demos. b.Code interpreter demos. Closing words. Discussion with an AI model on the future of AI. Additional course details: Nexus Humans AI for beginners 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 AI for beginners 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.