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The key to a confident birth experience is knowledge and education. This workshop will guide you through the process.

Understanding a buildings heat loss for heating engineers

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: Intermediate software developers Overview In this course, you will learn to: Set up the AWS SDK and developer credentials for Java, C#/.NET, Python, and JavaScript Interact with AWS services and develop solutions by using the AWS SDK Use AWS Identity and Access Management (IAM) for service authentication Use Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB as data stores Integrate applications and data by using AWS Lambda, Amazon API Gateway, Amazon Simple Queue Service (Amazon SQS), Amazon Simple Notification Service (Amazon SNS), and AWS Step Functions Use Amazon Cognito for user authentication Use Amazon ElastiCache to improve application scalability Leverage the CI/CD pipeline to deploy applications on AWS In this course, you learn how to use the AWS SDK to develop secure and scalable cloud applications using multiple AWS services such as Amazon DynamoDB, Amazon Simple Storage Service, and AWS Lambda. You explore how to interact with AWS using code and learn about key concepts, best practices, and troubleshooting tips. Module 0: Course Overview Agenda Introductions Student resources Module 1: Introduction to AWS Introduction to the AWS Cloud Cloud scenarios Infrastructure overview Introduction to AWS foundation services Module 2: Introduction to Developing on AWS Getting started with developing on AWS Introduction to developer tools Introduction to management tools Module 3: Introduction to AWS Identity and Access Management Shared responsibility model Introduction to IAM Use authentication and authorization Module 4: Introduction to the Lab Environment Introduction to the lab environment Lab 1: Getting started and working with IAM Module 5: Developing Storage Solutions with Amazon Simple Storage Service Overview of AWS storage options Amazon S3 key concepts Best practices Troubleshooting Scenario: Building a complete application Lab 2: Developing storage solutions with Amazon S3 Module 6: Developing Flexible NoSQL Solutions with Amazon DynamoDB Introduction to AWS database options Introduction to Amazon DynamoDB Developing with DynamoDB Best practices Troubleshooting Scenario: Building an end-to-end app Lab 3: Developing flexible NoSQL solutions with Amazon DynamoDB Module 7: Developing Event-Driven Solutions with AWS Lambda What is serverless computing? Introduction to AWS Lambda Key concepts How Lambda works Use cases Best practices Scenario: Build an end-to-end app Module 8: Developing Solutions with Amazon API Gateway Introduction to Amazon API Gateway Developing with API Gateway Best practices Introduction to AWS Serverless Application Model Scenario: Building an end-to-end app Lab 4: Developing event-driven solutions with AWS Lambda Module 9: Developing Solutions with AWS Step Functions Understanding the need for Step Functions Introduction to AWS Step Functions Use cases Module 10: Developing Solutions with Amazon Simple Queue Service and Amazon Simple Notification Service Why use a queueing service? Developing with Amazon Simple Queue Service Developing with Amazon Simple Notification Service Developing with Amazon MQ Lab 5: Developing messaging solutions with Amazon SQS and Amazon SNS Module 11: Caching Information with Amazon ElastiCache Caching overview Caching with Amazon ElastiCache Caching strategies Module 12: Developing Secure Applications Securing your applications Authenticating your applications to AWS Authenticating your customers Scenario: Building an end-to-end app Module 13: Deploying Applications Introduction to DevOps Introduction to deployment and testing strategies Deploying applications with AWS Elastic Beanstalk Scenario: Building an end-to-end app Lab 6: Building an end-to-end app Module 14: Course wrap-up Course overview AWS training courses Certifications Course feedback

Duration 3 Days 18 CPD hours This course is intended for Blockchain Architects Blockchain DevelopersApplication Developers Blockchain System AdministratorsNetwork Security Architects Cyber Security ExpertsIT Professionals w/cyber security experience Overview Those who attend the Security for Blockchain Professionals course and pass the exam certification will have a demonstrated knowledge of:Identifying and differentiating between security threats and attacks on a Blockchain network.Blockchain security methods, best practices, risk mitigation, and more.All known (to date) cyber-attack vectors on the Blockchain.Performing Blockchain network security risk analysis.A complete understanding of Blockchain?s inherent security features and risks.An excellent knowledge of best security practices for Blockchain System/Network Administrators.Demonstrating appropriate Blockchain data safeguarding techniques. This course covers all known aspects of Blockchain security that exist in the Blockchain environment today and provides a detailed overview of all Blockchain security issues, including threats, risk mitigation, node security integrity, confidentiality, best security practices, advanced Blockchain security and more. Fundamental Blockchain Security Cryptography for the Blockchain Hash Functions Public Key Cryptography Elliptic Curve Cryptography A Brief Introduction to Blockchain The Blocks The Chains The Network Promises of the Blockchain Blockchain Security Assumptions Digital Signature Security Hash Function Security Limitations of Basic Blockchain Security Public Key Cryptography Review Real-Life Public Key Protection Cryptography and Quantum Computers Lab 1 (Tentative) Finding Hash Function Collisions Reversible hash function Hash function with poor non-locality Hash function with small search space Breaking Public Key Cryptography Brute Forcing a Short Private Key Brute Forcing a Poorly-Chosen Private Key Consensus in the Blockchain Blockchain Consensus and Byzantine Generals Blockchain Networking Review Byzantine Generals Problem Relation to Blockchain Byzantine Fault Tolerance Introduction to Blockchain Consensus Security Blockchain Consensus Breakthrough Proof of Work What is Proof of Work? How does Proof of Work Solve BGP? Proof of Work Security Assumptions Attacking Proof of Work Proof of Stake What is Proof of Stake? How does Proof of Stake Solve BGP? Proof of Stake Security Assumptions Attacking Proof of Stake General Attacks on Blockchain Consensus Other Blockchain Consensus Algorithms Lab 2 (Tentative) Attacking Proof of Work Performing a 51% Attack Performing a Selfish Mining Attack Attacking Proof of Stake Performing a XX% Attack Performing a Long-Range Attack Malleable Transaction Attacks Advanced Blockchain Security Mechanisms Architectural Security Measures Permissioned Blockchains Checkpointing Advanced Cryptographic Solutions Multiparty Signatures Zero-Knowledge Proofs Stealth Addresses Ring Signatures Confidential Transactions Lab 3 (Tentative) Permissioned Blockchains 51% on a Checkpointed Blockchain Data mining on a blockchain with/without stealth addresses Zero-Knowledge Proof Simulation Trying to fake knowledge of a ZKP Module 4: Blockchain for Business Introduction to Ethereum Security What is Ethereum Consensus in Ethereum Smart Contracts in Ethereum Ethereum Security Pros and Cons of Ethereum Blockchains Introduction to Hyperledger Security What is Hyperledger Consensus in Hyperledger Smart Contracts in Hyperledger Hyperledger Security Pros and Cons of Hyperledger Blockchains Introduction to Corda Security What is Corda Consensus in Corda Smart Contracts in Corda Corda Security Pros and Cons of Corda Blockchains Lab 4 Blockchain Risk Assessment What are the Risks of the Blockchain? Information Security Information Sensitivity Data being placed on blockchain Risks of disclosure Regulatory Requirements Data encryption Data control PII protection Blockchain Architectural Design Public and Private Blockchains Open and Permissioned Blockchains Choosing a Blockchain Architecture Lab 5 Exploring public/private open/permissioned blockchains? Basic Blockchain Security Blockchain Architecture User Security Protecting Private Keys Malware Update Node Security Configuring MSPs Network Security Lab 6 (TBD) Smart Contract Security Introduction to Smart Contracts Smart Contract Security Considerations Turing-Complete Lifetime External Software Smart Contract Code Auditing Difficulties Techniques Tools Lab 7 (Tentative) Try a couple of smart contract code auditing tool against different contracts with built-in vulnerabilities Module 8: Security Implementing Business Blockchains Ethereum Best Practices Hyperledger Best Practices Corda Best Practices Lab 8 Network-Level Vulnerabilities and Attacks Introduction to Blockchain Network Attacks 51% Attacks Denial of Service Attacks Eclipse Attacks Routing Attacks Sybil Attacks Lab 9 Perform different network-level attacks System-Level Vulnerabilities and Attacks Introduction to Blockchain System Vulnerabilities The Bitcoin Hack The Verge Hack The EOS Vulnerability Lab 10 Smart Contract Vulnerabilities and Attacks Introduction to Common Smart Contract Vulnerabilities Reentrancy Access Control Arithmetic Unchecked Return Values Denial of Service Bad Randomness Race Conditions Timestamp Dependence Short Addresses Lab 11 Exploiting vulnerable smart contracts Security of Alternative DLT Architectures What Are Alternative DLT Architectures? Introduction to Directed Acyclic Graphs (DAGs) DAGs vs. Blockchains Advantages of DAGs DAG Vulnerabilities and Security Lab 12 Exploring a DAG network

Duration 1 Days 6 CPD hours This course is intended for This overview-level course is ideally suited for professionals seeking an introduction to microservices architecture and its application within a business context. Ideal attendee roles include software developers, system architects, technical managers, and IT professionals who are part of teams transitioning to a microservices approach. It's also an excellent starting point for non-technical roles such as product owners or business analysts who work closely with technical teams and want to better understand and become conversant in the language and principles of microservices. Overview This course combines engaging instructor-led presentations and useful demonstrations with engaging group activities. Throughout the course you'll explore: Understand the Basics of Microservices: Get to know the fundamental principles and characteristics of microservices and how they revolutionize traditional software development approaches. Explore the Design of Microservices: Gain an overview of how microservices are designed based on business requirements and what makes them unique in the software architecture world. Overview of Managing and Scaling Microservices: Get an introduction to how microservices are managed and scaled independently, and understand the significance of these features in your business operations. Familiarize with the Microservices Ecosystem: Learn about the typical patterns, best practices, and common pitfalls in the microservices world, setting a foundation for future learning and implementation. Introduction to Microservices in a Business Context: Acquire a basic understanding of how microservices can be aligned with specific business capabilities, and get a glimpse into how they can coexist with legacy systems in a business setting. Microservices have rapidly emerged as a popular architectural style, breaking down applications into small, independent services that can be developed, deployed, and scaled individually. Microservices offer a robust method to address a variety of projects, such as e-commerce platforms and content management systems, enhancing scalability and boosting productivity. This technology, when employed correctly, can greatly increase software delivery speed and system resilience, making it a crucial skill set for modern technology professionals.Understanding Microservices - A Technical Overview is a one-day course ideally suited for technical professionals seeking an introduction to microservices architecture and its application within a business context. Under the guidance of an industry expert, this engaging class combines lecture-style learning with lively demonstrations, case study review and group discussions.Throughout the course you?ll explore the principles and characteristics that define microservices, how to identify suitable projects for a microservices approach, the factors to consider when designing them, and the strategies to effectively manage and scale them within complex systems. You?ll also learn about the best practices, patterns, and anti-patterns, arming you with the knowledge to make the right architectural choices. This course also explores the real-world implementation of microservices in a business enterprise. We'll discuss how to align the application of microservices with your organization's specific business capabilities, and offer strategies for smoothly integrating this technology within existing legacy systems. Introduction to Microservices Understand what microservices are and their role in modern software development. Introduction to Microservices: what they are and why they matter. Monolithic vs Microservices: highlighting the shift and benefits. Key principles and characteristics of microservices. Identifying suitable applications for microservices transformation. Demo: Analyzing a sample application and identifying potential microservices Architecting and Managing Microservices Learn the basic strategies for scaling and managing microservices. Scaling Microservices: from a single service to hundreds. Key components of a microservices architecture. Introduction to resilience patterns: Circuit-Breakers and Bulkheads. Load management and provisioning in a microservices setup. Understanding the role of cloud services in microservices. Optional Demo: Illustrating how a microservice-based application scales in real-time Designing Microservices Learn the key aspects to consider when designing microservices. Defining microservice boundaries: Deciding the scope of a microservice. Communication patterns in microservices. Understanding Microservice endpoints. Exploring data stores and transaction boundaries in microservices. Overcoming challenges in Microservices design. Demo: Designing microservices for a hypothetical business requirement Implementing Microservices in a Business Enterprise Understand the process and considerations for implementing microservices in an enterprise context. Assessing enterprise readiness for microservices. Building the business case for microservices: strategic advantages and potential challenges. Aligning microservices with business capabilities. Organizational changes: Team structures and processes for microservices. Dealing with Legacy Systems: Strategies for microservices integration. Demo: Exploring a case study of successful microservices implementation in a business enterprise The Microservices Ecosystem Understand the key tools and best practices in the Microservices ecosystem. Understanding the typical Microservices Stack. Monitoring and Logging in Microservices. Introduction to Docker: Containerization of Microservices. Deployment strategies in a Microservices setup. Introduction to Orchestration in Microservices Demo: Containerizing and deploying a simple microservice Microservices Deployment Strategies Understand various ways to safely introduce changes in a microservices environment. The concept of Blue-Green Deployment: changing services without downtime. Canary Releases and Feature Toggles: slowly rolling out changes to users. Database changes in a microservices environment: keeping data consistent. Demo: Examining various deployment strategies Microservices Best Practices and DevOps Learn key strategies to ensure a smooth operation of your microservices setup. The DevOps culture in Microservices: collaboration for efficiency. Defining a Minimum Viable Product in a Microservices setup: building small, delivering fast. Dealing with data in a distributed setup: managing Data Islands. The importance of Continuous Integration/Continuous Delivery in a microservices setup. Governance: Keeping track of your services and their consumers. Demo: Visualizing a simple continuous delivery pipeline Microservices Patterns and Anti-Patterns Learn about common do's and don'ts when working with microservices. Understanding patterns that help with efficient microservices operation. Recognizing and avoiding anti-patterns that can hinder performance. Dealing with common challenges: dependencies between services, managing service boundaries. Demo: Examples of real-world patterns and anti-patterns Simple Overview of OAuth and OpenID for Microservices Introduction to OAuth and OpenID: What they are and why they matter in Microservices. The role of tokens in OAuth 2.0: How they help in securing communications. A simplified look at OpenID Connect: Linking identities across services. Demo

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

Duration 2 Days 12 CPD hours This course is intended for Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions. Overview Introduction to advanced statistical analysis Group variables: Factor Analysis and Principal Components Analysis Group similar cases: Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Predict categorical targets with Discriminant Analysis Predict categorical targets with Logistic Regression Predict categorical targets with Decision Trees Introduction to Survival Analysis Introduction to Generalized Linear Models Introduction to Linear Mixed Models This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases. Introduction to advanced statistical analysis Taxonomy of models Overview of supervised models Overview of models to create natural groupings Group variables: Factor Analysis and Principal Components Analysis Factor Analysis basics Principal Components basics Assumptions of Factor Analysis Key issues in Factor Analysis Improve the interpretability Use Factor and component scores Group similar cases: Cluster Analysis Cluster Analysis basics Key issues in Cluster Analysis K-Means Cluster Analysis Assumptions of K-Means Cluster Analysis TwoStep Cluster Analysis Assumptions of TwoStep Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Nearest Neighbor Analysis basics Key issues in Nearest Neighbor Analysis Assess model fit Predict categorical targets with Discriminant Analysis Discriminant Analysis basics The Discriminant Analysis model Core concepts of Discriminant Analysis Classification of cases Assumptions of Discriminant Analysis Validate the solution Predict categorical targets with Logistic Regression Binary Logistic Regression basics The Binary Logistic Regression model Multinomial Logistic Regression basics Assumptions of Logistic Regression procedures Testing hypotheses Predict categorical targets with Decision Trees Decision Trees basics Validate the solution Explore CHAID Explore CRT Comparing Decision Trees methods Introduction to Survival Analysis Survival Analysis basics Kaplan-Meier Analysis Assumptions of Kaplan-Meier Analysis Cox Regression Assumptions of Cox Regression Introduction to Generalized Linear Models Generalized Linear Models basics Available distributions Available link functions Introduction to Linear Mixed Models Linear Mixed Models basics Hierachical Linear Models Modeling strategy Assumptions of Linear Mixed Models Additional course details: Nexus Humans 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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.