89 Algorithm courses in Belfast delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

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 5 Days 30 CPD hours This course is intended for Experienced system administrators and network administrators Overview By the end of the course, you should be able to meet the following objectives: Describe the NSX Advanced Load Balancer architecture Describe the NSX Advanced Load Balancer components and main functions Explain the NSX Advanced Load Balancer key features and benefits Deploy and configure the NSX Advanced Load Balancer infrastructure within private or public clouds using Write and No Access Cloud Connectors Explain, deploy, and configure Service Engines Explain and configure local load balancing constructors such as virtual services, pools, health monitors, and related components Understand and modify application behavior through profiles, policies, and DataScripts Configure advanced services such as global server load balancing Describe how to use NSX Advanced Load Balancer REST API interfaces and related automation capabilities Describe and configure NSX Advanced Load Balancer application and infrastructure monitoring Gather relevant information and perform basic troubleshooting of applications that use built-in NSX Advanced Load Balancer tooling This five-day, fast-paced course provides comprehensive training to install, configure, and manage a VMware NSX© Advanced Load Balancer? (Avi Networks) solution. This course covers key NSX Advanced Load Balancer (Avi Networks) features and functionality offered in the NSX Advanced Load Balancer 20.x release. Features include the overall infrastructure, virtual services and application components, global server load balancing, various cloud connectors, application troubleshooting, and solution monitoring. Hands-on labs provide access to a software-defined data center environment to reinforce the skills and concepts presented in the course. Course Introduction Introduction and course logistics Course objectives Introduction to NSX Advanced Load Balancer Introduce NSX Advanced Load Balancer Discuss NSX Advanced Load Balancer use cases and benefits Explain NSX Advanced Load Balancer architecture and components Explain the management, control, data, and consumption planes and their respective functions Virtual Services Configuration Concepts Explain Virtual Service components Explain Virtual Service types Explain and configure basic Virtual Service components such as Application Profiles, Network Profiles Profiles and Policies Explain and deep dive on Advanced Virtual Service creation Explain and deep dive on Application Profiles and Types such as L4, DNS, Syslog, and HTTP Explain and configure advanced application HTTP Profile options Deep dive on Network Profiles and Types Explain and configure SSL Profiles and Certificates Explain and Configure HTTP and DNS policies Pools Configuration Concepts Explain and deep dive on Pools configuration options Describe available Load Balancing algorithms Explain multiple Health Monitor types Explain multiple Persistence Profiles Explain and configure Pool Groups Modifying Application Behavior Design and apply application solutions leveraging application profiles Design and apply application solutions leveraging Network and HTTP Policies and DataScripts Explain DataScript fundamentals Explain and leverage NSX Advanced Load Balancer analytics to understand application behavior Describe and configure Client SSL Certificate Validation Describe and configure Virtual Service DDoS, Rate Limiting, and Throttling capabilities Modify Network Profiles properties such as TCP connection properties Design and apply application solutions leveraging Persistence Profiles NSX Advanced Load Balancer Infrastructure Architecture Deep dive on the management, control, data, and consumption planes and functions Describe Control Plane Clustering and High Availability Describe Controller Process Sharding Describe Controller Sizing Describe Service Engine CPU and NIC Architecture Explain Tenants Deep dive and configure properties of Service Engine Groups Explain Service Engine Group High Availability Modes Describe and configure Active/Standby High Availability Mode Describe and configure Elastic HA High Availability Mode (Active/Active, N+M) Explain Service Engine Failure Detection and Self-Healing Describe Service Engine as a Router Deep dive on Virtual Service scale out options, such as Layer 2 (Native), Layer 3 (BGP), and DNS-based Introduction to Cloud Connector Introduce Cloud Connectors Review Cloud Connector integration modes Introduce Cloud Connector types Install, Configure and Manage NSX Advanced Load Balancer in No-Access Cloud Explain No Access Cloud concepts Configure No Access Cloud integration Explain and Configure Linux Server Cloud Describe the Advanced Configuration options available in Bare-Metal (Linux Server Cloud) Install, Configure and Manage NSX Advanced Load Balancer in VMware Environment: Cloud Configuration Introduce VMware integration options Explain and configure VMware No Access Cloud Connector Explain and configure VMware Write Access Cloud Connector Describe VMware Write with NSX-V Access Cloud Connector Describe VMware NSX-T integration AWS Cloud Configuration Describe NSX Advanced Load Balancer Public Cloud integrations Explain and demonstrate AWS Public Cloud Integration DNS Foundations Review, discuss, and explain DNS fundamentals Describe NSX Advanced Load Balancer DNS and IPAM providers Global Server Load Balancing Introduce Global Server Load Balancing Concepts and Benefits Explain and configure NSX Advanced Load Balancer infrastructure Explain and configure DNS Virtual Service components Explain and configure GSLB Service Engine Group Describe and configure GSLB Sites Explain and configure basic GSLB Services to include pools and health monitors Describe GSLB Service Load Balancing algorithms Explain and configure Data and Control Plane-based Health Monitors Describe GSLB Health Monitor Proxy NSX Advanced Load Balancer: Troubleshooting Introduce Infrastructure and Application Troubleshooting Concepts Describe Control Plane and Data Plane-based Troubleshooting Explain Application Analytics and Logs Describe client logs analysis Explain Headers troubleshooting and Packet Capture mechanism Leverage CLI for detailed data plane troubleshooting Explain Service Engine Logs Explain Health Monitors troubleshooting Explain BGP session troubleshooting Describe Control Plane Troubleshooting, Clustering, and Cloud Connector issues Events and Alerts Describe NSX Advanced Load Balancer Events Describe and configure NSX Advanced Load Balancer Alerts Describe NSX Advanced Load Balancer monitoring capabilities, leveraging SNMP, Syslog, and Email Introduction to NSX Advanced Load Balancer Rest API Introduce NSX Advanced Load Balancer REST API interface Describe REST API Object Schema Explain and interact with REST API interface, leveraging browser and command line utility Explain Swagger-based API documentation Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware NSX Advanced Load Balancer: Install, Configure, Manage [V20.x] 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 VMware NSX Advanced Load Balancer: Install, Configure, Manage [V20.x] 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 5 Days 30 CPD hours This course is intended for Experienced system administrators and network administrators Overview By the end of the course, you should be able to meet the following objectives: Describe the NSX Advanced Load Balancer architecture Describe the NSX Advanced Load Balancer components and main functions Explain the NSX Advanced Load Balancer key features and benefits Deploy and configure the NSX Advanced Load Balancer infrastructure within private or public clouds using Write and No-Access Cloud Connectors Explain, deploy, and configure Service Engines Explain and configure local load balancing constructs such as virtual services, pools, health monitors, and related components Explain and configure advanced virtual services and related concepts such as Subject Name Indication, Enhanced Virtual Hosting, and authentication of virtual services Explain and modify application behavior through profiles, policies, and DataScripts Describe Central licensing management using VMware NSX Advanced Load Balancer Enterprise with Cloud services (formerly Avi Pulse) Explain how to configure Role-Based Access Control (RBAC) in NSX Advanced Load Balancer Configure advanced services such as global server load balancing Describe how to use NSX Advanced Load Balancer REST API interfaces and related automation capabilities Describe and configure NSX Advanced Load Balancer application and infrastructure monitoring Gather relevant information and perform basic troubleshooting of applications that use built-in NSX Advanced Load Balancer tooling Identify the key features of VMware NSX Network Detection and Response This five-day, fast-paced course provides comprehensive training to install, configure, and manage a VMware NSX Advanced Load Balancer (Avi Networks) solution. This course covers key NSX Advanced Load Balancer (Avi Networks) features and functionality offered in the NSX Advanced Load Balancer 21.x release. Features include the overall infrastructure, virtual services, application components, global server load balancing, various cloud connectors, application troubleshooting, and solution monitoring. Hands-on labs provide access to a software-defined data center environment to reinforce the skills and concepts presented in the course. Course Introduction Introduction and course logistics Course objectives Introduction to NSX Advanced Load Balancer Introduce NSX Advanced Load Balancer Discuss NSX Advanced Load Balancer use cases and benefits Explain NSX Advanced Load Balancer architecture and components Explain the management, control, data, and consumption planes and their respective functions Virtual Services Configuration Concepts Explain virtual service components Explain virtual service types Explain and configure basic virtual service components such as application profiles and network profiles Virtual Services Configuration Advanced Concepts Explain the virtual service advanced components such as Wildcard VIP, Server Name Identification (SNI), and Enhanced Virtual Hosting (EVH) Explain the concept of virtual service VIP Sharing Explain different authentication mechanisms used for a virtual service such as LDAP, SAML, JSON Web Token, and OAUTH Profiles and Policies Explain application profiles and types such as L4, DNS, Syslog, HTTP, and VMware Horizon VDI Explain and configure advanced application HTTP profile options Describe network profiles and types Explain and configure SSL profiles and certificates Explain and configure HTTP, network, and DNS policies Pools Configuration Concepts Explain pools configuration options Describe the available load balancing algorithms Explain multiple health monitor types Explain multiple Persistence profiles Explain and configure pool groups Modifying Application Behavior Design and apply application solutions by using application profiles Design and apply application solutions by using network, HTTP policies, and DataScripts Explain DataScript fundamentals Explain and use NSX Advanced Load Balancer analytics to understand application behavior Describe and configure client SSL certificate validation Describe and configure virtual service DDoS, Rate limiting, and Throttling capabilities Modify network profile properties such as TCP connection properties Design and apply application solutions by using Persistence profiles NSX Advanced Load Balancer Infrastructure Architecture Explain management, control, data, and consumption planes and functions Describe control plane clustering and high availability Describe controller sizing and process sharing Describe Service Engine CPU and NIC architecture Explain tenants Configure properties of Service Engine groups Explain Service Engine group high availability modes Describe and configure active-standby high availability mode Explain Service Engine placement in multiple availability zones for public clouds Describe and configure elastic HA high availability mode (Active-Active, N+M) Explain Service Engine failure detection and self-healing Describe Service Engine as a router Explain virtual service scale-out options such as Layer 2 (Native), Layer 3 (BGP), and DNS-based Describe how to upgrade NSX Advanced Load Balancer Introduction to Cloud Connector Explain cloud connectors Review cloud connector integration modes List cloud connector types Review the different Service Engine image types in different ecosystems Installing, Configuring, and Managing NSX Advanced Load Balancer in No-Orchestrator Cloud Explain No-Access cloud concepts Configure No-Access cloud integration on bare metal Explain and configure Linux Server Cloud Explain and configure VMware No Orchestrator Describe the advanced configuration options available in bare metal (Linux Server Cloud) Installing, Configuring, and Managing NSX Advanced Load Balancer in VMware Environment: Cloud Configuration Introduce VMware integration options Explain and configure VMware Write Access Cloud Connector Explain NSX Advanced Load Balancer integration options in a VMware NSX environment Explain and configure NSX Cloud Connector for Overlay and VLAN-backed segments AWS Cloud Configuration Describe NSX Advanced Load Balancer public cloud integrations Explain different AWS components Explain and demonstrate AWS public cloud integration Deploy VMware NSX Advanced Load Balancer Controller, SEs, and virtual services in AWS Cloud Review Multi-AZ Support for virtual services in AWS cloud GCP Cloud Configuration Explain different GCP components Explain and demonstrate GCP public cloud integration Deploy NSX Advanced Load Balancer Controller, SEs, and virtual services in GCP cloud Azure Cloud Configuration Describe NSX Advanced Load Balancer public cloud integrations Explain different Microsoft Azure components Explain and demonstrate Azure public cloud integration Deploy NSX Advanced Load Balancer Controller, SEs, and virtual services in Azure Cloud NSX Advanced Load Balancer Enterprise with Cloud Services (Avi Pulse) Describe NSX Advanced Load Balancer public cloud services Explain different features of NSX Advanced Load Balancer Cloud Services Register the controller with Cloud Services DNS Foundations Review, discuss, and explain DNS fundamentals Describe NSX Advanced Load Balancer DNS and IPAM providers Global Server Load Balancing (GSLB) Introduce Global Server load balancing concepts and benefits Explain and configure the NSX Advanced Load Balancer infrastructure Explain and configure the DNS Virtual Service components Explain and configure GSLB Service Engine Group Describe and configure GSLB sites Explain and configure basic GSLB services to include pools and health monitors Describe GSLB Server Load Balancing algorithms Explain and configure health monitors based on data plane and control plane Describe GSLB Health Monitor Proxy Explain GSLB Site-Cookie Persistence Explain the different GSLB replication methods Role-Based Access Control (RBAC) Introduce local authentication in NSX Advanced Load Balancer Introduce remote authentication in NSX Advanced Load Balancer Review the different types of remote authentication Explain granular RBAC using labels NSX Advanced Load Balancer: Troubleshooting Introduce infrastructure and application troubleshooting concepts Describe troubleshooting based on control plane and data plane Explain application analytics and logs Describe client logs analysis Explain headers troubleshooting and packet capture mechanism Describe how to use CLI for detailed data plane troubleshooting Explain Service Engine logs Explain health monitors troubleshooting Explain BGP session troubleshooting Describe control plane troubleshooting, clustering, and cloud connector issues Events and Alerts Describe NSX Advanced Load Balancer events Describe and configure NSX Advanced Load Balancer alerts Describe NSX Advanced Load Balancer monitoring capabilities with SNMP, Syslog, and Email Introduction to NSX Advanced Load Balancer Rest API Introduce the NSX Advanced Load Balancer REST API interface Describe REST API Object Schema Explain and interact with REST API interface with

Duration 4 Days 24 CPD hours This course is intended for Project administrators and ETL developers responsible for data extraction and transformation using DataStage. Overview Describe the uses of DataStage and the DataStage workflowDescribe the Information Server architecture and how DataStage fits within itDescribe the Information Server and DataStage deployment optionsUse the Information Server Web Console and the DataStage Administrator client to create DataStage users and to configure the DataStage environmentImport and export DataStage objects to a fileImport table definitions for sequential files and relational tablesDesign, compile, run, and monitor DataStage parallel jobsDesign jobs that read and write to sequential filesDescribe the DataStage parallel processing architectureDesign jobs that combine data using joins and lookupsDesign jobs that sort and aggregate dataImplement complex business logic using the DataStage Transformer stageDebug DataStage jobs using the DataStage PX Debugger This course enables the project administrators & developers to acquire the skills necessary to develop parallel jobs in DataStage. Students will learn to create parallel jobs that access sequential & relational data, and combine and transform the data. Course Outline Introduction to DataStage Deployment DataStage Administration Work with Metadata Create Parallel Jobs Access Sequential Data Partitioning and Collecting Algorithms Combine Data Group Processing Stages Transformer Stage Repository Functions Work with Relational Data Control Jobs

Duration 1 Days 6 CPD hours This course is intended for Experienced system administrators or network administrators Overview By the end of the course, you should be able to meet the following objectives: Describe NSX Advanced Load Balancer architecture Describe the NSX Advanced Load Balancer components and main functions Explain the NSX Advanced Load Balancer key features and benefits Explain and configure Local Load Balancing constructors such as Virtual Services, Pools, Health Monitors and related components During this one-day course, you gain an understanding of the architecture and features of VMware NSX Advanced Load Balancer (Avi Networks) solution. This course provides hands-on labs to provide a solid foundation to load balancing fundamentals and work with most common load balancing functionality offered by VMware NSX Advanced Load Balancer (Avi Networks) solution. Course Introduction Introductions and course logistics Course objectives Introduction to NSX Advanced Load Balancer Introduce NSX Advanced Load Balancer Discuss NSX Advanced Load Balancer use cases and benefits Explain NSX Advanced Load Balancer architecture and components Explain the management, control, data, and consumption planes and their respective functions Virtual Services Configuration Concepts Explain Virtual Service components Explain Virtual Service types Explain and configure basic virtual services components such as Application Profiles, Network Profiles, Pools and Health Monitors Profiles and Policies Explain and deep dive on Advanced Virtual Service creation Explain and deep dive on Application Profiles and Types such as L4, DNS, Syslog and HTTP Explain and configure advanced application HTTP Profile options Deep dive on Network Profiles and Types Explain and configure SSL Profiles and Certificates Explain and Configure HTTP and DNS policies Pools Configuration Concepts Explain and deep dive on Pools configuration options Describe available Load Balancing algorithms Explain multiple Health Monitor types Explain multiple Persistence Profiles Explain and configure Pool Groups

Duration 3 Days 18 CPD hours This course is intended for Business application consultant Data Consultant / Manager Database Administrator Application developer BI specialist Overview This course will prepare you to: Understand and put into practice the main advanced modeling capabilities of SAP HANA 2.0 SPS04 in the areas of text search and analysis, graph modeling, spatial analysis and predictive modeling. Promote these advanced modeling capabilities to extend the core SAP HANA Modeling features. Broaden your experience with the modern SAP HANA tooling in XS Advanced (SAP Web IDE for SAP HANA) This course provides advanced knowledge and practical experience on several topics that are included in, or connected to, the scope of the modeler role. Its purpose is to take a step further, beyond the core modeling knowledge from HA300, and to demonstrate how applications powered by SAP HANA can benefit from innovations such as Spatial Data Storage and Processing, Text Search and Analysis, Predictive Analysis and Graph Modeling.The course is supported by many demos and exercise, which demonstrate the advanced modeling capabilities in several scenarios. For example, working with classical schemas or HDI containers in XS Advanced, using the SQL console, developing graphical models. Some of the proposed case studies blend together several modeling capabilities, such as text with spatial, or text with graph.An introduction to SAP HANA Series Data is also provided. Introduction to Advanced ModelingSAP HANA Predictive Analysis Library (PAL) Describing SAP HANA PAL Using PAL in Flowgraphs Calling PAL Functions in Calculation Views Calling PAL Procedures in SQL Scripts Exploring the PAL Library SAP HANA Spatial Introducing SAP HANA Spatial Working with Spatial Data Types Importing and Exporting Spatial Data Accessing and Manipulating Spatial Data Using Spatial Clustering SAP HANA Graph Defining SAP HANA Graph Workspace Describing the Different Graph Algorithms Using the Graph Node in Calculation Views Using GraphScript Procedures SAP HANA Text Understanding Full Text Search Understanding Text Analysis Understanding Text Mining SAP HANA Series Data Getting Started with SAP HANA Series Data

Duration 3 Days 18 CPD hours This course is intended for Java Fundamentals is designed for tech enthusiasts who are familiar with some programming languages and want a quick introduction to the most important principles of Java. Overview After completing this course, you will be able to: Create and run Java programs Use data types, data structures, and control flow in your code Implement best practices while creating objects Work with constructors and inheritance Understand advanced data structures to organize and store data Employ generics for stronger check-types during compilation Learn to handle exceptions in your code Since its inception, Java has stormed the programming world. Its features and functionalities provide developers with the tools needed to write robust cross-platform applications. Java Fundamentals introduces you to these tools and functionalities that will enable you to create Java programs. The course begins with an introduction to the language, its philosophy, and evolution over time, until the latest release. You'll learn how the javac/java tools work and what Java packages are - the way a Java program is usually organized. Once you are comfortable with this, you'll be introduced to advanced concepts of the language, such as control flow keywords. You'll explore object-oriented programming and the part it plays in making Java what it is. In the concluding lessons, you'll be familiarized with classes, typecasting, and interfaces, and understand the use of data structures, arrays, strings, handling exceptions, and creating generics. Introduction to Java The Java Ecosystem Our First Java Application Packages Variables, Data Types, and Operators Variables and Data Types Integral Data Types Type casting Control Flow Conditional Statements Looping Constructs Object-Oriented Programming Object-Oriented Principles Classes and Objects Constructors The this Keyword Inheritance Overloading Constructor Overloading Polymorphism and Overriding Annotations References OOP in Depth Interfaces Typecasting The Object Class Autoboxing and Unboxing Abstract Classes and Methods Data Structures, Arrays, and Strings Data Structures and Algorithms Strings The Java Collections Framework and Generics Reading Data from Files The Java Collections Framework Generics Collection Advanced Data Structures in Java Implementing a Custom Linked List Implementing Binary Search Tree Enumerations Set and Uniqueness in Set Exception Handling Motivation behind Exceptions Exception Sources Exception Mechanics Best Practices

Duration 3 Days 18 CPD hours This course is intended for This class is designed for experienced Salesforce Administrators with little or no Flow experience who need to streamline business processes with no-code automated solutions. This class is not recommended for developers. However, if you are a developer interested in learning Flow, we highly recommend Declarative Development for Platform App Builders in Lightning Experience (DEX403). Overview When you complete this course, you will be able to: Create automated no-code solutions with Salesforce Flow. Analyze use cases and effectively translate requirements into design plans that detail accurate Flow building processes. Understand and leverage various Flow types, elements, and resources. Build Screen Flows and manage screen layouts and field visibility. Implement Flows on Home and Record pages. Automate business processes by creating Record-Triggered Flows (based on record create, update, or delete) to perform specific actions. Identify best practices for creating and managing Flows. Streamline business processes and automate manual tasks across your organization by building no-code automation solutions with Flow Builder. In this 3-day class designed for administrators, our experts will introduce you to Screen and Record-Trigger Flows, in addition to various Flow elements and resources. Learn how to effectively create and manage Flows that champion automation best practices, solve for user requirements, and empower you to get more out of Salesforce. Foundations of Flow Create Variables Understand Algorithms Explore Control Structures Examine Flow Best Practices Screen Flows Use Elements and Resources Control Field Visibility Manage Data and Navigation Surface a Flow Complete the Flow Record-Triggered Flows Define Flow Triggers Use Flow Triggers to Replace Formula Fields Share Records with Flow Additional course details: Nexus Humans Salesforce Automate No-Code Solutions Using Flow (ADX301) 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 Salesforce Automate No-Code Solutions Using Flow (ADX301) 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.