680 PIP courses delivered Online

Duration 3 Days 18 CPD hours This course is intended for Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek an understanding of the parallel framework architecture. In this course, students will develop a deeper understanding of the DataStage architecture, including a deeper understanding of the DataStage development and runtime environments. Introduction to the Parallel Framework Architecture Describe the parallel processing architecture Describe pipeline and partition parallelism Describe the role of the configuration file Design a job that creates robust test data Compiling & Executing Jobs Describe the main parts of the configuration file Describe the compile process and the OSH that the compilation process generates Describe the role and the main parts of the Score Describe the job execution process Partitioning & Collecting Data Understand how partitioning works in the Framework Viewing partitioners in the Score Selecting partitioning algorithms Generate sequences of numbers (surrogate keys) in a partitioned, parallel environment Sorting Data Sort data in the parallel framework Find inserted sorts in the Score Reduce the number of inserted sorts Optimize Fork-Join jobs Use Sort stages to determine the last row in a group Describe sort key and partitioner key logic in the parallel framework Buffering in Parallel Jobs Describe how buffering works in parallel jobs Tune buffers in parallel jobs Avoid buffer contentions Parallel Framework Data Types Describe virtual data sets Describe schemas Describe data type mappings and conversions Describe how external data is processed Handle nulls Work with complex data Reusable Components Create a schema file Read a sequential file using a schema Describe Runtime Column Propagation (RCP) Enable and disable RCP Create and use shared containers Balanced Optimization Enable Balanced Optimization functionality in Designer Describe the Balanced Optimization workflow List the different Balanced Optimization options. Push stage processing to a data source Push stage processing to a data target Optimize a job accessing Hadoop HDFS file system Understand the limitations of Balanced Optimizations Additional course details: Nexus Humans KM404 IBM InfoSphere Advanced DataStage - Parallel Framework (v11.5) 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 KM404 IBM InfoSphere Advanced DataStage - Parallel Framework (v11.5) 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 for analysts, developers, and administrators of IBM Watson Explorer Deep Analytics Edition oneWEX. Overview Identify oneWEX platformsIdentify the process and data flows of oneWEX projectsExplore the oneWEX user interfaceExplain ingestion and conversionUtilize Content MinerDefine enrichmentIdentify advanced features of oneWEX This course is designed to teach students core concepts of IBM Watson Explorer Deep Analytics Edition oneWEX. Students will learn to identify the oneWEX platforms as well as the process flow and data flow of oneWEX projects. Students will explore oneWEX tools, such as Content Miner and the Admin Console, while gaining hands-on experience in data acquisition and enrichment. Finally, students will be exposed to more advanced topics, such as Application Builder, Content Analytics Studio, and API usage. Overview of oneWEX Introduction to oneWEX Explore oneWEX architecture Identify installation options Navigation in oneWEX Explore the Admin Console Explore navigation using Content Miner The Collection detail view The REST API Data flow Explore the data flow of oneWEX Search and Analytics collection templates Identify data acquisition Data ingestion Work with datasets Work with crawlers Use an importer Explore conversion Data ingestion log files Analysis using oneWEX Content Miner Explore analysis using Content Miner The Guided Analysis Experience The Guided Analysis view Explore Annotators Enrichment using Annotators Annotator types Enrichment using Labeler Identify enrichment Identify document classification Classify using training data Classification versus clustering The document classification process Enrichment using Ranker Identify enrichment using Ranker The ranking process Migrate annotators from Content Analytics Studio Migrate Content Analytics Studio annotators Identify the UIMA pipeline configuration for oneWEX Update annotators Using Application Builder with oneWEX Application Builder and user roles Explore Application Builder Set up a oneWEX data source Functionality for oneWEX data sources Additional course details: Nexus Humans O3201 Fundamentals of IBM Watson Explorer Deep Analytics Edition oneWEX (V12.0.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 O3201 Fundamentals of IBM Watson Explorer Deep Analytics Edition oneWEX (V12.0.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 1 Days 6 CPD hours This course is intended for This course is intended for: Developers System Administrators Solutions Architects Overview This course is designed to teach you how to: Design a microservices-based architecture that uses containers Use Amazon ECS to run and scale a microservices-based application Integrate Amazon ECS with other AWS services Running Container-Enabled Microservices on AWS is designed to teach you how to manage and scale container-enabled applications by using Amazon Elastic Container Service (ECS). This course highlights the challenges of running containerized applications at scale and provides guidance on creating and using Amazon ECS to develop and deploy containerized microservices-based applications. In the hands-on lab exercises you will use Amazon ECS to handle long-running services, build and deploy container images, link services together, and scale capacity to meet demand. You will also learn how to run container workers for asynchronous application processes. Module 1a: Overview of Microservices on AWS Welcome to Simple Mustache Service! The monolith What are microservices? How to implement a microservices infrastructure The six principles of microservices Module 1b: Containers and Docker Introduction to containers Comparing virtual machines with containers Docker Running containers Storing container images Hands-on lab: Building and running your first container Module 2: Continuous delivery for container-based microservices Compare and contrast different software development cycles Use AWS CodePipeline to code, build, and deploy a microservice Use AWS CodeCommit as a source control service Use Jenkins to perform a Docker build Use Postman to run and test microservices Use AWS CloudFormation to provision and deploy microservices Hands-on lab: Using the Amazon ECS Service Scheduler Module 3: High availability and scaling with Amazon Elastic Container Service High availability Cluster management and scheduling Monitoring Scaling a cluster Scaling services Hands-on lab: Continuous delivery pipelines for container-based microservices Module 4: Security for container-based microservices Implement security Apply best practices Automate security Evaluate compliance requirements Embed security into the CI/CD Hands-on lab: Extending Amazon ECS with Service Discovery and Config Management Additional course details: Nexus Humans Running Container Enabled Microservices on AWS 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 Running Container Enabled Microservices on AWS 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 primary audience for this course are Application Consultants, Business Process Architects, Business Process Owners/Team Leads/Power Users, Program/Project Managers, and Users. Overview Teach participants the basic functions of inventory management and physical inventory, and how to create different goods movements in the SAP system.Familiarize participants with the special functions of inventory management and physical inventory, and the Customizing settings relevant to inventory management and physical inventory. This course will give students an overview and in depth knowledge of the inventory management process in SAP. After attending this class, students will understand the major business processes, functions & configuration in inventory management. Introduction to Inventory Management Defining Inventory Management and Physical Inventory Moving Goods with the MIGO Transaction Goods Receipts (GRs) Posting a GR Without Reference Posting a GR with Reference Posting a GR with Automatic Generation of a PO Posting Cancellations, Return Deliveries, and Returns Applying Order Price Unit in Inventory Management Applying Tolerances and the Delivery Completed Indicator Adding Date Checks to GRs for a PO Stock Transfers and Transfer Postings Performing Transfer Postings Performing Stock Transfers Between Storage Locations Performing Stock Transfers Between Plants Reservations Creating Manual Reservations Evaluating Reservations Checking Availability and Missing Parts Goods Issues (GIs) Posting Unplanned GIs Posting a GI with Reference Configuring Stock Determination Allowing Negative Stock Special Forms of Procurement and Special Stocks Performing the Process of Subcontracting Performing Vendor Consignment Operating Pipeline Processes Posting Goods Movements for Special Stocks Special Features of Material Valuation Posting Goods Movements for Material Types UNBW and NLAG Configuring Split Valuation Physical Inventory Conducting Physical Inventory Analyzing the Determination of Book Inventory Simplifying Physical Inventory Applying Cycle Counting Applying Inventory Sampling Analyses Applying Stock Lists and Document Lists Applying Periodic Processing Customizing Implementing Settings for Documents Setting Up Movement Types Applying Other Customizing Settings Logistics Execution Managing Logistics Execution Processes Posting a GR Against a PO Posting a GI for a Sales Order Additional course details: Nexus Humans SCM510 SAP Inventory Management and Physical Inventory 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 SCM510 SAP Inventory Management and Physical Inventory 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 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 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 5 Days 30 CPD hours This course is intended for This course is intended for administrators in a Microsoft-centric environment who want to build reusable units of automation, automate business processes, and enable less-technical colleagues to accomplish administrative tasks. Overview Describe the correct patterns for building modularized tools in Windows PowerShell Build highly modularized functions that comply with native PowerShell patterns Build controller scripts that expose user interfaces and automate business processes Manage data in a variety of formats Write automated tests for tools Debug tools This course covers advanced Windows PowerShell topics, with an emphasis on building reusable tools. Students are introduced to workflow, engage in best practices, and learn a variety of script development and toolmaking techniques. Tool Design Tools do one thing Tools are flexible Tools look native Start with a Command Why start with a command? Discovery and experimentation Build a Basic Function and Module Start with a basic function Create a script module Check prerequisites Run the new command Adding CmdletBinding and Parameterizing About CmdletBinding and common parameters Accepting pipeline input Mandatory-ness Parameter validation Parmeter aliases Emitting Objects as Output Assembling information Constructing and emitting output Quick tests An Interlude: Changing Your Approach Examining a script Critiquing a script Revising the script Using Verbose, Warning, and Informational Output Knowing the six channels Adding verbose and warning output Doing more with verbose output Informational output Comment-Based Help Where to put your help Getting started Going further with comment-based help Broken help Handling Errors Understanding errors and exceptions Bad handling Two reasons for exception handling Handling exceptions in our tool Capturing the actual exception Handling exceptions for non-commands Going further with exception handling Deprecated exception handling Basic Debugging Two kinds of bugs The ultimate goal of debugging Developing assumptions Write-Debug Set-PSBreakpoint The PowerShell ISE Going Deeper with Parameters Parameter positions Validation Multiple parameter sets Value from remaining arguments Help messages Aliases More CmdletBinding Writing Full Help External help Using PlatyPs Supporting online help ?About? topics Making your help updatable Unit Testing Your Code Sketching out the test Making something to test Expanding the test Going further with Pester Extending Output Types Understanding types The Extensible Type System Extending an object Using Update-TypeData Analyzing Your Script Performing a basic analysis Analyzing the analysis Publishing Your Tools Begin with a manifest Publishing to PowerShell Gallery Publishing to private repositories Basic Controllers: Automation Scripts and Menus Building a menu Using UIChoice Writing a process controller Proxy Functions A proxy example Creating the proxy base Modifying the proxy Adding or removing parameters Working with XML Data Simple: CliXML Importing native XML ConvertTo-XML Creating native XML from scratch Working with JSON Data Converting to JSON Converting from JSON Working with SQL Server Data SQL Server terminology and facts Connecting to the server and database Writing a query Running a query Invoke-SqlCmd Thinking about tool design patterns Design tools that use SQL Server for data storage Final Exam Lab problem Break down the problem Do the design Test the commands Code the tool

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 3 Days 18 CPD hours This course is intended for The ideal audience for the RPA and UiPath Boot Camp is beginners in the field of RPA and individuals in roles such as developers, project managers, operation analysts, and tech enthusiasts looking to familiarize themselves with automation technologies. It's also perfectly suited for business professionals keen on understanding and implementing automated solutions within their organizations to optimize processes. Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Automation Learning expert instructor, students will explore: Gain a thorough understanding of Robotic Process Automation (RPA) and its applications using UiPath, setting a solid foundation for future learning and application. Learn to record and play in UiPath Studio, a key skill that enables automating complex tasks in a user-friendly environment. Master the art of designing and controlling workflows using Sequencing, Flowcharting, and Control Flow, helping to streamline and manage automation processes effectively. Acquire practical skills in data manipulation, from variable management to CSV/Excel and data table conversions, empowering you to handle data-rich tasks with confidence. Develop competence in managing controls and exploring various plugins and extensions, providing a broader toolkit for handling diverse automation projects. Get hands-on experience with exception handling, debugging, logging, code management, and bot deployment, fundamental skills that ensure your automated processes are reliable and efficient. How to deploy and control Bots with UiPath Orchestrator The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. What is Robotic Process Automation? Scope and techniques of automation Robotic process automation About UiPath The future of automation Record and Play UiPath stack Downloading and installing UiPath Studio Learning UiPath Studio Task recorder Step-by-step examples using the recorder Sequence, Flowchart, and Control Flow Sequencing the workflow Activities Control flow, various types of loops, and decision making Step-by-step example using Sequence and Flowchart Step-by-step example using Sequence and Control flow Data Manipulation Variables and scope Collections Arguments ? Purpose and use Data table usage with examples Clipboard management File operation with step-by-step example CSV/Excel to data table and vice versa (with a step-by-step example) Taking Control of the Controls Finding and attaching windows Finding the control Techniques for waiting for a control Act on controls ? mouse and keyboard activities Working with UiExplorer Handling events Revisit recorder Screen Scraping When to use OCR Types of OCR available How to use OCR Avoiding typical failure points Tame that Application with Plugins and Extensions Terminal plugin SAP automation Java plugin Citrix automation Mail plugin PDF plugin Web integration Excel and Word plugins Credential management Extensions ? Java, Chrome, Firefox, and Silverlight Handling User Events and Assistant Bots What are assistant bots? Monitoring system event triggers Monitoring image and element triggers Launching an assistant bot on a keyboard event Exception Handling, Debugging, and Logging Exception handling Common exceptions and ways to handle them Logging and taking screenshots Debugging techniques Collecting crash dumps Error reporting Managing and Maintaining the Code Project organization Nesting workflows Reusability of workflows Commenting techniques State Machine When to use Flowcharts, State Machines, or Sequences Using config files and examples of a config file Integrating a TFS server Deploying and Maintaining the Bot Publishing using publish utility Overview of Orchestration Server Using Orchestration Server to control bots Using Orchestration Server to deploy bots License management Publishing and managing updates

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course is as follows: Network Video Engineer Voice/UC/Collaboration/Communications Engineer Collaboration Tools Engineer Collaboration Sales/Systems Engineer Overview Implement and Configure Cisco Endpoints Implement and Configure Cisco Unified Communications Manager for Video Endpoints Implement and Configure Cisco IMP for Jabber Implement and Configure Cisco Expressway Implement and Configure Cisco Video Communications Server (VCS) Control Implement and Configure Cisco Video Communications Server (VCS) Expressway Implement and Configure connectivity between a Cisco VCS and Cisco UCM Implement and Configure a Cisco Conductor Implement, Configure, and Operate Cisco TelePresence Management Suite (TMS) and provision devices using Cisco TMS Provisioning Extension (TMSPE) Implement and Configure Cisco Meeting Server This one of kind Cisco Collaboration in-depth course takes student from initial endpoint configuration to a full solution deployment using all of the Cisco Collaboration Components. Students will have extensive labs in which they will configure and troubleshoot a full Cisco Telepresence Deployment. Participants will gain in-depth practical knowledge with exercises on installing, configuring, maintaining and troubleshooting of all Cisco Telepresence Components.The software applications that are covered in this course include:Cisco Unified Communications Manager (CUCM)Cisco Unified Communications Manager IM and PresenceCisco ExpresswayCisco Video Communication Server (VCS)Cisco ConductorCisco Telepresence Server (TS)Cisco Meeting Server (CMS)Cisco Telepresence Management Suite (TMS)Cisco TMS Extensions Understanding Cisco TelePresence Endpoints Cisco TelePresence Endpoint Solutions Overview Cisco DX Series Endpoint Characteristics Cisco TelePresence TC Software-Based Endpoint Characteristics Cisco TelePresence EX60 and EX90 Cisco TelePresence MX Series Cisco TelePresence SX Series Endpoints Cisco TelePresence Room Kits Cisco Intelligent Proximity Cisco TelePresence TC Software-Based Endpoint Peripherals Cisco TelePresence TC Software-Based Endpoint Deployments Cisco TelePresence Integrator C Series-Based Endpoints Cisco Jabber Video for TelePresence Characteristics Cisco Jabber Understanding the Cisco Video Network Network Overview H323 Overview SIP Overview VCS vs Unified Communications Manager Internetworking QOS Overview Implementing and Configuring Unified Communication Manager Unified Communications Manager Initial Settings Service Activation Enterprise and Service Parameters SIP Domains Configure IMP for Jabber Configure Jabber Settings Registering Endpoints Verifying Network Registration Cisco VCS / Expressway Overview and Configuration Configuring Initial VCS Setting Configuring Authentication Registration Conflict Policy Registration Restriction Policy Local Zone Components Membership Rules Transforms Purpose of Zones Search Priority Neighbor Zones Creating Neighbor Zones Need for Traversal Zones H.460 and Assent Protocols Traversal Zone Configuration Domain Name System Dialing E.164 Number Mapping Bandwidth Management Pipes Subzones and Zones Subzone Call Failure Call Processing Order Certificates Certificate Installation Clustering and the Cisco VCS Clustering Configuration Collecting Information Log Files Search History Connecting VCS-C to CUCM Connect VCS-C to VCS-E Connecting CUCM to VCS-C Understanding the Cisco TelePresence Conductor What is Conductor? Conductor ? How it works Installing Cisco Conductor Configuring Conductor Network Settings Configuring Conductor for a CUCM Configuration MCU Configuration Telepresence Server Telepresence Conductor Server Configuration Configuring TelePresence Conductor for ad hoc conferences Configuring TelePresence Conductor for rendezvous conferences Configuring Unified CM Configuring general settings on Unified CM Locations in Unified CM Unified CM trusts TelePresence Conductor's server certificate SIP trunk security profile Configuring Unified CM for rendezvous conferences Sip Trunk Configuration Conference Bridge Configuration MRG and MRGL Configuration Unified CM normalization scripts Configuring Unified CM for ad hoc conferences Sip Trunk Configuration Route pattern to match the SIP Trunk Configuration Unified CM normalization scripts Configuring the Cisco VCS with TelePresence Conductor Designing a Dial Plan MCU Configuration Telepresence Server Configuring the Cisco VCS Telepresence Conductor Server Configuration Backing Up Conductor Troubleshooting Conductor Overview of Cisco Telepresence Management Server (TMS) Windows Server Installation SQL Server Installation Server Pre-requisites and configuration Installing TMS Installing TMS Provisioning Extensions Initial Configuration Endpoint Configuration Adding Systems to TMS Configuration Templates Setting Configuration VCS Configuration for TMS Direct Endpoint Management VCS/TMS CUCM Configuration for TMS Direct Endpoint Management?CUCM Phonebooks & Phonebook Sources Conference Creation Advanced Conference Settings Booking & Scheduling Participant Types Methods Conference Monitoring Dial Plans, Configuration Templates Scheduler/Smart Scheduler Managing and Troubleshooting Cisco TMS Using the Logs Cisco TMS Ticketing System Troubleshooting VCS Registrations Troubleshooting CUCM Registrations System Maintenance Configuring Cisco Meeting Server (formerly Acano) Setting up CMS Installing Certificates Configuring CUCM with CMS Provision the RTC Client Configuring Meeting Spaces Additional course details: Nexus Humans Advanced TP-CT - Implementing and Configuring Cisco TelePresence Video Suite 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 Advanced TP-CT - Implementing and Configuring Cisco TelePresence Video Suite 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.