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Duration 3 Days 18 CPD hours This course is intended for This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. 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. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.

Advanced Adobe InDesign Training Program Learn InDesign with a course at Real Animation Works. Choose from Weekend, Weekday or Evening Courses and learn from expert tutors. Benefit from professional InDesign training conducted by Adobe Certified Instructors with extensive graphic design expertise. Check our Website Duration: 10 hours. Approach: 1-on-1 and personalized attention. Schedule: 1-on-1 sessions, available Monday to Saturday from 9 am to 7 pm.   Course Title: Comprehensive Adobe InDesign Training Duration: 10 Hours Session 1: Introduction to Adobe InDesign (1 hour) Overview of InDesign interface and tools Document setup: page size, margins, and columns Basic text formatting and paragraph styles Introduction to working with images and graphics Session 2: Advanced Text Formatting and Styles (1 hour) In-depth exploration of character and paragraph styles Advanced text composition techniques Managing text flow with threaded frames Incorporating special characters for typographic control Session 3: Mastering Images and Graphics (1 hour) Advanced image manipulation: resizing, cropping, and effects Text wrap options and integrating text with images Creating image frames and working with transparency Interactive elements: buttons and hyperlinks Session 4: Layout Design Techniques (1.5 hours) Grids and guides: precise alignment and spacing Working with layers for efficient design management Advanced object arrangement and distribution Utilizing master pages for consistent layout elements Session 5: Advanced Document Features (1.5 hours) Tables and data merge: organizing and automating data Interactive PDFs: forms, multimedia, and navigation Advanced print preparation: color management and preflighting Exporting for various digital and print outputs Session 6: Advanced Special Effects (1 hour) Creating drop shadows, gradients, and blending modes Working with typography on a path Advanced text and image effects Integrating Adobe Illustrator and Photoshop files Session 7: Project-Based Learning (1 hour) Participants work on a comprehensive project applying learned skills Instructor-guided project review and feedback Session 8: Tips, Tricks, and Time-Saving Techniques (1 hour) Productivity hacks and shortcuts Troubleshooting common issues and errors Best practices for efficient workflow and collaboration Session 9: Portfolio Building and Career Guidance (0.5 hour) Creating a professional portfolio showcasing InDesign projects Career advice and industry insights from the instructor Session 10: Q&A, Certification, and Course Completion (0.5 hour) Addressing participant questions and concerns Certificate of Completion distribution and course review Celebrating the completion of the Adobe InDesign training journey Upon completion of the Comprehensive Adobe InDesign Training course, participants will: Master Core Skills: Develop proficiency in essential InDesign tools, functions, and techniques for effective layout design. Advanced Text and Typography: Understand advanced text formatting, paragraph styles, and typographic controls for professional typography. Image Manipulation Expertise: Acquire skills in advanced image manipulation, text wrapping, transparency, and integration of multimedia elements. Advanced Layout Design: Learn precise layout techniques using grids, guides, layers, and master pages for consistency and visual appeal. Interactive Document Creation: Create interactive PDFs, forms, multimedia-rich content, and navigation elements for digital publications. Data Management and Automation: Master tables, data merge, and automation features for organized data presentation and streamlined workflow. Print and Export Proficiency: Understand color management, preflighting, and export settings for high-quality print and digital output. Special Effects and Integration: Apply advanced effects, gradients, blending modes, and integrate InDesign with Illustrator and Photoshop files seamlessly. Project-Based Expertise: Develop a comprehensive portfolio-worthy project, showcasing a range of InDesign skills and creativity. Efficient Workflow and Troubleshooting: Implement time-saving techniques, shortcuts, and troubleshoot common design challenges effectively. Career Readiness: Gain valuable insights into industry practices, portfolio building, and career guidance for pursuing opportunities in graphic design and desktop publishing. Versatile Learning Choices: Opt for either in-person sessions at our London center or engage in interactive online learning. Both options offer hands-on experience, detailed demonstrations, and ample chances for inquiries. Compatibility and Assistance: InDesign operates smoothly on Windows and Mac systems. Participants receive a comprehensive InDesign training manual for reference and an electronic certificate upon course completion. Additionally, enjoy lifelong email assistance from your InDesign instructor. Entry Requirements: No prior InDesign expertise is necessary. The training concentrates on InDesign 2023, relevant to recent software updates. Guarantees: We ensure exceptional value for your investment, guaranteeing your acquisition of essential skills and concepts during the training. Course Highlights: Master advanced typography techniques, including paragraph styles, character styles, and nested styles. Explore multi-page layout design, long document management, and advanced table formatting. Acquire skills to create and manipulate complex shapes, vector graphics, and custom illustrations. Learn efficient workflows for data merging, interactive documents, and digital/print output. Collaborate seamlessly with other Adobe Creative Cloud applications. Upon completion, receive a Certificate of Completion and access recorded lessons for self-paced learning. Expert Instruction: Learn from certified tutors and industry experts, gaining valuable insights, tips, and best practices for professional-level designs. Flexible Learning Options: Choose between in-person or live online sessions based on your schedule. Sessions are available Monday to Sunday, from 9 am to 8 pm, accommodating your convenience. Lifetime Support: Benefit from lifetime email support for continuous assistance. Our dedicated team is available to address your queries and challenges. Explore Adobe InDesign - Free Trial:  https://www.adobe.com/uk/products/indesign/free-trial-download.html

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 Ideal candidates are IT Professionals who deploy small-to-medium scale enterprise network solutions based on Aruba products and technologies. Overview Network Fundamentals Review Aruba Switching portfolio ArubaOS-CX Network Operating System VLANs Spanning Tree Protocol VRRP Link Aggregation IP Routing Subnetting OSPFv2 - Single Area Stacking using VSF Secure Management and Maintenance Aruba NetEdit Teaches you the fundamental skills necessary to configure and manage modern, open standards-based networking solutions using Aruba's OS-CX routing & switching technologies. This course consists of approximately 60% lecture and 40% hands-on lab exercises Network Fundamentals What is a network? What is a Protocol? OSI Reference Model Encapsulation, frames, packets, segments Layer 2 to Layer 7 headers Media, cabling, Ethernet/wifi headers Binary/Hex/Decimal theory and conversion TCP/IP Stack (IP addressing & Transport Protocols TCP/UDP) Types of traffic: Unicast, Broadcast, Multicast TCP/IP Stack Overview Ethernet frames IPv4 Header TCP Header ? Three-way Handshake TCP Header ? Sequence Numbers TCP Header ? Port Numbers TCP Header UPD Header Basic Networking with Aruba Solutions Networking devices: Switches, Routers, Multilayer Switches, APs, Mobility Controllers, Firewalls, Servers (HTTP, DHCP, DNS, Telnet, FTP) 2-Tier vs 3-Tier hierarchy Switching Portfolio (AOS switches & AOS-CX switches) is this introducing both portfolio on a couple of slide and few slides on AOS-CX hardware architecture, software architecture and intro to NAE high level. Introduction to AOS-CX and feature set Port numbering Accessing Aruba OS-CX CLI Prompt modes/levels and navigation Context sensitive help Show logs, configuration, interfaces, transceivers, flash, version Hostname/interface name, enabling interfaces Link Layer Discovery Protocol ICMP and reachability testing tools: Ping and Traceroute PoE (standards one slide and what we support and one or two slide on configuration VLANs Broadcast/collision domains VLAN benefits VLAN creation DHCP server configuration in switches (optional) 802.1Q tagging Switchports vs. Routed ports MAC address table ARP table Packet Delivery part 1 Spanning Tree Protocol Redundant network L2 loops 802.1D Common Spanning Tree 802.1s 802.1w overview 802.1w load balancing 802.1w region configuration Link Aggregation Static Aggregation LACP Load Balancing IP Routing - Part 1 Default Gateway DHCP IP Helper Address IP Routing Service Inter-VLAN routing Packet Delivery Part 2 Need for layer 3 redundancy Introduction to VRF VRRP VRRP overview VRRP basic operation VRRP failover and preempt VRRP and MSTP coordination IP Routing - Part 2 Subnetting CIDR Static routes Administrative Distance Floating routes Scalability issues IP Routing - Part 3 IGP vs EGP Distance Vector vs Link State OSPF Router-ID and Hello Messages Passive interfaces States DR and BDR LSDB: LSA 1 and 2 Path selection and convergence Using cost to manipulate routes Stacking Control Plane, Management Plane, and Data Plane Introduction to Stacking technologies Stacking Benefits Centralized control and management plane Distributed Data Plane and Distributed Link Aggregation VSF VSF requirements VSF Link and member roles VSF member IDs and port numbers VSF Configuration VSF Provisioning use cases Tracing Layer 2 traffic: Unicast Tracing Layer 2 traffic: Broadcast, Multicast, and Unknown Unicast VSF Failover and OSFP Graceful-Restart VSF Link failure without MAD MAD VSX Introduction Secure Management and Maintenance OOBM port Management VRF Secure Management Protocols: AAA, SSH, HTTPS, RBAC Radius-based management auth (VSA) SNMP Web interface Configuration file management (Backup, restore, checkpoint and roll back) Operating System image management (backup and restore) Factory default/password recovery AOS-CX Management tools Intro to NetEdit NetEdit installation Basic monitoring with NetEdit AOS-CX Mobile App

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts 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. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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 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 Ideal candidates are IT Professionals who deploy small-to-medium scale enterprise network solutions based on Aruba products and technologies. Overview After you successfully complete this course, expect to be able to: Explain Networking Fundamentals Describe and review the Aruba Switching portfolio with customers Install and configure devices running the ArubaOS-CX Network Operating System Describe and configure VLANs Explain, describe and configure Spanning Tree Protocol Understand when to use VRRP and how to configure it Explain and configure Link Aggregation Understand and configure IP Routing Explain IP Subnetting Understand and configure OSPFv2 - Single Area Describe and configure Switch Stacking using VSF Configuration of Aruba solutions using Secure Management and Maintenance methodologies Manage, monitor, administer and operate Aruba solutions using Aruba's NetEdit tool This course teaches you the fundamental skills necessary to configure and manage modern, open standards-based networking solutions using Aruba's OS-CX routing and switching technologies. This course consists of approximately 60% lecture and 40% hands-on lab exercises to help you learn how to implement and validate small to medium enterprise network solutions. This 5-day course prepares candidates for the Aruba Certified Switching Associate exam.In this course, participants learn about ArubaOS-CX switch technologies including multi-layer switches. You will also learn about broadcast domains and Virtual Local Area Networks (VLANs); secure management protocols such as AAA, SSH, HTTPS, and Dynamic Segmentation using Aruba's Role-Based Access Control (RBAC); availability technologies such as Multiple Spanning Tree Protocol (MSTP); link aggregation techniques including Link Aggregation Control Protocol (LACP) and switch virtualization with Aruba?s Virtual Switching Framework (VSF). Static and dynamic IP routing protocols such as Open Shortest Path First (OSPF) are also covered. Network Fundamentals What is a network? What is a Protocol? OSI Reference Model Encapsulation, frames, packets, segments Layer 2 to Layer 7 headers Media, cabling, Ethernet/wifi headers Binary/Hex/Decimal theory and conversion TCP/IP Stack (IP addressing & Transport Protocols TCP/UDP) Types of traffic: Unicast, Broadcast, Multicast TCP/IP Stack Overview Ethernet frames IPv4 Header TCP Header ? Three-way Handshake TCP Header ? Sequence Numbers TCP Header ? Port Numbers TCP Header UPD Header Basic Networking with Aruba Solutions Networking devices: Switches, Routers, Multilayer Switches, APs, Mobility Controllers, Firewalls, Servers (HTTP, DHCP, DNS, Telnet, FTP) 2-Tier vs 3-Tier hierarchy Switching Portfolio (AOS switches & AOS-CX switches) is this introducing both portfolio on a couple of slide and few slides on AOS-CX hardware architecture, software architecture and intro to NAE high level. Introduction to AOS-CX and feature set Port numbering Accessing Aruba OS-CX CLI Prompt modes/levels and navigation Context sensitive help Show logs, configuration, interfaces, transceivers, flash, version Hostname/interface name, enabling interfaces Link Layer Discovery Protocol ICMP and reachability testing tools: Ping and Traceroute PoE (standards one slide and what we support and one or two slide on configuration and verifications.) VLANs Broadcast/collision domains VLAN benefits VLAN creation DHCP server configuration in switches (optional) 802.1Q tagging Switchports vs. Routed ports MAC address table ARP table Packet Delivery part 1 Spanning Tree Protocol Redundant network L2 loops 802.1D Common Spanning Tree 802.1s 802.1w overview 802.1w load balancing 802.1w region configuration Link Aggregation Static Aggregation LACP Load Balancing IP Routing - Part 1 Default Gateway DHCP IP Helper Address IP Routing Service Inter-VLAN routing Packet Delivery Part 2 Need for layer 3 redundancy Introduction to VRF VRRP VRRP overview VRRP basic operation VRRP failover and preempt VRRP and MSTP coordination IP Routing - Part 2 Subnetting CIDR Static routes Administrative Distance Floating routes Scalability issues IP Routing - Part 3 IGP vs EGP Distance Vector vs Link State OSPF Router-ID and Hello Messages Passive interfaces States DR and BDR LSDB: LSA 1 and 2 Path selection and convergence Using cost to manipulate routes Stacking Control Plane, Management Plane, and Data Plane Introduction to Stacking technologies Stacking Benefits Centralized control and management plane Distributed Data Plane and Distributed Link Aggregation VSF VSF requirements VSF Link and member roles VSF member IDs and port numbers VSF Configuration VSF Provisioning use cases Tracing Layer 2 traffic: Unicast Tracing Layer 2 traffic: Broadcast, Multicast, and Unknown Unicast VSF Failover and OSFP Graceful-Restart VSF Link failure without MAD MAD VSX Introduction Secure Management and Maintenance OOBM port Management VRF Secure Management Protocols: AAA, SSH, HTTPS, RBAC Radius-based management auth (VSA) SNMP Web interface Configuration file management (Backup, restore, checkpoint and roll back) Operating System image management (backup and restore) Factory default/password recovery AOS-CX Management tools Intro to NetEdit NetEdit installation Basic monitoring with NetEdit AOS-CX Mobile App Additional course details: Nexus Humans Aruba OS-CX Switching Fundamentals, Rev. 20.21 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 Aruba OS-CX Switching Fundamentals, Rev. 20.21 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 4 Days 24 CPD hours This course is intended for This is an introductory-level C++ programming course designed for developers with experience programming in C or other languages. Practical hands-on prior programming experience and knowledge is required. Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, designed to train attendees in basic coding with C++, coupling the most current, effective techniques with the soundest industry practices. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn: Writing procedural programs using C++ Using private, public and protected keywords to control access to class members Defining a class in C++ Writing constructors and destructors Writing classes with const and static class members Overloading operators Implementing polymorphic methods in programs Writing programs using file I/O and string streams Using manipulators and stream flags to format output Using the keyword template to write generic functions and classes Writing programs that use generic classes and functions Writing programs that use algorithms and containers of the Standard Library Apply object-oriented design techniques to real-world programming problems Using algorithms and containers of the Standard Library to manipulate string data Understand how C++ protects the programmer from implementation changes in other modules of an application Using try() blocks to trap exceptions Using catch() blocks to handle exceptions Defining exceptions and using throw to trigger them Introduction to C++ Programming / C++ Essentials is a skills-focused, hands-on C++ training course geared for experienced programmers who need to learn C++ coupled with sounds coding skills and best practices for OO development. Students will leave this course armed with the required skills to put foundation-level C++ programming skills right to work in a practical environment. The central concepts of C++ syntax and style are taught in the context of using object-oriented methods to achieve reusability, adaptability and reliability. Emphasis is placed on the features of C++ that support abstract data types, inheritance, and polymorphism. Students will learn to apply the process of data abstraction and class design. Practical aspects of C++ programming including efficiency, performance, testing, and reliability considerations are stressed throughout. Comprehensive hands on exercises are integrated throughout to reinforce learning and develop real competency Moving from C to C++ (Optional) New Compiler Directives Stream Console I/O Explicit Operators Standard Libraries Data Control Capabilities Handling Data New Declaration Features Initialization and Assignment Enumerated Types The bool Type Constant Storage Pointers to Constant Storage Constant Pointers References Constant Reference Arguments Volatile Data Global Data Functions Function Prototypes and Type Checking Default Function Data Types Function Overloading Problems with Function Overloading Name Resolution Promotions and Conversions Call by Value Reference Declarations Call-by-Reference and Reference Types References in Function Return Constant Argument Types Conversion of Parameters Using Default Initializers Providing Default Arguments Inline Functions Operator Overloading Advantages and Pitfalls of Overloading Member Operator Syntax and Examples Class Assignment Operators Class Equality Operators Non-Member Operator Overloading Member and Non-Member Operator Functions Operator Precedence This Pointer Overloading the Assignment Operator Overloading Caveats Creating and Using Objects Creating Automatic Objects Creating Dynamic Objects Calling Object Methods Constructors Initializing Member consts Initializer List Syntax Allocating Resources in Constructor Destructors Block and Function Scope File and Global Scope Class Scope Scope Resolution Operator :: Using Objects as Arguments Objects as Function Return Values Constant Methods Containment Relationships Dynamic Memory Management Advantages of Dynamic Memory Allocation Static, Automatic, and Heap Memory Free Store Allocation with new and delete Handling Memory Allocation Errors Controlling Object Creation Object Copying and Copy Constructor Automatic Copy Constructor Conversion Constructor Streaming I/O Streams and the iostream Library Built-in Stream Objects Stream Manipulators Stream Methods Input/Output Operators Character Input String Streams Formatted I/O File Stream I/O Overloading Stream Operators Persistent Objects Introduction to Object Concepts The Object Programming Paradigm Object-Orientated Programming Definitions Information Hiding and Encapsulation Separating Interface and Implementation Classes and Instances of Objects Overloaded Objects and Polymorphism Declaring and Defining Classes Components of a Class Class Structure Class Declaration Syntax Member Data Built-in Operations Constructors and Initialization Initialization vs. Assignment Class Type Members Member Functions and Member Accessibility Inline Member Functions Friend Functions Static Members Modifying Access with a Friend Class Templates Purpose of Template Classes Constants in Templates Templates and Inheritance Container Classes Use of Libraries Strings in C++ Character Strings The String Class Operators on Strings Member Functions of the String Class Inheritance Inheritance and Reuse Composition vs. Inheritance Inheritance: Centralized Code Inheritance: Maintenance and Revision Public, Private and Protected Members Redefining Behavior in Derived Classes Designing Extensible Software Systems Syntax for Public Inheritance Use of Common Pointers Constructors and Initialization Inherited Copy Constructors Destructors and Inheritance Public, Protected, Private Inheritance Exceptions Types of Exceptions Trapping and Handling Exceptions Triggering Exceptions Handling Memory Allocation Errors C++ Program Structure Organizing C++ Source Files Integrating C and C++ Projects Using C in C++ Reliability Considerations in C++ Projects Function Prototypes Strong Type Checking Constant Types C++ Access Control Techniques Polymorphism in C++ Definition of Polymorphism Calling Overridden Methods Upcasting Accessing Overridden Methods Virtual Methods and Dynamic Binding Virtual Destructors Abstract Base Classes and Pure Virtual Methods Multiple Inheritance Derivation from Multiple Base Classes Base Class Ambiguities Virtual Inheritance Virtual Base Classes Virtual Base Class Information The Standard Template Library STL Containers Parameters Used in Container Classes The Vector Class STL Algorithms Use of Libraries

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