22 Natural Language Processing (NLP) courses in Leeds delivered Live Online

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.

Duration 1 Days 6 CPD hours This course is intended for The audience for this course includes software developers and data scientists who need to use large language models for generative AI. Some programming experience is recommended, but the course will be valuable to anyone seeking to understand how the Azure OpenAI service can be used to implement generative AI solutions. Note Generative AI is a fast-evolving field of artificial intelligence, and the Azure OpenAI service is subject to frequent changes. The course materials are maintained to reflect the latest version of the service at the time of writing. Azure OpenAI Service provides access to OpenAI's powerful large language models such as GPT; the model behind the popular ChatGPT service. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio. In this course, you'll learn how to provision Azure OpenAI service, deploy models, and use them in generative AI applications. Prerequisites Familiarity with Azure and the Azure portal. Experience programming with C# or Python. 1 - Get started with Azure OpenAI Service Access Azure OpenAI Service Use Azure OpenAI Studio Explore types of generative AI models Deploy generative AI models Use prompts to get completions from models Test models in Azure OpenAI Studio's playgrounds 2 - Build natural language solutions with Azure OpenAI Service Integrate Azure OpenAI into your app Use Azure OpenAI REST API Use Azure OpenAI SDK 3 - Apply prompt engineering with Azure OpenAI Service Understand prompt engineering Write more effective prompts Provide context to improve accuracy 4 - Generate code with Azure OpenAI Service Construct code from natural language Complete code and assist the development process Fix bugs and improve your code 5 - Generate images with Azure OpenAI Service What is DALL-E? Explore DALL-E in Azure OpenAI Studio Use the Azure OpenAI REST API to consume DALL-E models 6 - Use your own data with Azure OpenAI Service Understand how to use your own data Add your own data source Chat with your model using your own data Additional course details: Nexus Humans AI-050T00: Develop Generative AI Solutions with Azure OpenAI Service training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AI-050T00: Develop Generative AI Solutions with Azure OpenAI Service 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 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

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Duration 5 Days 30 CPD hours This course is intended for A Microsoft Power Platform Functional Consultant is responsible for creating and configuring apps, automations, and solutions. They act as the liaison between users and the implementation team. The functional consultant promotes utilization of solutions within an organization. The functional consultant may perform discovery, engage subject matter experts and stakeholders, capture requirements, and map requirements to features. They implement components of a solution including application enhancements, custom user experiences, system integrations, data conversions, custom process automation, and simple visualizations. This course will teach you to use Microsoft Power Platform solutions to simplify, automate, and empower business processes for organizations in the role of a Functional Consultant. A Microsoft Power Platform Functional Consultant is responsible for creating and configuring apps, automations, and solutions. They act as the liaison between users and the implementation team. The functional consultant promotes utilization of solutions within an organization. The functional consultant may perform discovery, engage subject matter experts and stakeholders, capture requirements, and map requirements to features. They implement components of a solution including application enhancements, custom user experiences, system integrations, data conversions, custom process automation, and simple visualizations. This course may contain a 1-day Applied Workshop. This workshop will allow you to practice your Functional Consultant skills by creating an end-to-end solution to solve a problem for a fictitious company. The solution will include a Microsoft Dataverse database, Power Apps canvas app, and Power Automate flows. Prerequisites Experience as an IT professional or student Working knowledge of Microsoft Power Platform and its key components Knowledge of Microsoft Dataverse (or general data modeling) and security concepts 1 - Describe the business value of the Microsoft Power Platform Explore Microsoft Power Platform Describe the business value of the Power Platform Explore connectors and Microsoft Dataverse Describe how Power Platform works with Microsoft 365 apps and services Explore how Power Platform works with Microsoft Teams Describe how Power Platform works with Microsoft Dynamics 365 apps Describe how Power Platform solutions consume Microsoft Azure services Explore how Microsoft Power Platform apps work together Use Artificial Intelligence to increase productivity 2 - Core components of Power Pages Get started with Power Pages Core tools and components of Power Pages Overview of Power Pages security Overview of Power Pages extensibility 3 - Explore Power Pages templates Site design templates Scenario-based templates Dynamics 365 Power Pages site templates 4 - Explore Power Pages design studio Work with pages Page components Site styling and templates 5 - Explore Power Pages design studio data and security features Data workspace in Power Pages design studio Power Pages security features 6 - Introduction to Power Pages administration Power Pages administrative tools Set up workspace in Power Pages design studio 7 - Get started building with Power BI Use Power BI Building blocks of Power BI Tour and use the Power BI service 8 - Get data with Power BI Desktop Overview of Power BI Desktop Explore Power BI Desktop Connect to data sources Get data from Excel Transform data to include in a report Combine data from multiple sources Clean data to include in a report 9 - Model data in Power BI How to manage your data relationships Create calculated columns Optimize data models Create measures Create calculated tables Explore time-based data 10 - Use visuals in Power BI Create and customize simple visualizations Create slicers Map visualizations Matrices and tables Create scatter, waterfall, and funnel charts Modify colors in charts and visuals Page layout and formatting 11 - Explore data in Power BI Quick insights in Power BI Create and configure a dashboard Ask questions of your data with natural language Create custom Q&A suggestions Share dashboards with your organization Display visuals and tiles in full screen Edit tile details and add widgets Get more space on your dashboard 12 - Publish and share in Power BI Publish Power BI Desktop reports Print and export dashboards and reports Introducing Power BI Mobile Create workspaces in Power BI Build apps Use apps Integrate OneDrive for Business with Power BI Publish to web 13 - Create and manage workspaces in Power BI Distribute a report or dashboard Monitor usage and performance Recommend a development life cycle strategy Troubleshoot data by viewing its lineage Configure data protection 14 - Manage semantic models in Power BI Use a Power BI gateway to connect to on-premises data sources Configure a semantic model scheduled refresh Configure incremental refresh settings Manage and promote semantic models Troubleshoot service connectivity Boost performance with query caching (Premium) 15 - Create dashboards in Power BI Configure data alerts Explore data by asking questions Review Quick insights Add a dashboard theme Pin a live report page to a dashboard Configure a real-time dashboard Set mobile view 16 - Implement row-level security Configure row-level security with the static method Configure row-level security with the dynamic method 17 - Create tables in Dataverse Table characteristics Table relationships Dataverse logic and security Dataverse auditing Dual-write vs. virtual tables 18 - Manage tables in Dataverse Identify tables and table types in Dataverse Create a custom table Enable attachments within a table Licensing requirements for each table type 19 - Create and manage columns within a table in Dataverse Define columns in Microsoft Dataverse Column types in Microsoft Dataverse Add a column to a table Create a primary name column Restrictions that apply to columns in a table Create an auto numbering column Create an alternate key 20 - Create a relationship between tables in Dataverse Relate one or more tables - Introduction Relationship types that are available in Microsoft Dataverse Create a one-to-many relationship between tables Create a many-to-many relationship between tables Edit or delete relationships 21 - Working with choices in Dataverse Define choice column Standard choices column 22 - Get started with security roles in Dataverse Understand environment roles Adding or disabling an environment user Understand security concepts in Dataverse Understand user security roles and security role defaults Check the roles that a user belongs to Configure Dataverse teams for security Configure Dataverse group teams for security 23 - Use administration options for Dataverse Use Microsoft Power Platform Admin Center portal Tenant storage capacity Advanced Customization options in Power Apps Portal Enable and disable auditing 24 - Manage Dynamics 365 model-driven app settings and security Configure role-based security Manage teams and business units Explore settings and customizations 25 - Introduction to Microsoft Power Platform security and governance Identify Microsoft Power Platform environments Data Loss Prevention policies Microsoft Power Platform Center of Excellence Starter Kit 26 - Get started with model-driven apps in Power Apps Introducing model-driven apps Components of model-driven apps Design model-driven apps Incorporate business process flows 27 - Configure forms, charts, and dashboards in model-driven apps Forms overview Form elements Configure multiple forms Use specialized form components Configure views overview Configure grids Create and edit views Configure charts overview Dashboards overview Use interactive streams and tiles 28 - Get started with Power Apps canvas apps Power Apps building blocks Ways to build Power Apps Power Apps related technologies Additional Power Apps related technologies Designing a Power Apps app 29 - Connect to other data in a Power Apps canvas app Overview of the different data sources Work with action-based data sources Power Automate is a companion to Power Apps 30 - How to build the UI in a canvas app in Power Apps Use themes to quickly change the appearance of your app Branding a control Icons Images Personalization Using the tablet or phone form factors 31 - Manage apps in Power Apps Power Apps review 32 - Build your first app with Power Apps and Dataverse for Teams Create your first app with the hero template Customize your app with Power Apps Studio Publish your app Install template apps 33 - Access Dataverse in Power Pages websites Use lists to display multiple Dataverse records Use forms to interact with Dataverse data 34 - Authentication and user management in Power Pages Power Pages authentication settings User registration in Power Pages Authentication management for Power Pages users Power Pages authentication providers 35 - Power Pages maintenance and troubleshooting Power Pages website maintenance Power Pages website troubleshooting 36 - Define and create business rules in Dataverse Define business rules - Introduction Define the components of a business rule Create a business rule 37 - Get started with Power Automate Introducing Power Automate Troubleshoot flows 38 - Use the Admin center to manage environments and data policies in Power Automate Administer flows Export and import flows Learn how to distribute button flows 39 - Use Dataverse triggers and actions in Power Automate Dataverse triggers Query data Create, update, delete, and relate actions 40 - Extend Dataverse with Power Automate Set up a flow and configure its trigger Email Dataverse record Add to-do items Test and run your flow 41 - Introduction to expressions in Power Automate Get started with expressions Notes make things easier Types of functions Write complex expressions 42 - Build your first Power Automate for desktop flow Set up the environment Explore Power Automate for desktop Create your first Power Automate for desktop flow Record Power Automate for desktop actions Edit and test recorded actions 43 - Run a Power Automate for desktop flow in unattended mode Set up an unattended desktop flow Create a new cloud flow that calls an existing flow in unattended mode Perform a test run Best practices 44 - Optimize your business process with process advisor Get familiar with process advisor Create your first recording Edit recordings and group actions Analyze recordings and interpret results Automation recommendations 45 - Get started with Microsoft Copilot Studio bots Get started working with environments Create bots and work with the Microsoft Copilot Studio interface Create topics Test bots Publish bots and analyze performance 46 - Enhance Microsoft Copilot Studio bots Use Power Automate to add actions Transfer conversations to agents by using Omnichannel for Customer Service Create topics for existing support content Analyze bot performance 47 - Manage topics in Microsoft Copilot Studio Work with bot topics Branch a topic Create topics for existing support content Work with system fallback topics Manage topics 48 - Manage Power Virtual Agents Environments in Microsoft Copilot Studio Bot topics permissions Bot entities and flow permissions Monitor and diagnose Administer and manage Export and import bots Authentication 49 - Get started building with Power BI Use Power BI Building blocks of Power BI Tour and use the Power BI service 50 - Get data with Power BI Desktop Overview of Power BI Desktop Explore Power BI Desktop Connect to data sources Get data from Excel Transform data to include in a report Combine data from multiple sources Clean data to include in a report 51 - Model data in Power BI How to manage your data relationships Create calculated columns Optimize data models Create measures Create calculated tables Explore time-based data 52 - Use visuals in Power BI Create and customize simple visualizations Create slicers Map visualizations Matrices and tables Create scatter, waterfall, and funnel charts Modify colors in charts and visuals Page layout and formatting 53 - Explore data in Power BI Quick insights in Power BI Create and configure a dashboard Ask questions of your data with natural language Create custom Q&A suggestions Share dashboards with your organization Display visuals and tiles in full screen Edit tile details and add widgets Get more space on your dashboard 54 - Publish and share in Power BI Publish Power BI Desktop reports Print and export dashboards and reports Introducing Power BI Mobile Create workspaces in Power BI Build apps Use apps Integrate OneDrive for Business with Power BI Publish to web 55 - Manage solutions in Power Apps and Power Automate Add and remove apps, flows, and entities in a solution Edit a solution-aware app, flow, and table Build and deploy a complex solution with flows, apps, and entities Automate solution management 56 - Load/export data and create data views in Dataverse View data in a table Create or edit views of data in a table Load data into a table Export data from a table Add, update, or delete data in a table by using Excel Import data using Power Query Generate a new dataflow from an Excel Template Dataflow and Azure integration 57 - Get started with AI Builder Choose an AI capability Create your first model Ways to use your models 58 - Manage models in AI Builder Model lifecycle Manage model versions Share your models 59 - Use AI Builder in Power Automate AI Builder in Power Automate saves time Advanced usage of AI Builder in Power Automate 60 - Functional Consultant skills Create entity relationship diagrams Create and document mock-ups Document functional requirements and artifacts Complete fit-gap analysis Discuss stakeholder management responsibilities Understand industry accelerators Define Application Lifecycle Management Participate in testing Evaluate options Define connectors Understand Power Apps component framework 61 - Solution Architect series: Plan application lifecycle management for Power Platform Key considerations for ALM Solutions Configuration and reference data Release process ALM with Azure DevOps

Duration 4 Days 24 CPD hours This course is intended for Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure. AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language. Prerequisites Before attending this course, students must have: Knowledge of Microsoft Azure and ability to navigate the Azure portal Knowledge of either C# or Python Familiarity with JSON and REST programming semantics Recommended course prerequisites AI-900T00: Microsoft Azure AI Fundamentals course 1 - Prepare to develop AI solutions on Azure Define artificial intelligence Understand AI-related terms Understand considerations for AI Engineers Understand considerations for responsible AI Understand capabilities of Azure Machine Learning Understand capabilities of Azure AI Services Understand capabilities of the Azure Bot Service Understand capabilities of Azure Cognitive Search 2 - Create and consume Azure AI services Provision an Azure AI services resource Identify endpoints and keys Use a REST API Use an SDK 3 - Secure Azure AI services Consider authentication Implement network security 4 - Monitor Azure AI services Monitor cost Create alerts View metrics Manage diagnostic logging 5 - Deploy Azure AI services in containers Understand containers Use Azure AI services containers 6 - Analyze images Provision an Azure AI Vision resource Analyze an image Generate a smart-cropped thumbnail 7 - Classify images Provision Azure resources for Azure AI Custom Vision Understand image classification Train an image classifier 8 - Detect, analyze, and recognize faces Identify options for face detection analysis and identification Understand considerations for face analysis Detect faces with the Azure AI Vision service Understand capabilities of the face service Compare and match detected faces Implement facial recognition 9 - Read Text in images and documents with the Azure AI Vision Service Explore Azure AI Vision options for reading text Use the Read API 10 - Analyze video Understand Azure Video Indexer capabilities Extract custom insights Use Video Analyzer widgets and APIs 11 - Analyze text with Azure AI Language Provision an Azure AI Language resource Detect language Extract key phrases Analyze sentiment Extract entities Extract linked entities 12 - Build a question answering solution Understand question answering Compare question answering to Azure AI Language understanding Create a knowledge base Implement multi-turn conversation Test and publish a knowledge base Use a knowledge base Improve question answering performance 13 - Build a conversational language understanding model Understand prebuilt capabilities of the Azure AI Language service Understand resources for building a conversational language understanding model Define intents, utterances, and entities Use patterns to differentiate similar utterances Use pre-built entity components Train, test, publish, and review a conversational language understanding model 14 - Create a custom text classification solution Understand types of classification projects Understand how to build text classification projects 15 - Create a custom named entity extraction solution Understand custom named entity recognition Label your data Train and evaluate your model 16 - Translate text with Azure AI Translator service Provision an Azure AI Translator resource Specify translation options Define custom translations 17 - Create speech-enabled apps with Azure AI services Provision an Azure resource for speech Use the Azure AI Speech to Text API Use the text to speech API Configure audio format and voices Use Speech Synthesis Markup Language 18 - Translate speech with the Azure AI Speech service Provision an Azure resource for speech translation Translate speech to text Synthesize translations 19 - Create an Azure AI Search solution Manage capacity Understand search components Understand the indexing process Search an index Apply filtering and sorting Enhance the index 20 - Create a custom skill for Azure AI Search Create a custom skill Add a custom skill to a skillset 21 - Create a knowledge store with Azure AI Search Define projections Define a knowledge store 22 - Plan an Azure AI Document Intelligence solution Understand AI Document Intelligence Plan Azure AI Document Intelligence resources Choose a model type 23 - Use prebuilt Azure AI Document Intelligence models Understand prebuilt models Use the General Document, Read, and Layout models Use financial, ID, and tax models 24 - Extract data from forms with Azure Document Intelligence What is Azure Document Intelligence? Get started with Azure Document Intelligence Train custom models Use Azure Document Intelligence models Use the Azure Document Intelligence Studio 25 - Get started with Azure OpenAI Service Access Azure OpenAI Service Use Azure OpenAI Studio Explore types of generative AI models Deploy generative AI models Use prompts to get completions from models Test models in Azure OpenAI Studio's playgrounds 26 - Build natural language solutions with Azure OpenAI Service Integrate Azure OpenAI into your app Use Azure OpenAI REST API Use Azure OpenAI SDK 27 - Apply prompt engineering with Azure OpenAI Service Understand prompt engineering Write more effective prompts Provide context to improve accuracy 28 - Generate code with Azure OpenAI Service Construct code from natural language Complete code and assist the development process Fix bugs and improve your code 29 - Generate images with Azure OpenAI Service What is DALL-E? Explore DALL-E in Azure OpenAI Studio Use the Azure OpenAI REST API to consume DALL-E models 30 - Use your own data with Azure OpenAI Service Understand how to use your own data Add your own data source Chat with your model using your own data 31 - Fundamentals of Responsible Generative AI Plan a responsible generative AI solution Identify potential harms Measure potential harms Mitigate potential harms Operate a responsible generative AI solution

Duration 1 Days 6 CPD hours This course is intended for The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don?t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful. This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. Prerequisites Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services. Specifically: Experience using computers and the internet. Interest in use cases for AI applications and machine learning models. A willingness to learn through hands-on exp... 1 - Fundamental AI Concepts Understand machine learning Understand computer vision Understand natural language processing Understand document intelligence and knowledge mining Understand generative AI Challenges and risks with AI Understand Responsible AI 2 - Fundamentals of machine learning What is machine learning? Types of machine learning Regression Binary classification Multiclass classification Clustering Deep learning Azure Machine Learning 3 - Fundamentals of Azure AI services AI services on the Azure platform Create Azure AI service resources Use Azure AI services Understand authentication for Azure AI services 4 - Fundamentals of Computer Vision Images and image processing Machine learning for computer vision Azure AI Vision 5 - Fundamentals of Facial Recognition Understand Face analysis Get started with Face analysis on Azure 6 - Fundamentals of optical character recognition Get started with Vision Studio on Azure 7 - Fundamentals of Text Analysis with the Language Service Understand Text Analytics Get started with text analysis 8 - Fundamentals of question answering with the Language Service Understand question answering Get started with the Language service and Azure Bot Service 9 - Fundamentals of conversational language understanding Describe conversational language understanding Get started with conversational language understanding in Azure 10 - Fundamentals of Azure AI Speech Understand speech recognition and synthesis Get started with speech on Azure 11 - Fundamentals of Azure AI Document Intelligence Explore capabilities of document intelligence Get started with receipt analysis on Azure 12 - Fundamentals of Knowledge Mining with Azure Cognitive Search What is Azure Cognitive Search? Identify elements of a search solution Use a skillset to define an enrichment pipeline Understand indexes Use an indexer to build an index Persist enriched data in a knowledge store Create an index in the Azure portal Query data in an Azure Cognitive Search index 13 - Fundamentals of Generative AI What is generative AI? Large language models What is Azure OpenAI? What are copilots? Improve generative AI responses with prompt engineering 14 - Fundamentals of Azure OpenAI Service What is generative AI Describe Azure OpenAI How to use Azure OpenAI Understand OpenAI's natural language capabilities Understand OpenAI code generation capabilities Understand OpenAI's image generation capabilities Describe Azure OpenAI's access and responsible AI policies 15 - Fundamentals of Responsible Generative AI Plan a responsible generative AI solution Identify potential harms Measure potential harms Mitigate potential harms Operate a responsible generative AI solution Additional course details: Nexus Humans AI-900T00 - Microsoft Azure AI Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AI-900T00 - Microsoft Azure AI Fundamentals 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.

This course will allow you to explore the potential of self-service business intelligence using Power BI Desktop to analyse and connect to different sources of data, creating Relationships between those different datasets, Query the data using Shaping and data Modelling, to create Visualizations, and publish Reports to different platforms . Course Objectives At the end of this course you will be able to: Connect to data from different sources. Use the Query Editor Perform Power BI desktop data Shaping and Transformation. Create Power BI desktop Modelling. Create Power BI desktop Visualizations and Reports. ' 1 year email support service Take a closer look at the consistent excellent feedback from our growing corporate clients visiting our site ms-officetraining co uk With more than 20 years experience, we deliver courses on all levels of the Desktop version of Microsoft Office and Office 365; ranging from Beginner, Intermediate, Advanced to the VBA level and Business Intelligence. Our trainers are Microsoft certified professionals with a proven track record with several years experience in delivering public, one to one, tailored and bespoke course Tailored training courses: in in company training, you can choose to run the course exactly as they are outlined by us or we can customise it so that it meets your specific needs. A tailored or bespoke course will follow the standard outline but may be adapted to your specific organisational needs. Please visit our site (ms-officetraining co uk) to get a feel of the excellent feedback our courses have had and look at other courses you might be interested in. Introduction to Power BI Power BI Jargon explained A quick look at Power BI Desktop A quick look at the Power BI service Helpful resources Power BI and Excel Introduction to using Excel data in Power BI Upload Excel data to Power BI Import Power View and Power Pivot to Power BI Getting started with Power BI Desktop Overview of Power BI Desktop Accessing Help and Helpful resources Connect to data sources in Power BI Desktop Shaping and Transforming Data with Query Editor Introduction to the Query Editor Data Sources Power BI Desktop can Connect to Introduction to Steps and M code Combining Data Using Merge and Append Queries Data Type Properties Working with Delimiters Clean and transform your data with the Query Editor Text Specific Transformation Tools Number Specific Transformation Tools Date Specific Transformation Tools Split and Merge columns Creating an Index Column Adding Conditional Columns Columns From Examples Grouping and Aggregating data Pivoting and Unpivoting Using filters Modeling the data Introduction to modeling your data How to manage your data relationships Create calculated columns Optimizing data models Create calculated measures Show Values As and Quick Measures Create calculated tables Explore your time-based data Introduction to DAX DAX calculation types DAX functions Visualizations Introduction to visuals in Power BI Create and customize simple visualizations Modify colors in charts and visuals Shapes, text boxes, and images Page layout and formatting Group interactions among visualizations Visual hierarchies and drill-down Using custom visualizations Create a KPI Visualization Geo-Data and Maps Reports, Publishing and Sharing Introduction to the Power BI service Quick insights in Power BI Create and configure a dashboard Ask questions of your data with natural language Create custom Q&A suggestions Share dashboards with your organization Introduction to content packs, security, and groups Publish Power BI Desktop reports Print and export dashboards and reports Create groups in Power BI Use content packs Update content packs Publish to web Who is this course for? Who is this course for? This course facilitates you with knowledge on the potential for Power BI Desktop to analyse and connect to different sources of data, creating Relationships between those different datasets, Query the data using Shaping and data Modelling, and to create Visualizations, and publish Reports to different platforms. Requirements Requirements Before attending this course, delegates should have: - A general knowledge of database concepts (fields, records and relationships) - Familiarity with Excel. Career path Career path Business Intelligence Data Analysis ETL & Data Warehousing