This half-day workshop aims to provide non-technical business professionals with a foundational understanding of applied AI. The session will introduce key AI concepts, practical applications, and strategic considerations. The goal is to equip participants with the knowledge and confidence to explore AI opportunities within their organisations and explore the comprehensive 8-module Applied AI Micro-Credential course.
Begin your journey into the practical world of AI with our Foundation Certificate in Applied AI. This program is meticulously crafted for those starting in the field or aiming to strengthen
their foundational knowledge in Applied AI. The certificate spans the essentials of AI readiness, data-driven AI strategies, AI strategy development and skill sets, and the AI development pipeline.
Completing this 4 module program will enable you to:
• Understand the fundamental concepts and applications of Applied AI, setting a solid
foundation for further exploration and specialisation.
• Develop key skills to assess AI readiness and integrate AI strategies effectively within various
business contexts.
• Gain essential insights into the AI development process, from conceptualization to
deployment, preparing you for more advanced studies and applications in Applied AI.
Course Objectives - Introduction to AI Readiness and Generative AI - Module 1
Understand the basic concepts and history of AI and Generative AI.
Assess organisational readiness for AI integration.
Develop strategies for AI adoption and fostering an AI-compatible organisational culture.
Begin your journey into the practical world of AI with our Foundation Certificate in Applied AI. This program is meticulously crafted for those starting in the field or aiming to strengthen
their foundational knowledge in Applied AI. The certificate spans the essentials of AI readiness, data-driven AI strategies, AI strategy development and skill sets, and the AI development pipeline.
Completing this 4 module program will enable you to:
• Understand the fundamental concepts and applications of Applied AI, setting a solid
foundation for further exploration and specialisation.
• Develop key skills to assess AI readiness and integrate AI strategies effectively within various business contexts.
• Gain essential insights into the AI development process, from conceptualization to
deployment, preparing you for more advanced studies and applications in Applied AI.
Course Objectives - Harnessing the Power of AI in a Data-Driven Organisation
Comprehend the role of AI in data-driven organisations.
Implement strategies for effective data management and AI-driven decision-making.
Analyse real-world case studies of AI integration in various industries.
Begin your journey into the practical world of AI with our Foundation Certificate in Applied AI. This program is meticulously crafted for those starting in the field or aiming to strengthen
their foundational knowledge in Applied AI. The certificate spans the essentials of AI readiness, data-driven AI strategies, AI strategy development and skill sets, and the AI development pipeline.
Completing this 4 module program will enable you to:
• Understand the fundamental concepts and applications of Applied AI, setting a solid
foundation for further exploration and specialisation.
• Develop key skills to assess AI readiness and integrate AI strategies effectively within various
business contexts.
• Gain essential insights into the AI development process, from conceptualization to
deployment, preparing you for more advanced studies and applications in Applied AI.
Course Objectives - Developing an AI Strategy and Building AI Skill Sets:
Formulate a robust AI strategy aligned with business objectives.
Identify and develop essential AI skills within teams.
Foster a culture of AI literacy and continuous learning.
Begin your journey into the practical world of AI with our Foundation Certificate in Applied AI. This program is meticulously crafted for those starting in the field or aiming to strengthen
their foundational knowledge in Applied AI. The certificate spans the essentials of AI readiness, data-driven AI strategies, AI strategy development and skill sets, and the AI development pipeline.
Completing this 4 module program will enable you to:
• Understand the fundamental concepts and applications of Applied AI, setting a solid
foundation for further exploration and specialisation.
• Develop key skills to assess AI readiness and integrate AI strategies effectively within various
business contexts.
• Gain essential insights into the AI development process, from conceptualization to
deployment, preparing you for more advanced studies and applications in Applied AI.
Course Objectives - AI Development Pipeline: From Conceptualisation to Deployment:
Understand the stages of the AI project development lifecycle.
Learn best practices in AI model development deployment and scaling.
Manage ongoing monitoring and maintenance of AI systems.
Elevate your proficiency in Applied AI with our Practitioner Certificate in Applied AI, designed for individuals who have a solid foundation in AI principles and are ready to advance their skills. This advanced program covers post-AI deployment ethical considerations and AI regulations, applications of AI in NLP, the creative and innovative uses of AI, and techniques for measuring AI impact and ROI.
By completing this 4 module certification program, you will:
• Delve deeper into the ethical, regulatory, and societal impacts of AI, ensuring responsible
and compliant AI implementations.
• Explore specialized applications of AI in natural language processing and creative industries,
enhancing your capabilities in these cutting-edge areas.
• Develop expert skills in evaluating the effectiveness and ROI of AI projects, a critical
competency for any AI practitioner in business and technology sectors.
Course Objectives - Post AI Deployment: Ethical Considerations and AI Regulations:
Grasp the fundamental principles of AI ethics and their societal impact.
Navigate AI regulations and compliance requirements.
Implement ethical AI practices in organisational settings
Elevate your proficiency in Applied AI with our Practitioner Certificate in Applied AI, designed for individuals who have a solid foundation in AI principles and are ready to advance their skills. This advanced program covers post-AI deployment ethical considerations and AI regulations, applications of AI in NLP, the creative and innovative uses of AI, and techniques for measuring AI impact and ROI.
Completing this 4 module certification program will enable you to:
• Delve deeper into the ethical, regulatory, and societal impacts of AI, ensuring responsible
and compliant AI implementations.
• Explore specialised applications of AI in natural language processing and creative industries, enhancing your capabilities in these cutting-edge areas.
• Develop expert skills in evaluating the effectiveness and ROI of AI projects, a critical
competency for any AI practitioner in business and technology sectors.
Course Objectives - AI Applications in Natural Language Processing (NLP)
Understand the foundations and practical applications of NLP in AI.
Explore various NLP applications like chatbots sentiment analysis and machine translation.
Delve into advanced NLP techniques and their role in AI.
Elevate your proficiency in Applied AI with our Practitioner Certificate in Applied AI, designed for individuals who have a solid foundation in AI principles and are ready to advance their skills. This advanced program covers post-AI deployment ethical considerations and AI regulations, applications of AI in NLP, the creative and innovative uses of AI, and techniques for measuring AI impact and ROI.
Completing this 4 module certification program will enable you to:
• Delve deeper into the ethical, regulatory, and societal impacts of AI, ensuring responsible
and compliant AI implementations.
• Explore specialised applications of AI in natural language processing and creative industries, enhancing your capabilities in these cutting-edge areas.
• Develop expert skills in evaluating the effectiveness and ROI of AI projects, a critical
competency for any AI practitioner in business and technology sectors.
Course Objectives - Advanced AI Applications: Creativity and Innovation
Recognise the role of AI in creative fields and innovative product development.
Analyse case studies of AI-driven creativity and innovation in various industries.
Understand AI's impact on enhancing service delivery and customer experience.
Elevate your proficiency in Applied AI with our Practitioner Certificate in Applied AI, designed for individuals who have a solid foundation in AI principles and are ready to advance their skills. This advanced program covers post-AI deployment ethical considerations and AI regulations, applications of AI in NLP, the creative and innovative uses of AI, and techniques for measuring AI impact and ROI.
Completing this 4 module certification program will enable you to:
• Delve deeper into the ethical, regulatory, and societal impacts of AI, ensuring responsible
and compliant AI implementations.
• Explore specialised applications of AI in natural language processing and creative industries, enhancing your capabilities in these cutting-edge areas.
• Develop expert skills in evaluating the effectiveness and ROI of AI projects, a critical
competency for any AI practitioner in business and technology sectors.
Course Objectives - Measuring AI Impact and ROI in Organisational Settings
Learn techniques for evaluating the impact of AI initiatives.
Calculate and track the ROI of AI projects.
Analyse real-world examples of AI-driven business transformations.
Artificial Intelligence (AI) is set to shape the future of software solutions, unlocking the potential to develop extraordinary applications that enhance life for all. This Microsoft Azure AI Fundamentals Training provides an introduction to AI and the Microsoft services available for students to create AI solutions.
Objectives
Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing (NLP) workloads on Azure.
Describe features of conversational AI workloads on Azure.
The Azure OpenAI Service grants access to OpenAI's robust large language models, including GPT, which powers the widely-used ChatGPT service. These models facilitate a range of natural language processing (NLP) solutions, enabling comprehension, conversation, and content generation. Users can utilize the service via REST APIs, SDKs, and Azure OpenAI Studio. This AI Solutions with Azure course will guide you through the process of provisioning the Azure OpenAI service, deploying models, and incorporating them into generative AI applications.
Objectives
Get to know the connection between artificial intelligence (AI), Responsible AI, and text, code, and image generation.
Begin building an Azure OpenAI Service solution.
Begin building apps that integrate with the Azure OpenAI Service.
Understand the concept of prompt engineering and its role in optimising Azure OpenAI models' performance.
In this course, you will learn how to:
Design tailored solutions for analysing business forms through Azure AI Document Intelligence.
Develop solutions for analysing various types of common documents using Document Intelligence.
Implement solutions for analysing diverse custom form types with Document Intelligence.
Learn to amalgamate multiple Document Intelligence models into a composite model for enhanced functionality.
Build a Natural Language Processing Solution with Azure AI Services (AI-3003) is a dynamic one-day course tailored for software developers keen on integrating AI capabilities into their applications utilising Azure AI Services and Azure AI Language. Throughout the course, participants will utilise either C# or Python programming languages to harness the power of Azure AI Language.
In this course, you will learn how to:
Designing solutions for processing natural language with Azure AI Language.
Creating solutions for analysing text using preconfigured features.
Training models for custom language solutions, specifically for question answering and conversational language understanding.
Understanding, synthesising, and translating speech.
Build an Azure AI Vision solution with Azure AI Services (AI-3004) is an immersive one-day course designed for software developers seeking to integrate AI capabilities into their applications using Azure AI Services and Azure AI Language. Throughout the course, participants will utilise either C# or Python programming languages to harness the power of Azure AI Services.
In this course, you will learn how to:
Design solutions for image analysis using prebuilt features.
Create custom vision models for classification and object detection.
Perform optical character recognition (OCR) and extracting insights from video.
Generative Artificial Intelligence (AI) is becoming more accessible through easy-to-use platforms like Azure AI Foundry. Learn how to build generative AI applications that use language models with prompt flow to provide value to your users.
In this learning path, business leaders will find the knowledge and resources to adopt AI in their organizations. It explores planning, strategizing, and scaling AI projects in a responsible way.
Copilots are quickly becoming a popular way to use with AI models, automate tasks, and improve productivity. In this learning path, you'll explore options for adopting, using, and creating copilots.
This learning path explores how the Azure AI and Azure Machine Learning Services integrations provided by the Azure AI extension for Azure Database for PostgreSQL - Flexible Server can enable you to build AI-powered apps.
Acquire the skills to manage machine learning solutions at cloud scale using Azure Machine Learning. This certification course in Azure Data Science guides you in utilizing your existing proficiency in Python and machine learning to oversee tasks such as data ingestion and preparation, model training and deployment, and monitoring machine learning solutions, all within the framework of Azure Machine Learning and MLflow.
The AI-102 Designing and Implementing an Azure AI Solution is designed for software developers aiming to create applications enriched with AI capabilities utilizing Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. This Microsoft Azure AI Solution Training course will utilize either C# or Python as the programming language.
This hands-on Excel data analysis training course enhances your capability to analyze large datasets and professionally present your findings. Acquire the knowledge and skills to develop more advanced spreadsheets, conduct What-If analyses, utilize functions, manipulate PivotTables, and utilize Excel's advanced features for improved business decision-making.
Throughout this Excel data analysis course, you'll employ advanced techniques to construct sophisticated spreadsheets. You'll master conducting ``What-If`` analyses, harnessing features, applying functions, manipulating PivotTables for data analysis, and presenting findings to facilitate better business decisions for planning and budgeting. Additionally, you'll learn how to automate processes and refine worksheet models.
Data science has surged in popularity in recent years, and for good reason. Companies across various industries increasingly rely on data to guide their decision-making, leading to a high demand for skilled data scientists. This comprehensive course provides the foundational skills and techniques needed to thrive in this dynamic field.
You will begin by exploring the role of a data scientist and the lifecycle of data science projects within an organization. Then, you'll develop essential technical skills, such as using Python and its relevant libraries for data analysis and visualization, preprocessing unstructured data, and building AI/ML models.
The course covers key machine learning algorithms, including linear regression, decision tree classifiers, and clustering algorithms. You'll learn to apply these techniques to real-world problems, such as predicting customer churn and building recommendation engines.
Throughout the training, you'll engage in hands-on exercises and projects, allowing you to practice your skills and build your portfolio. By the end of the course, you'll have a thorough understanding of the data science process, the tools and techniques used by data scientists, and the ability to apply these skills to real-world challenges.
In a landscape where an abundance of data is constantly being generated, developers proficient in data analytics and visualization remain highly sought after. Through ``Data Visualization with Python,`` you'll acquire the skills to harness Python alongside NumPy, Pandas, Matplotlib, and Seaborn to craft compelling data visualisations using real-world, publicly available data.
Data Visualization with Python adopts a practical, hands-on approach towards creating impactful data visuals using Python. Through numerous activities embedded within real-life business scenarios, you'll have ample opportunities to practice and implement your newfound skills in a highly relevant context.
Data Visualisation with Python caters to developers and scientists aiming to venture into data science or leverage data visualisations for enhancing their personal and professional projects. While prior experience in data analytics and visualisation is not necessary, a basic understanding of Python and familiarity with high school-level mathematics can be beneficial. Although this course is designed for beginners in data visualisation, seasoned developers can enhance their Python proficiency through hands-on engagement with real-world data.
The ``Data Science Projects with Python`` course aims to familiarize you with the Python environment tailored for data science tasks. It serves as a stepping stone in your quest to become proficient in various machine learning topics. These acquired skills will enable you to develop advanced predictive models, aligning with industry standards and delivering substantial value to businesses across diverse sectors.
``Data Science Projects with Python`` is meticulously crafted to provide practical guidance on utilizing industry-standard data analysis and machine learning tools in Python, utilizing real-world datasets. This course empowers you to grasp how to leverage pandas and Matplotlib to meticulously examine datasets through summary statistics and visualizations, extracting the desired insights effectively.
Continuing your learning journey, you will delve into data preparation techniques and the application of machine learning algorithms, such as regularized logistic regression and random forest, utilizing the scikit-learn package. You will explore techniques for fine-tuning these algorithms to yield optimal predictions on new and unseen data instances.
Advancing through subsequent chapters, you will gain a comprehensive understanding of the inner workings and outputs of these algorithms. This knowledge will not only enhance your predictive modeling capabilities but also enable you to comprehend the rationale behind these predictions.
Upon completing this course, you will possess the skills necessary to proficiently employ various machine learning algorithms for in-depth data analysis and extract actionable insights from datasets with confidence.
Hands on Training
Data Science Projects with Python takes a case study approach to simulate the working conditions you will experience when applying data science and machine learning concepts. You will be presented with a problem and a data set and walked through the steps of defining an answerable question, deciding what analysis methods to use, and implementing all of this in Python to create a deliverable.
An increasing number of enterprises are embracing data science as a pivotal driver for informed business strategies. Irrespective of the industry, the capacity to glean insights from data holds paramount importance in maintaining competitiveness in today's market. Python®, emerging as a frontrunner in data science, stands out with its extensive libraries, empowering data scientists to effortlessly load, analyze, manipulate, cleanse, and visualize data in user-friendly yet robust ways. This course is tailored to equip you with the essential skills to proficiently harness these pivotal libraries, enabling you to derive actionable insights from data, thereby delivering substantial value to the business.
Specialists in Customer Data Platforms implement solutions that offer insights into customer profiles and track engagement activities to enhance customer experiences and boost customer retention. In this Data Platform Specialty course, students will delve into the Dynamics 365 Customer Insights solution. They will learn how to unify customer data using prebuilt connectors, predict customer intent through robust segmentation, and maintain control over customer data. The course commences with the importation and transformation of customer data and concludes by extending the customer data platform solution into the Power Platform and Dynamics 365 applications.
Through Business Central, you can efficiently manage financials, automate and secure your supply chain, enhance sales strategies, improve customer service, optimize project performance, and streamline operations.
The training offers hands-on experience in configuring and deploying Microsoft Teams Phone, meetings, and certified devices, including Microsoft Teams Rooms and Surface Hub. It encompasses the management and monitoring of Teams Phone, meetings, and certified devices using tools such as the Microsoft Teams admin center, PowerShell, the Microsoft Teams Rooms Pro Portal, and the Call Quality Dashboard.
Moreover, the course addresses the configuration and deployment of Microsoft Teams Phone with PSTN connectivity through various methods, including Microsoft Calling Plans, Operator Connect, Teams Phone Mobile, and Direct Routing.
This course covers the fundamentals of Microsoft's version of the standard SQL language known as Transact-SQL. It encompasses both querying and modifying data in relational databases hosted on Microsoft SQL Server-based database systems, which include Microsoft SQL Server, Azure SQL Database, and Azure Synapse Analytics.
This course is beneficial for individuals requiring the ability to write basic SQL or Transact-SQL queries. This audience may include those working with data in roles such as data analysts, data engineers, data scientists, database administrators, or database developers. It is also relevant for individuals peripherally involved with data or those looking to enhance their understanding of working with data, such as solution architects, students, and technology managers.
In the Microsoft Azure Data Fundamentals course, participants will acquire foundational knowledge of essential data concepts and relevant Microsoft Azure data services. The topics covered align with the Microsoft role-based DP-900 certification exam. Students will delve into core data concepts encompassing relational, non-relational, big data, and analytics, building a foundational understanding of cloud data services within Microsoft Azure. The course covers fundamental relational data concepts and relational database services in Azure, explores Azure storage for non-relational data, and introduces the basics of Azure Cosmos DB. Additionally, students will gain insights into modern data warehousing, real-time analytics, and data visualisation.
Within this Azure Data Engineering training, participants will gain the knowledge to implement and oversee data engineering workloads on Microsoft Azure, utilizing services like Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and more. The course emphasizes essential data engineering responsibilities such as orchestrating pipelines for data transfer and transformation, handling data files within a data lake, establishing and loading relational data warehouses, capturing and aggregating real-time data streams, and monitoring data assets and lineage.
Acquire the skills to manage machine learning solutions at cloud scale using Azure Machine Learning. This certification course in Azure Data Science guides you in utilizing your existing proficiency in Python and machine learning to oversee tasks such as data ingestion and preparation, model training and deployment, and monitoring machine learning solutions, all within the framework of Azure Machine Learning and MLflow.
The Power BI Data Analyst Training Course will cover diverse methods and optimal practices aligned with both business and technical requirements for modeling, visualizing, and analyzing data using Power BI. Additionally, the course will demonstrate how to access and process data from a variety of sources, encompassing both relational and non-relational data.
Furthermore, the training will delve into the implementation of robust security standards and policies throughout the Power BI spectrum, covering datasets and groups. Lastly, the course will address the management and deployment of reports and dashboards, facilitating sharing and content distribution.
AI-Assisted Coding: Boosting Developer Productivity and Efficiency is a hands-on, future-focused course that empowers learners to integrate AI—particularly large language models like ChatGPT—into their development workflows, driving speed, quality, and innovation in coding tasks.
Participants will gain practical skills in using AI tools to streamline code creation, optimize performance, debug more efficiently, and tackle common development challenges. From foundational web development to data transformation and scripting, this course offers comprehensive, real-world guidance on how AI is reshaping the software development landscape.
The ISTQB® Certified Tester AI Testing (CT-AI) certification is a globally recognized credential designed for professionals involved in testing artificial intelligence (AI) systems. This certification provides foundational knowledge of AI concepts, technologies, and the specific challenges associated with testing AI-driven applications. This course covers fundamental AI concepts, testing methodologies for AI systems, unique challenges such as bias and explainability, key quality characteristics like performance and security, and introduces tools and techniques for effective AI system assessment.
