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Expert Tailored AI & Machine Learning Package

The average AI & Machine Learning salary is £80,000 - £120,000+

About the

AZ-900, AI-900, AI-050, AI-102
18-24 months to complete
7 Modules, with 4 exams.

Our Beginner AI & Machine Learning Course is designed for anyone interested in acquiring 7 Internationally recognised Certifications that will propel them in the AI & Machine Learning industry! These certifications validate your skills and knowledge in AI, Machine Learning, and associated technologies. The modules are designed to be completed in sequence, building upon each other.

This course is tailored for students with limited IT experience, allowing you to start your journey with Microsoft Azure Fundamentals and work your way up to Designing and Implementing a Microsoft Azure AI Solution.

This training is made up of seven professional certifications, which are are in high demand by employers. These are as follows:

  • Microsoft Azure Fundamentals (AZ-900)
  • Microsoft Security, Compliance, and Identity Fundamentals (SC-900)
  • Microsoft Azure AI Fundamentals (AI-900)
  • Python Programming (PY-001)
  • ChatGPT & AI Business Fundamentals (GPT-001)
  • Designing and Implementing a Microsoft Azure AI Solution (AI-102)
  • Course Review
Do I Need Prior Experience?

No prior experience or knowledge in IT or AI & Machine Learning is required. This course assumes no prior knowledge and will take you from beginner to expert.

Job Roles

Upon completion, you can pursue various roles such as AI Developer, Machine Learning Engineer, Data Scientist, AI Business Analyst, and more. AI & Machine Learning roles are in high demand across various industries.

How to Get Started?

Our AI & Machine Learning Training course is designed to provide the knowledge and certifications you need to excel in this field. We also offer unique recruitment advice, including CV and LinkedIn formatting, interview techniques, and job placement in your local area.

Salary Expectations

Salaries in the AI & Machine Learning field can vary but are generally lucrative. An AI Developer can expect an average salary of around £80k p.a, while a Machine Learning Engineer can earn up to £120k p.a. The earning potential in this field is substantial, and our course is your gateway to tapping into it.

Benefits at a Glance

Why choose Robust IT?

  • Delivered courses to thousands, catering to diverse backgrounds.
  • Mission to make IT accessible to all, regardless of experience or financial situation.
  • Partnerships with Microsoft, CompTIA, NCSC, PeopleCert, and Pearson Vue and more.
  • UK-based, certified trainers and support staff.
  • Flexible budget and schedule options with multiple training locations.
  • Official, industry-recognized certifications upon course completion.
  • In-house recruitment team for job placement and career guidance.
Average salary for Machine Learning Engineer


Avg. Base Salary (GBP).

Job Roles

  • Machine Learning Engineer
  • Data Scientist
  • AI Developer
  • Natural Language Processing Engineer
  • Computer Vision Engineer
  • AI Research Scientist
  • AI Product Manager
  • AI Architect
  • Robotics Engineer
  • AI Business Analyst
What's included?

Course package details

Candidates for this exam should have foundational knowledge of cloud services and how those services are provided with Microsoft Azure. The exam is intended for candidates who are just beginning to work with cloud-based solutions and services or are new to Azure.

Azure Fundamentals exam is an opportunity to prove knowledge of cloud concepts, Azure services, Azure workloads, security and privacy in Azure, as well as Azure pricing and support. Candidates should be familiar with the general technology concepts, including concepts of networking, storage, compute, application support, and application development.

Azure Fundamentals can be used to prepare for other Azure role-based or specialty certifications, but it is not a prerequisite for any of them.
  • Describe cloud concepts (20-25%)
  • Describe core Azure services (15-20%)
  • Describe core solutions and management tools on Azure (10-15%)
  • Describe general security and network security features (10-15%)
  • Describe identity, governance, privacy, and compliance features (20-25%)
  • Describe Azure cost management and Service Level Agreements (10-15%)

This exam measures your ability to describe the following concepts: cloud concepts; core Azure services; core solutions and management tools on Azure; general security and network security features; identity, governance, privacy, and compliance features; and Azure cost management and Service Level Agreements.

This exam is an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services. As a candidate for this exam, you should have familiarity with Exam AI-900’s self-paced or instructor-led learning material.

This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:

  • Cloud basics
  • Client-server applications

You can use Azure AI Fundamentals to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

  • Describe Artificial Intelligence workloads and considerations (20–25%)
  • Describe fundamental principles of machine learning on Azure (25–30%)
  • Describe features of computer vision workloads on Azure (15–20%)
  • Describe features of Natural Language Processing (NLP) workloads on Azure (25–30%)
Python is developed under an OSI-approved open source license, making it freely usable and distributable, even for commercial use. Python is a general-purpose programming language. Created nearly 30 years ago, it is now one of the most popular languages out there to use. Its popularity is particularly important in the data science and machine learning fields. But it is also a language that is easy to learn, and that’s why it has become the language most taught in universities.

Python interpreters are available for the main operating systems as well (Linux, Mac OS, Windows, Android, iOS, BSD, etc.) so it’s very flexible in where it is used. Python has a simple syntax that makes it suitable for learning to program as a first language. The learning curve is smoother than other languages such as Java, which quickly requires learning about Object Oriented Programming or C/C++ that require understanding pointers. Still, it's possible to learn about OOP or functional programming in Python when the time comes.

Where is Python Used?

  • Web Development, using the frameworks Django, Flask, Pylons
  • Data Science and Visualization using Numpy, Pandas and Matplotlib
  • Machine learning with Tensorflow and Scikit-learn
  • Desktop applications with PyQt, Gtk, wxWidgets and many more
  • Mobile applications using Kivy or BeeWare
  • Education: Python is a great language to learn programming
Learn about the exciting world of ChatGPT and AI for Business to apply immediately for business content requirements.

(Beginner) This course has been designed for anyone who would like to grasp how the world is adapting to ChatGPT which is an Open AI platform revolutionizing the way we engage with artificial intelligence. An AI-powered Chatbot can have natural-sounding conversations with users and is used to comprehend queries and give precise answers.

Did You Know that following the release of ChatGPT, Open AI was valued at $29 billion.

This course will guide you through the all the media hype and provide you with the right foundation to understand the world of ChatGPT.

The course will provide the learner demonstrations on how to immediately use ChatGPT to create content and derive value from using this AI service.
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.

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.


Before starting this learning path, you should already have:
  • Familiarity with Azure and the Azure portal.
  • Introduction to Azure OpenAI Service
  • Get started with Azure OpenAI Service
  • Build natural language solutions with Azure OpenAI Service
  • Apply prompt engineering with Azure OpenAI Service
As a Microsoft Azure AI engineer, you build, manage, and deploy AI solutions that make the most of:
  • Azure Cognitive Services.
  • Azure Applied AI services.
Your responsibilities include participating in all phases of AI solutions development, including:
  • Requirements definition and design
  • Development
  • Deployment
  • Integration
  • Maintenance
  • Performance tuning
  • Monitoring
You work with solution architects to translate their vision and with data scientists, data engineers, Internet of Things (IoT) specialists, infrastructure administrators, and other software developers to build complete end-to-end AI solutions.

As an Azure AI engineer, you have experience developing solutions that use languages such as:
  • Python
  • C#

You should be able to use Representational State Transfer (REST) APIs and SDK to build secure image processing, video processing, natural language processing, knowledge mining, and conversational AI solutions on Azure. You should be familiar with all methods of implementing AI solutions. Plus, you understand the components that make up the Azure AI portfolio and the available data storage options. As an Azure AI engineer, you also need to understand and be able to apply responsible AI principles.

Skills Measured

  • Plan and manage an Azure AI solution (25–30%)
  • Implement image and video processing solutions (15–20%)
  • Implement natural language processing solutions (25–30%)
  • Implement knowledge mining solutions (5–10%)
  • Implement conversational AI solutions (15–20%)
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