Digital Innovation and Transformation

Fundamentals of Artificial Intelligence (AI)

This course provides a comprehensive introduction to the fundamentals of Artificial Intelligence (AI), focusing on practical applications, tools, and techniques. Participants will learn to implement AI solutions, understand algorithms, and assess AI's impact on business processes and decision-making.

5 days Classroom or live online English

Smart Horizon Certified - Global Leadership Consultants

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Download the full standard outline: why attend, methodology, objectives, audience, competencies, the daily outline and current plans.

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Why attend

From knowledge to real-world impact.

AI is transforming industries by automating processes, enhancing decision-making capabilities, and driving innovation across various sectors. Professionals must grasp AI fundamentals to leverage its capabilities effectively. This course addresses the challenge of integrating AI into existing workflows, ensuring participants can identify opportunities for AI applications and mitigate risks associated with AI implementation. By mastering AI concepts, attendees will be equipped to drive projects that enhance operational efficiency, improve customer experiences, and contribute to their organisation's competitive advantage. Understanding AI is essential for making informed strategic decisions that align with business objectives and ethical standards, ultimately leading to sustainable growth.

Participants will engage in hands-on exercises to develop practical skills in AI technologies, including data analysis, model training, and performance evaluation. They will learn to evaluate algorithms, design AI models, and apply machine learning techniques to real-world problems. Practical capabilities include assessing data quality, implementing ethical AI practices, and making informed decisions about AI projects. By the end of the course, attendees will be equipped to contribute to their organisation's AI strategy, ensuring that AI initiatives align with business objectives and ethical standards, ultimately driving innovation, efficiency, and responsible AI deployment.

Workplace challenges

Professional Problems This Course Is Designed to Solve

The programme explores practical approaches to the following challenges. Results depend on how the learning is applied in your organisation.

Confusion about AI capabilities, terminology and limitations.
Difficulty distinguishing feasible use cases from unrealistic claims.
Limited understanding of data quality, bias and responsible use.

Course methodology

How you will learn.

The course employs a blend of lectures, hands-on workshops, and case studies to facilitate learning. Participants will engage in coding exercises to implement AI algorithms, analyse datasets, and develop predictive models. Group discussions will encourage collaboration and critical thinking, while real-world scenarios will help participants apply theoretical knowledge to practical challenges. Outputs will include project documentation, model evaluations, and ethical assessments, demonstrating their understanding of AI concepts and applications. This structured approach ensures that participants not only learn the theory behind AI but also gain the skills necessary to implement AI solutions effectively in their workplaces, preparing them for real-world challenges.

Course objectives

What you'll take away.

By the end of this course, participants will be able to:

Explain the core concepts of Artificial Intelligence and its applications in various industries.
Identify and compare different AI algorithms and their suitability for specific tasks.
Design a simple machine learning model using appropriate tools and frameworks.
Evaluate the ethical implications of AI technologies in business contexts, focusing on bias and fairness.
Implement basic data preprocessing techniques for AI applications, including data cleaning and transformation.
Assess the performance of AI models using relevant metrics and techniques, such as accuracy and precision.
Develop a project plan for an AI implementation, including risk assessment and resource allocation.
Critique AI solutions based on effectiveness, efficiency, and ethical considerations, ensuring alignment with business goals.

Target audience

Who should attend.

This course is suitable for:

Data Analysts seeking to enhance their analytical capabilities with AI tools.
IT Professionals looking to integrate AI solutions into existing systems.
Business Managers aiming to understand AI's impact on decision-making processes.
Researchers interested in applying AI methodologies to their projects.

Target competencies

Capabilities you will build.

Participants will develop competencies in:

Understanding of AI terminology and concepts.
Ability to evaluate AI algorithms for specific applications.
Proficiency in designing and implementing machine learning models.
Skill in data preprocessing and feature engineering for AI.
Awareness of ethical considerations in AI deployment.
Capability to assess AI model performance effectively.
Experience in project planning for AI initiatives.

Course outline

Your learning journey.

A focused, practical outline with time to explore, apply and reflect.

Day 1Introduction to AI Concepts

Applied activity: Participants will analyse a case study of an AI implementation in a business setting, identifying key concepts and applications discussed. They will assess the effectiveness of the AI solution and suggest improvements based on their understanding of AI principles.

Output: A summary report highlighting AI concepts and applications relevant to the case study, along with recommendations for improvement.

09:00–09:54Define Artificial Intelligence and its significance in modern business environments.
09:54–10:48Explore the history and evolution of AI technologies and their impact on industries.
10:48–11:00Identify key AI applications across various sectors, including healthcare and finance.
11:00–11:30Coffee break
11:30–12:12Identify key AI applications across various sectors, including healthcare and finance. (continued)
12:12–13:06Discuss the differences between AI, machine learning, and deep learning methodologies.
13:06–14:00Examine the role of data in AI development and its implications for businesses.

Day 2AI Algorithms and Techniques

Applied activity: Participants will work in groups to select an algorithm for a given dataset, justifying their choice based on the problem context and data characteristics. They will also outline the expected outcomes of their selected algorithm.

Output: A presentation outlining the selected algorithm, its suitability for the dataset, and expected outcomes.

09:00–09:54Introduce fundamental AI algorithms, including decision trees, support vector machines, and neural networks.
09:54–10:48Compare supervised and unsupervised learning techniques, discussing their strengths and weaknesses.
10:48–11:00Explore reinforcement learning and its applications in real-world scenarios.
11:00–11:30Coffee break
11:30–12:12Explore reinforcement learning and its applications in real-world scenarios. (continued)
12:12–13:06Discuss the importance of algorithm selection based on project requirements and data characteristics.
13:06–14:00Examine case studies of successful AI algorithm implementations in various industries.

Day 3Machine Learning Model Design

Applied activity: Participants will design and implement a basic machine learning model using a provided dataset, focusing on feature selection, model training, and evaluation. They will document their process and results for peer review.

Output: A functional machine learning model with comprehensive documentation of the design process, evaluation results, and insights gained.

09:00–09:54Understand the machine learning lifecycle from data collection to model deployment and monitoring.
09:54–10:48Learn about feature selection and engineering techniques to improve model accuracy.
10:48–11:00Implement a simple machine learning model using Python libraries such as Scikit-learn.
11:00–11:30Coffee break
11:30–12:12Implement a simple machine learning model using Python libraries such as Scikit-learn. (continued)
12:12–13:06Discuss model validation and testing methods, including cross-validation and performance metrics.
13:06–14:00Explore tools for model deployment and monitoring, ensuring ongoing performance evaluation.

Day 4Data Preprocessing and Ethics in AI

Applied activity: Participants will preprocess a dataset, addressing quality issues and preparing it for model training, while considering ethical implications. They will document their preprocessing steps and ethical considerations in a report.

Output: A cleaned and transformed dataset ready for machine learning application, along with a report detailing preprocessing steps and ethical considerations.

09:00–09:54Discuss the importance of data quality and preprocessing in AI projects, including data cleaning and transformation.
09:54–10:48Learn techniques for data cleaning, normalisation, and handling missing values.
10:48–11:00Examine ethical considerations in AI, including bias, fairness, and transparency.
11:00–11:30Coffee break
11:30–12:12Examine ethical considerations in AI, including bias, fairness, and transparency. (continued)
12:12–13:06Explore regulatory frameworks governing AI use, such as GDPR and AI ethics guidelines.
13:06–14:00Discuss strategies for responsible AI deployment and stakeholder engagement.

Day 5Integrated AI Project Application

Applied activity: Participants will collaborate to create a complete AI project, from problem identification to model implementation. They will present their solution to the class, highlighting the methodologies used and ethical considerations addressed.

Output: A comprehensive project report detailing the AI solution, methodologies used, ethical considerations addressed, and reflections on the project outcomes.

09:00–09:54Integrate knowledge of AI concepts, algorithms, and ethics into a comprehensive project.
09:54–10:48Work collaboratively to develop an AI solution for a real-world problem, including project planning and resource allocation.
10:48–11:00Present findings and solutions to peers for feedback, focusing on the implementation process and outcomes.
11:00–11:30Coffee break
11:30–12:12Present findings and solutions to peers for feedback, focusing on the implementation process and outcomes. (continued)
12:12–13:06Discuss challenges faced during project development and solutions implemented, reflecting on lessons learned.
13:06–14:00Reflect on the learning journey and future applications of AI knowledge in participants' professional contexts.

The timings provided are estimates and may vary depending on the number of participants and the discussions taking place during the sessions. They are indicative only and do not constitute a binding commitment.

Dates & venues

Find a session that works for you.

Confirmed dates accept registration requests. Tentative dates are enquiry only.

Good to know

A few things to know.

How is the course delivered?

Our classroom programmes combine facilitated discussion, case studies, exercises and reflection. The precise format will be confirmed before enrolment.

Can you deliver this programme for our team?

Yes, you can enquire about a tailored programme. Share your goals, group size and preferred dates with our learning team.

Will I receive a certificate?

Participants who complete the programme receive a Smart Horizon UK certificate of attendance. Smart Horizon UK programmes are not externally accredited and do not award a professional qualification, licence or designation. Where a course title or outline refers to a professional certification (for example, AACE International's Certified Cost Professional), the programme supports learning and preparation only; the credential itself is awarded solely by the relevant professional body under its own application and examination requirements.

What should I confirm before booking travel?

Please confirm the session, exact venue, accessibility arrangements, fees and any visa requirements with the team before arranging travel. Tentative dates are not confirmed sessions.

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