fr
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Introduction to artificial intelligence and machine learning
Contributing to an AI project requires the ability to distinguish real capabilities from hype. Connect data, models and use cases to understand the choices involved and their limitations. Build a foundation for more precise discussions with technical teams and better-supported decisions.
- Duration
- 1 day 7 hours
- Code
- DEV020FR Code
Presentation
Artificial intelligence is radically transforming every sector, from finance to healthcare. Yet concepts such as machine learning and deep learning often remain unclear to non-specialists. This one-day course is specifically designed to demystify these technologies and give you the understanding you need to operate in an AI-driven digital environment.
The program takes a progressive, accessible learning approach. Start by clarifying AI terminology and history before exploring how machine learning works. Discover how algorithms learn through supervised and unsupervised approaches, and the tools and roles that make up this rapidly developing ecosystem.
Rather than focusing on complex mathematics, the day emphasises experimentation. Practical workshops using intuitive tools such as Google Teachable Machine or Excel give you first-hand experience of working with data. You will leave with a clear understanding of the AI project lifecycle and the skills needed to contribute.
Objectives
By the end of this course, you will be able to:
- define key AI concepts and distinguish machine learning from deep learning;
- identify the main techniques, including regression and classification, and the algorithms used;
- describe different AI roles, such as Data Scientist and ML Engineer, and their associated skills;
- understand the key stages of an AI project lifecycle, from data collection to deployment;
- experiment with creating a simple model without requiring programming skills.
Program
Module 1: understanding AI fundamentals
- Definitions, history and the distinction between AI, machine learning and deep learning.
- Overview of application areas: healthcare, finance, industry and HR.
- Types of learning (supervised and unsupervised) and common algorithms (clustering and regression).
Hands-on exercises
- Use an interactive quiz to check your understanding of key concepts in an engaging way.
Module 2: discovering the ecosystem and professional roles
- Using tools such as Python and TensorFlow, and platforms such as Google Colab and Azure ML.
- The roles of the Data Scientist, Machine Learning Engineer and AI Researcher.
- Key skills, including statistics and ethics, and career prospects.
Hands-on exercises
- Explore and prepare a simple dataset using office tools such as Excel or Google Sheets.
Module 3: managing an AI project through practice
- The project lifecycle: problem definition, data collection, modelling and monitoring.
- Essential concepts of overfitting and model reliability assessment.
Hands-on exercises
- Create and train an image or sound classification model using the no-code tool Teachable Machine.
Audience
This course is intended for non-technical participants who want to understand AI, including:
- professionals changing careers and moving into digital roles;
- recruitment and HR professionals who need to assess AI/ML profiles more accurately;
- managers and IT project managers working with data teams;
- students or recent graduates in management or IT.
Prerequisites
The following background is useful for this course:
- General IT knowledge or an interest in data management is an advantage.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Quiz / multiple-choice questions
- Practical exercises
Course highlights
- No-code approach: create your own AI model with Teachable Machine without writing a single line of code.
- Career focus: an entire module explores job descriptions (Data Scientist versus ML Engineer) to clarify the employment landscape.
- 360° perspective: gain an overall understanding, from the history of AI to its operational deployment.
- Accessibility: technical jargon is explained in plain language, making AI understandable regardless of your initial background.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.
Any brand names and logos mentioned in this course description (such as Google, Azure, Python and TensorFlow) belong to their respective owners. Their mention for educational purposes does not imply any commitment or partnership.