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Machine learning fundamentals
A machine learning model must be evaluated against the problem it is intended to solve. Structure data preparation, training and validation to make your choices explicit. Develop a technical approach that enables you to compare results and identify the model's limitations.
- Duration
- 3 days 21 hours
- Code
- FC-FML Code
Presentation
In data science, machine learning is a specific branch of artificial intelligence. Its aim is to enable algorithms to identify patterns, meaning recurring elements within datasets. This data can include numbers, words, images and statistics.
This machine learning course aims to familiarise you with best practices for the technology, according to business profiles and requirements. Every organisation needs its data experts to have a sound understanding of machine learning algorithms and their uses.
A data scientist, data engineer or data analyst develops predefined models to adapt them to specific uses and also creates machine learning models. These topics form the programme covered over 3 intensive days, supported by practical Python exercises.
Objectives
After completing the machine learning course, you will achieve the following learning objectives:
- understand and describe the roles of the main machine learning algorithms and their associated parameters;
- adapt a data model to business requirements and its context of use;
- create a machine learning model from start to finish using good practices.
Program
Machine learning basics (part 1)
- Review and explanation of Python for machine learning.
- Presentation of types of artificial intelligence and their characteristics.
- Presentation of the main families of machine learning algorithms.
Machine learning basics (part 2)
- Using self-supervised learning (SSL) for automated data processing.
- Making the most of data through unsupervised learning and data transformation.
Artificial intelligence training techniques
- Applying feature engineering.
- Configuring hyperparameters in Python.
Data processing methods
- Using a text data analysis approach.
- Using a sequential data analysis approach.
Audience
This course is intended for:
- IT professionals, such as data analysts and digital development managers;
- project managers or managers wishing to specialise in data management and learn more about the importance of data;
- IT professionals wishing to learn or acquire new skills in Big Data and artificial intelligence technologies.
Prerequisites
The machine learning course requires the following prerequisites:
- sound knowledge of mathematics or data science;
- the ability to work with a programming language, particularly Python.
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
- Practical exercises
- Case study
Course highlights
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.