Artificial Intelligence and Machine Learning

Quel niveau est visé à l’issue de la formation ?

Intermédiaire

Combien de temps dure la formation ?

6,00 heure(s)

Dans quelle(s) langue(s) la formation est-elle donnée ?

Quand a lieu la prochaine session ?

Qui organise cette formation ?

L'European Business Institute (EBU), dont le siège se trouve au Luxembourg, est une école de commerce à vocation internationale au service d'une communauté dynamique de plus de 30 000 apprenants. L'EBU propose une formation flexible et à fort impact à travers des programmes en soirée, en ligne et sur campus, conçus pour répondre aux besoins des professionnels en activité et des apprenants. Fidèle à son engagement en faveur de l'innovation, de l'accessibilité et de l'excellence académique, l'Institut allie des expériences d'apprentissage interactives en direct à l'expertise d'un corps professoral de renommée internationale. Rejoignez un environnement d’apprentissage collaboratif et constructiviste à distance en direct, aux côtés d’étudiants venus de plus de 60 pays.

Le contenu de cette page n'est pas disponible en français

À qui s’adresse la formation ?

Open to all candidates

Quels sont les prérequis ?

No prerequisites

Quels sont les objectifs poursuivis ou les compétences visées ?

1. Master the concepts of supervised and unsupervised learning, recommendation engines, and time series modelling

2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises

3. Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning

4. Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python

5. Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting and Bagging techniques

6. Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

Quel est le contenu de la formation ?

Lesson 1: Introduction to Artificial Intelligence and Machine Learning

Lesson 2: Data Preprocessing

Lesson 3: Supervised Learning

Lesson 4: Feature Engineering

Lesson 5: Supervised Learning-Classification

Lesson 6: Unsupervised Learning

Lesson 7: Time Series Modelling

Lesson 8: Ensemble Learning

Lesson 9: Recommender Systems

Lesson 10: Text Mining

2 six hour sessions

Quels sont les points abordés ?

On successful completion of the course, the candidate will be able to:

1. Master the concepts of supervised and unsupervised learning, recommendation engines, and time series modelling

3. Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning

4. Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python

5. Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting and Bagging techniques

6. Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

Quelles méthodes pédagogiques sont utilisées ?

The online delivery blends synchronous and asynchronous components. Students complete self-directed assignments hosted on the course platform. Forums support a required active, rubric-based student contributions that foster collaboration.

Comment l’évaluation est-elle organisée ?

Exams may include essays, short answers, or MCQs. Formats include open/closed book. Time zones are considered. Assessments are formative and summative. Students have 2 weeks to review grades and must follow the appeal process in the Student Handbook.

Que recevez-vous à la fin de la formation ?

Certificate issued on the blockchain.

Quel support de cours est fourni ?

The EBU ensures administrative and Tech support in an inclusive, accessible learning environment for all students, including those with disabilities or special needs. This policy applies to all students, staff, and learning spaces, full-or part-time.

Mode d'organisation

Two six hour sessions

Quelles informations supplémentaires sont utiles à savoir ?

To join the course, please follow these steps:

- STEP 1: COMPLETE YOUR ONLINE PROFILE
Complete your online profile on the online campus. During this process, you will confirm your essential profile information to accept the offer.
Access the EBU scholarship online campus: https://connect.ebulux.lu/
Click the ‘Join EBU/Log In’ button on the homepage. You will need to create a new account.
During the Account creation process, you will be requested to indicate how you heard about the program and to select the partner organization on a drop-down list. If you found EBU by yourself, please simply write 'EBU' in these fields.
Please note as you create a new account, make sure to use a password you can easily remember.
After you create the account, open the link sent to your email for confirmation and then click continue.

- STEP 2: COMPLETE THE COMMITMENT FEE PAYMENT
To enroll in the Autumn Term courses, go to the Certificate Enrolment page: https://connect.ebulux.lu/mod/page/view.php?id=48775
Select the course you would like to enroll in and click ‘Enroll Now’.
Select Payment Type and click proceed.
Enter card details to complete the commitment fee payment. Once you complete your application and commitment fee payment, you will be enrolled in your selected course and ready to commence.