Artificial Intelligence and Machine Learning

Open to all candidates

No prerequisites

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

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

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

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

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

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.

Certificate issued on the blockchain.

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.

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.