Artificial Intelligence and Machine Learning

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.

Please note that there are three semesters as intake periods as noted on the academic calendar.
Scholarship Awarded (Total Fee: €100)

Registration is done online.