DATA SCIENCE: MACHINE LEARNING (2/1 Credits)
Learning Outcomes :
On successful completion of this course, student will be able to: Explain fundamental machine learning concepts and differentiate between AI, Machine Learning, and Deep Learning, perform data pre-processing data using Python, including feature engineering; apply the best machine learning model to solve problem(s) using Python; apply a variety of machine learning techniques in software engineering contexts
Topics :
Introduction to AI in Software Engineering, Data Preparation, Regression, Classification, Tree-based Methods, Clustering, Quiz, Fundamentals of Statistical Machine Learning, Machine Learning Process, Supervised Learning, Resampling Methods, Model Selection and Regularization Techniques, Tree-based Methods, Support Vector Machines (SVM), Unsupervised Learning, Deep Learning and Neural Networks, Multiple Testing, Ethical Considerations and Potential Biases in Machine Learning Applications, Project Presentation
SOCIAL MEDIA
Let’s relentlessly connected and get caught up each other.
Looking for tweets ...