Course

Course Summary

Credit Type:

Course

ACE ID:

UETC-0001

Organization:

University of Emerging Technologies

Location:

Online

Length:

8 weeks (125 hours total)

Dates Offered:

Credit Recommendation & Competencies

Level	Credits (SH)	Subject
Lower-Division Baccalaureate	3	Computer Science

Description

Objective:

Upon completing this course, students will demonstrate expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes, and Q-Learning. The course will also help students understand the concepts of Statistics, Time Series, and different classes of machine learning algorithms like supervised, unsupervised, and reinforcement algorithms.

Learning Outcomes:

Understand the roles played by a Machine Learning Engineer
Automate data analysis using Python
Analyze use cases in real-world scenarios on machine learning applications
Learn tools and techniques for predictive modeling
Practice implementing types of classification methods, including SVM, Naive Bayes, decision tree, and random forest
Interpret unsupervised learning and apply clustering algorithms
Understand Time Series and its related concepts
Import and wrangle data using Python libraries and divide them into training and test datasets
Build real-world solutions using Machine Learning algorithms