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Course Description

Following up on the fundamentals started in Introduction to Data Science, this course explores all the basic parts of executing a machine learning experiment. Upon successful completion of this course, you will be able to design your own machine learning experiments. Machine Learning I will present basic algorithms, such as regression, C4.5 decision trees, and Nearest Neighbours, with an emphasis on the fundamentals of properly designing a machine learning experiment which includes cross validation and evaluation metrics. At the end of this course you will be able to create your own ML pipeline, describe the differences between classic ML algorithms and of their best fit use cases, and build well performing ML models. Lessons are taught with the scikit-learn library in python. It is recommended that you complete CCTB463 - Data Mining and Data Visualization prior to enrolling in this course.

Course Information

CCTB 464

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in

Section Title
Machine Learning I
Type
Discussion
Days
Friday
Time
6:00PM to 9:30PM
Dates
Nov 13, 2026
Type
Discussion
Days
Saturday
Time
8:00AM to 4:00PM
Dates
Nov 14, 2026
Type
Discussion
Days
Sunday
Time
8:00AM to 11:30AM
Dates
Nov 15, 2026
Schedule and Location
Contact Hours
14.0
Location
Delivery Options
Online w/instructor, set times  
Course Fee(s)
Tuition non-credit $750.00
Section Title
Machine Learning I
Type
Discussion
Days
Friday
Time
6:00PM to 9:30PM
Dates
Apr 02, 2027
Type
Discussion
Days
Saturday
Time
8:00AM to 4:00PM
Dates
Apr 03, 2027
Type
Discussion
Days
Sunday
Time
8:00AM to 11:30AM
Dates
Apr 04, 2027
Schedule and Location
Contact Hours
14.0
Location
Delivery Options
Online w/instructor, set times  
Course Fee(s)
Tuition non-credit $750.00
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