Online (Virtual Campus): no scheduled meeting times; all coursework completed online in Canvas/uCertify. Semester dates: August 17 - December 10, 2026.
This is an online course presented through uCertify with no in person lecture requirements.
Faculty Contact Information
Please email rcooper@kcc.edu for support
Ron Cooper, Adjunct Professor — rcooper@kcc.edu
Dean: Paul Carlson, Dean - Business, Technology & Human Services — 815-802-8858 — pcarlson@kcc.edu
Division Office: Room W102 — 815-802-8850
Course Information
Machine Learning with Python for Everyone by Mark E. Fenner — Addison-Wesley Professional (Pearson), 1st edition, August 2019. Delivered via uCertify access code (includes course and labs).
A PC with Windows 10 or higher ((or other system that works)) and Internet access is required to successfully work with the course materials. If needed, KCC may be able to lend students Windows laptops for particular tasks for a duration. Contact your program advisor, ITS (815-802-8900), or the Bookstore (815-802-8590) for more information.
Student evaluation is based on points accrued via Virtual Labs, Assignments, Quizzes, and Exams. Point values may change based on added or removed assignments.
Grading scale: 90-100% = A; 80-89% = B; 70-79% = C; 60-69% = D; 59% or lower = F.
Retakes and attempts: There are no limits on attempts in this course and no penalty for retaking. If you are not happy with a score, take it again. Work completed in uCertify records your most recent attempt, so if a retake goes worse than an earlier try, simply take it again until you have a score you are comfortable with.
Business, Technology & Human Services
Dean, Paul Carlson; 815-802-8858; V105; pcarlson@kcc.edu; Division Office – W102; 815-802-8650
Attendance/participation: Attendance is MANDATORY - for times stated, measured in this online course by weekly participation and on-time submission of assigned work. If a week's work must be missed, arrangements must be made with the instructor in advance; for unplanned absences, a valid excuse must be provided. Make-up work is at the discretion of the instructor and must be completed within one week.
Reading and communication: This course requires you to complete the assigned reading. Weekly participation in discussion boards and peer reviews is required. Primary communication will be via Canvas and email. The instructor will respond to messages within 48 hours (weekdays) and grade assignments within one week of submission. Maintain respectful, professional communication at all times.
Quizzes, assignments, and exams: Quizzes assess recall and retention of readings and assignments and are posted in Canvas/uCertify. Assignments follow up on readings throughout the semester. Exams assess retention of the reading material and your ability to compare/contrast and apply concepts.
Late work: Late assignments will not be accepted unless arrangements are made. However my due dates are fairly open ended - so if a date is about to pass or has passed, please make arrangements or realize there will be impacts to grades.
Student integrity: All students are expected to complete quizzes, exams, and papers with integrity and respect for fellow students, the instructor, and the institution. Cheating will not be tolerated. Upon evidence of cheating, the student will be dropped from the course and receive a grade of F.
Use respectful, professional language in all discussions and emails. Avoid ALL CAPS, slang, or inappropriate language. When replying to peers, provide constructive feedback.
You will gain the most from this course by actively participating in the weekly discussions and sharing your experiences and questions. Get to know your classmates and help one another whenever possible (but not during quizzes and exams).
Weekly work each week: Read, Cards, Quizzes, Labs (in uCertify/Canvas). Chapters from Fenner, Machine Learning with Python for Everyone.
Week 1 (Aug 18-24): Ch. 1: Let's Discuss Learning — features, targets, classifiers vs. regressors, building learning systems
Week 2 (Aug 25-31): Ch. 2: Some Technical Background — Python setup, probability, linear combinations, NumPy, floating point
Week 3 (Sep 1-7): Ch. 3: Getting Started with Classification — train/test, k-NN, Naive Bayes, evaluation basics
Week 4 (Sep 8-14): Ch. 4: Getting Started with Regression — k-NN regression, linear regression, optimization, RMSE
Week 5 (Sep 15-21): Ch. 5: Evaluating and Comparing Learners — overfitting/underfitting, cross-validation, bias-variance
Week 6 (Sep 22-28): Ch. 6: Evaluating Classifiers — confusion matrix, ROC curves, AUC, precision-recall
Week 7 (Sep 29-Oct 5): Ch. 7: Evaluating Regressors — baselines, R2, residual plots, standardization
Week 8 (Oct 6-12): Ch. 8: More Classification Methods — decision trees, SVMs, logistic regression
Week 9 (Oct 13-19): Ch. 9: More Regression Methods
Week 10 (Oct 20-26): Ch. 10: Manual Feature Engineering — manipulating data for fun and profit
Week 11 (Oct 27-Nov 2): Ch. 11: Tuning Hyperparameters and Pipelines
Week 12 (Nov 3-9): Ch. 12: Combining Learners (ensembles)
Week 13 (Nov 10-16): Ch. 13: Models That Engineer Features for Us
Week 14 (Nov 17-23): Ch. 14: Feature Engineering for Domains — domain-specific learning
Week 15 (Nov 24-30): Ch. 15: Extensions and Further Directions
Week 16 (Dec 1-7): Review
Final exams: Dec 5 and Dec 7-10. No classes: Labor Day Sep 5-7, Veterans Day Nov 11, Thanksgiving break Nov 26-29. Last day to withdraw: Nov 5.
College Policies, Resources and Supports
For information related to the Student Code of Conduct Policy, Withdrawal Policy, Email Policy, and Non- Attendance/Non-Participation Policy, please review the college’s Code of Campus Affairs and Regulations webpage, which can be found at catalog.kcc.edu under the Academic Regulations & Conduct Guide.
KCC offers various academic and personal resources for all students. Many services are offered virtually, as well as in person. Please visit Student Resources - Kankakee Community College to access student resources services such as:
- Clubs and organizations
- Counseling and referral services
- Office of disability services
- Student complaint policy
- Transfer services
- Tutoring services, etc.
The materials on this course are only for the use of students enrolled in this course for purposes associated with this course. Further information regarding KCC's copyright policy is available at https://kcc.libguides.com/copyright.
|Course syllabus/calendar is subject to change.