About this Course

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Shareable Certificate
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100% online
Start instantly and learn at your own schedule.
Flexible deadlines
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Approx. 70 hours to complete
English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 70 hours to complete
English

Offered by

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University of Illinois at Urbana-Champaign

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Accountancy (iMSA) from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

Week
1

Week 1

1 hour to complete

Course Orientation

1 hour to complete
2 videos (Total 9 min), 4 readings, 1 quiz
2 videos
Meet Professor Brunner4m
4 readings
Syllabus10m
About the Discussion Forums10m
Updating Your Profile10m
Social Media10m
1 practice exercise
Orientation Quiz30m
9 hours to complete

Module 1: Introduction to Machine Learning

9 hours to complete
4 videos (Total 47 min), 3 readings, 2 quizzes
4 videos
Introduction to Machine Learning14m
Introduction to Linear Regression14m
Introduction to k-nn12m
3 readings
Module 1 Overview10m
Lesson 1-1 Readings10m
Lesson 1-2 Readings10m
1 practice exercise
Module 1 Graded Quiz30m
Week
2

Week 2

9 hours to complete

Module 2: Fundamental Algorithms

9 hours to complete
5 videos (Total 52 min), 4 readings, 2 quizzes
5 videos
Introduction to Fundamental Algorithms3m
Introduction to Logistics Regression14m
Introduction to Decision Trees15m
Introduction to Support Vector Machine13m
4 readings
Module 2 Overview10m
Lesson 2-1 Readings10m
Lesson 2-3 Readings10m
Lesson 2-4 Readings10m
1 practice exercise
Module 2 Graded Quiz30m
Week
3

Week 3

9 hours to complete

Module 3: Practical Concepts in Machine Learning

9 hours to complete
5 videos (Total 40 min), 3 readings, 2 quizzes
5 videos
Introduction to Modeling Success6m
Introduction to Bagging11m
Introduction to Boosting9m
Introduction to ML Pipelines8m
3 readings
Module 3 Overview10m
Lesson 3-1 Readings10m
Lesson 3-2 Readings10m
1 practice exercise
Module 3 Graded Quiz30m
Week
4

Week 4

9 hours to complete

Module 4: Overfitting & Regularization

9 hours to complete
5 videos (Total 48 min), 4 readings, 2 quizzes
5 videos
Introduction to Overfitting4m
Introduction to Cross-Validation13m
Introduction to Model-Selection16m
Introduction to Regularization8m
4 readings
Module 4 Overview10m
Lesson 4-1 Readings10m
Lesson 4-2 Readings10m
Lesson 4-3 Readings10m
1 practice exercise
Module 4 Graded Quiz30m

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