About this Course

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Flexible deadlines
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Shareable Certificate
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100% online
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Coursera Labs
Includes hands on learning projects.
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Intermediate Level

Calculus, Linear algebra, Python

Approx. 39 hours to complete
English

What you will learn

  • Use modern machine learning tools and python libraries.

  • Compare logistic regression’s strengths and weaknesses.

  • Explain how to deal with linearly-inseparable data.

  • Explain what decision tree is & how it splits nodes.

Skills you will gain

  • Hyperparameter
  • Decision Tree
  • ensembling
  • sklearn
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs External Link
Intermediate Level

Calculus, Linear algebra, Python

Approx. 39 hours to complete
English

Instructor

Offered by

Placeholder

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

Week1
Week 1
7 hours to complete

Introduction to Machine Learning, Linear Regression

7 hours to complete
5 videos (Total 67 min), 11 readings, 6 quizzes
Week2
Week 2
6 hours to complete

Multilinear Regression

6 hours to complete
4 videos (Total 44 min), 5 readings, 3 quizzes
Week3
Week 3
7 hours to complete

Logistic Regression

7 hours to complete
4 videos (Total 63 min), 6 readings, 3 quizzes
Week4
Week 4
7 hours to complete

Non-parametric Models

7 hours to complete
5 videos (Total 66 min), 6 readings, 3 quizzes

About the Machine Learning: Theory and Hands-on Practice with Python Specialization

Machine Learning: Theory and Hands-on Practice with Python

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