In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM.
This course is part of the Machine Learning: Theory and Hands-on Practice with Python Specialization
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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.
Learn more about Coursera Labs Course 1 of 3 in the
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 Course 1 of 3 in the
Intermediate Level
Calculus, Linear algebra, Python
Approx. 39 hours to complete
English
Offered by
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
7 hours to complete
Introduction to Machine Learning, Linear Regression
7 hours to complete
5 videos (Total 67 min), 11 readings, 6 quizzes
6 hours to complete
Multilinear Regression
6 hours to complete
4 videos (Total 44 min), 5 readings, 3 quizzes
7 hours to complete
Logistic Regression
7 hours to complete
4 videos (Total 63 min), 6 readings, 3 quizzes
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

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