edX
MOOC / Non-credit
0
Supervised Learning
About this course
Learn how to build supervised learning models using Python and Sklearn (Sci-Learn). This course includes the most popular supervised learning models, including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Regression, Random Forest and Decision Trees. With Sklearn and Python all of these models can be quickly created using just a few lines of code.
A
82/100
CourseAsk score
- What the provider tells you
- 31/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- understand how to implement K-Nearest Neighbor models
- create Support Vector Machines
- apply Regression techniques
- develop Random Forest models
- build Decision Trees using Python and Sklearn
Course objectives
- to equip students with the skills to build various supervised learning models
- to enhance proficiency in using Python and Sklearn for machine learning tasks
Machine Learning
#python
#regression
#machine learning
#data science
#supervised learning
#decision trees
#random forest
#svm
#KNN
#sklearn
$249.00
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