University of Colorado Boulder
Trees, SVM and Unsupervised Learning
University of Colorado Boulder

Trees, SVM and Unsupervised Learning

This course is part of Statistical Learning for Data Science Specialization

Taught in English

Osita Onyejekwe

Instructor: Osita Onyejekwe

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

12 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Describe the advantages and disadvantages of trees, and how and when to use them.

  • Apply SVMs for binary classification or K > 2 classes.

  • Analyze the strengths and weaknesses of neural networks compared to other machine learning algorithms, such as SVMs.

Details to know

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

12 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Statistical Learning for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

The module provides an introductory overview of the course and introduces the course instructor.

What's included

1 video2 readings1 discussion prompt

To begin the course, we will learn about support vector machines (SVMs). SVMs have become a popular method in the field of statistical learning due to their ability to handle non-linear and high-dimensional data. SVMs seek to maximize the margin, or distance between the decision boundary and the closest data points, to improve generalization performance. Throughout the week, you will learn how to apply SVMs to classify or predict outcomes in a given dataset, select appropriate kernel functions and parameters, and evaluate model performance

What's included

4 videos1 reading1 programming assignment1 ungraded lab

Neural Networks have become increasingly popular in the field of statistical learning due to their ability to model complex relationships in data. In this module, we will cover introductory concepts of neural networks, such as activation functions and backpropagation. You will have the opportunity to apply Neural Networks to classify or predict outcomes in a given dataset and evaluate model performance in the labs for this module.

What's included

5 videos1 reading1 programming assignment

Welcome to the final module for the course. This module will focus on the ensemble methods decision trees, bagging, and random forests, which combine multiple models to improve prediction accuracy and reduce overfitting. Decision Trees are a popular machine learning method that partitions the feature space into smaller regions and models the response variable in each region using simple rules. However, Decision Trees can suffer from high variance and instability, which can be addressed by Bagging and Random Forests. Bagging involves generating multiple trees on bootstrapped samples of the data and averaging their predictions, while Random Forests further decorrelate the trees by randomly selecting subsets of features for each tree.

What's included

1 video1 reading1 programming assignment1 ungraded lab

Instructor

Osita Onyejekwe
University of Colorado Boulder
2 Courses755 learners

Offered by

Recommended if you're interested in Probability and Statistics

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