Trees, SVM and Unsupervised Learning

Trees, SVM and Unsupervised Learning

Trees, SVM and Unsupervised Learning This course is part of Statistical Learning for Data Science Specialization Taught in English Instructor: Osita Onyejekwe Enroll for Free Starts Mar 21 Financial aid available Included with • Learn more Course Gain insight into a topic and learn the fundamentals Intermediate level Recommended experience Close Recommended experience Intermediate level

Description

Trees, Support Vector Machines (SVM), and Unsupervised Learning are three important concepts in the field of machine learning that have a significant impact on the way data is analyzed and patterns are identified. Each of these techniques has its own strengths and weaknesses, and understanding how they work can help data scientists and researchers make informed decisions about which approach to use for a given problem.

Trees are a popular method for supervised learning tasks, where the goal is to predict a target variable based on a set of input features. Decision trees, random forests, and gradient boosting are some of the tree-based algorithms commonly used in machine learning. Trees are intuitive to interpret and can handle both numerical and categorical data. They are also robust to outliers and missing values, which makes them a versatile tool for various types

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.

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