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Machine Learning Algorithms
  • Level Foundation
  • المدة 16 ساعات hours
  • الطبع بواسطة Sungkyunkwan University
  • Offered by

عن

In this course you will: a) understand the naïve Bayesian algorithm. b) understand the Support Vector Machine algorithm. c) understand the Decision Tree algorithm. d) understand the Clustering. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.

الوحدات

Probability Theory Ⅰ

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 01

1 Videos

  • Probability and Conditional Probability

1 Readings

  • Learning Classifiers based on Bayes Rule

Probability Theory Ⅱ

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 02

1 Videos

  • Bayesian Reasoning, and Independence

1 Readings

  • Naive Bayes for Continuous Inputs

Naïve Bayesian Classifier

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 03

1 Videos

  • Formal Description of NBC and examples

1 Readings

  • Regularization

Support Vector Machine Ⅰ

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 04

1 Videos

  • Linear Support Vector Machine

1 Readings

  • Kernel Methods

Support Vector Machine Ⅱ

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 05

1 Videos

  • Dual Form for Learning LSVM and Linear SVM with Soft Margin

1 Readings

  • Support Vector Machines

Support Vector Machine Ⅲ

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 06

1 Videos

  • Basic Idea

1 Readings

  • Optimal margin classifiers

Decision Tree Ⅰ

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 07

1 Videos

  • What is the key idea of Decision Tree?

1 Readings

  • Introduction

Decision Tree Ⅱ

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 08

1 Videos

  • Regression Tree and Random Forests

1 Readings

  • A connection to game theory and linear programming

Decision Tree Ⅲ

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 09

1 Videos

  • Regression Tree and Random Forests

1 Readings

  • Experiments and applications

k-Means, k-Medoids

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 10

1 Videos

  • Introduction, k-means and k-medoids

1 Readings

  • Approximation algorithms for k-means and k-median

k-Means Example, Hierarchical Clustering

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 11

1 Videos

  • Simple Example of k-Means, Hierarchical Method

1 Readings

  • Clustering of stable instances

Gaussian Mixture Model

    • 1 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Quiz 12
  • Final Test

1 Videos

  • What is the Gaussian Mixture Model?

1 Readings

  • Mixture Models

Auto Summary

Unlock the power of machine learning with Coursera's "Machine Learning Algorithms" course. Designed for IT and computer science enthusiasts, this foundational course covers key algorithms like naïve Bayesian, Support Vector Machines, Decision Trees, and Clustering. Ideal for those comfortable with Python and basic math, it spans 960 minutes and is available with a Starter subscription. Perfect for aspiring data scientists and AI professionals!

Jaekwang KIM
Instructor

Jaekwang KIM