机器学习分类算法使用MATLAB

 

教学时长:7小时 视频格式:MP4 文件大小:913MB

Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer

Basic Course Description

This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Ouput Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the esesential ideas. The following are the course outlines.

Sgement 1: Instructor and Course Introduction

Segment 2: MATLAB Crash Course

Segment 3: Grabbing and Importing Dataset

Segment 4: K-Nearest Neighbor

Segment 5: Naive Bayes

Segment 6: Decision Trees

Segment 7: Discriminant Analysis

Segment 8: Support Vector Machines

Segment 9: Error Correcting Ouput Codes

Segment 10: Classification with Ensembles

Segment 11: Validation Methods

Segment 12: Evaluating Performance

At the end of this course,

You can confidently implement machine learning algorithms using MATLAB.
You can perform meaningful analysis on the data

.


下面的译文仅作参考(请以上面的英文说明为准)
学习如何在科学家和工程师使用的最强大的工具中实现分类算法

基础课程描述

本课程旨在涵盖机器学习的一个最有趣的领域,即分类。我将在本课程中逐步讲解,并将首先介绍MATLAB的基础知识。接下来,我们将研究如何使用MATLAB来使用不同的机器学习算法。具体来说,我们将会看到一个名为统计和机器学习工具箱的MATLAB工具箱。我们将实现一些最常用的分类算法,如k -近邻、朴素贝叶斯、判别分析、决策树、支持向量机、纠错码和集合。接下来我们将讨论如何交叉验证这些模型以及如何评估它们的性能。对分类算法的直觉也包括在内,这样一个没有数学背景的人仍然可以理解这些基本概念。以下是课程大纲。

Sgement 1:讲师和课程介绍

段2:MATLAB速成课程

片段3:抓取和导入数据集

第四段:再

第五段:朴素贝叶斯

第六部分:决策树

第七段:判别分析

段8:支持向量机

段9:错误校正输出代码

段10:用套装分类

段11:验证方法

段12:评估性能

在课程的最后,

您可以使用MATLAB实现机器学习算法。
您可以对数据进行有意义的分析。
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