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曙海教學(xué)優(yōu)勢(shì)

　該課程，秉承21年教學(xué)品質(zhì)，以項(xiàng)目實(shí)現(xiàn)為導(dǎo)向,面向企事業(yè)單位項(xiàng)目實(shí)際需求,講師將會(huì)與您分享設(shè)計(jì)的全流程以及工具的綜合使用技巧與經(jīng)驗(yàn)。課程可定制,線下/線上/上門皆可,全國(guó)免費(fèi)報(bào)名熱線:4008699035。

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最新課程列表

MATLAB機(jī)器學(xué)習(xí)培訓(xùn)與咨詢

此為期兩天的課程重點(diǎn)介紹 MATLAB 中使用 Statistics Toolbox , Machine Learning Toolbox? 和
Deep Learning Toolbox? 功能的數(shù)據(jù)分析和機(jī)器學(xué)習(xí)技術(shù)。本課程
演示如何通過(guò)非監(jiān)督學(xué)習(xí)發(fā)現(xiàn)大數(shù)據(jù)集的特點(diǎn)，以及通過(guò)監(jiān)督學(xué)
習(xí)建立預(yù)測(cè)模型。課程中的示例和練習(xí)強(qiáng)調(diào)用于呈現(xiàn)和評(píng)估結(jié)果
的技巧。內(nèi)容包括：

·?組織和預(yù)處理數(shù)據(jù)

·?聚類數(shù)據(jù)

·?創(chuàng)建分類模型

·?評(píng)估和改善模型

·?化簡(jiǎn)數(shù)據(jù)集

·?改善模型性能 ?

課程要求

MATLAB 基礎(chǔ)課程

Day 1 of 2

Importing and Organizing Data

Objective:?Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.

·?Data types

·?Tables

·?Categorical data

·?Data preparation

Finding Natural Patterns in Data

Objective:?Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.

·?Unsupervised learning

·?Clustering methods

·?Cluster evaluation and interpretation

Building Classification Models

Objective:?Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.

·?Supervised learning

·?Training and validation

·?Classification methods

?

Day 2 of 2

Improving Predictive Models

Objective:?Reduce the dimensionality of a data set. Improve and simplify machine learning models.

·?Cross validation

·?Hyperparameter optimization

·?Feature transformation

·?Feature selection

·?Ensemble learning

Building Regression Models

Objective:?Use supervised learning techniques to perform predictive modeling for continuous response variables.

·?Parametric regression methods

·?Nonparametric regression methods

·?Evaluation of regression models

Creating Neural Networks

Objective:?Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.

·?Clustering with Self-Organizing Maps

·?Classification with feed-forward networks

·?Regression with feed-forward networks

?
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MATLAB機(jī)器學(xué)習(xí)培訓(xùn)與咨詢

課程要求

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MATLAB機(jī)器學(xué)習(xí)培訓(xùn)與咨詢-中心

課程介紹

最新課程列表

Day 1 of 2
Importing and Organizing Data	Objective:?Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values. ·?Data types ·?Tables ·?Categorical data ·?Data preparation
Finding Natural Patterns in Data	Objective:?Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set. ·?Unsupervised learning ·?Clustering methods ·?Cluster evaluation and interpretation
Building Classification Models	Objective:?Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model. ·?Supervised learning ·?Training and validation ·?Classification methods

Day 2 of 2
Improving Predictive Models	Objective:?Reduce the dimensionality of a data set. Improve and simplify machine learning models. ·?Cross validation ·?Hyperparameter optimization ·?Feature transformation ·?Feature selection ·?Ensemble learning
Building Regression Models	Objective:?Use supervised learning techniques to perform predictive modeling for continuous response variables. ·?Parametric regression methods ·?Nonparametric regression methods ·?Evaluation of regression models
Creating Neural Networks	Objective:?Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance. ·?Clustering with Self-Organizing Maps ·?Classification with feed-forward networks ·?Regression with feed-forward networks