曙海教學(xué)優(yōu)勢
該課程，秉承21年教學(xué)品質(zhì)，以項目實現(xiàn)為導(dǎo)向,面向企事業(yè)單位項目實際需求,講師將會與您分享設(shè)計的全流程以及工具的綜合使用技巧與經(jīng)驗。課程可定制,線下/線上/上門皆可,全國免費報名熱線:4008699035。
曙海培訓(xùn)的課程培養(yǎng)了大批受企業(yè)歡迎的工程師。大批企業(yè)和曙海
建立了良好的合作關(guān)系,合作企業(yè)三十多萬家。曙海的課程得到業(yè)內(nèi)企事業(yè)單位廣泛贊譽。
?此課程重點介紹 MATLAB 中使用 Statistics Toolbox , Machine Learning Toolbox? 和
Deep Learning Toolbox? 功能的數(shù)據(jù)分析和機器學(xué)習(xí)技術(shù)。本課程
演示如何通過非監(jiān)督學(xué)習(xí)發(fā)現(xiàn)大數(shù)據(jù)集的特點，以及通過監(jiān)督學(xué)
習(xí)建立預(yù)測模型。課程中的示例和練習(xí)強調(diào)用于呈現(xiàn)和評估結(jié)果
的技巧。內(nèi)容包括：
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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 |
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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 |
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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 |
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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 |
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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 |
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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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