曙海教学优势
免费报名电话:4008699035。线下/线上/上门都可以，课程支持定制。秉承21年教研经验，本课程以项目案例为依托,面向企事业单位实际需求,讲师将会与您分享设计的全流程以及工具的综合使用技巧及项目经验。
我们的课程培养了大批受欢迎的工程师。大批企业与我们
建立了良好的合作关系,合作企业三十多万家。我们的课程得到广大企事业单位广泛认可与赞赏。
此课程重点介绍 MATLAB 中使用 Statistics Toolbox , Machine Learning Toolbox™ 和
Deep Learning Toolbox™ 功能的数据分析和机器学习技术。本课程
演示如何通过非监督学习发现大数据集的特点，以及通过监督学
习建立预测模型。课程中的示例和练习强调用于呈现和评估结果
的技巧。内容包括：
|
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 |
|
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 |
