曙海教学优势
曙海培训的课程培养了大批受企业欢迎的工程师。大批企业和曙海
建立了良好的合作关系。曙海培训的课程在业内有着响亮的知名度。
本课程，秉承二十一年积累的教学品质，以项目实现为导向,老师将会与您分享设计的全流程以及工具的综合使用经验、技巧。
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 |
|
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 |
|
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 |
