課程介紹

 

其他精品熱門課程班: NI機(jī)器視覺編程培訓(xùn) Ansys CFD流體力學(xué)培訓(xùn)課程班 Sigrity仿真培訓(xùn)班 aspen-plus 培訓(xùn)班 ICEPAK培訓(xùn)班 Simulink自動駕駛建模仿真培訓(xùn)班 電子產(chǎn)品可靠性設(shè)計（DFR）與失效分析（FA）培訓(xùn)班 SmartPlant3DDesign（SP3D設(shè)計）培訓(xùn) 管道應(yīng)力分析軟件培訓(xùn)班 汽車輕量化車身結(jié)構(gòu)設(shè)計與碰撞仿真基礎(chǔ)技術(shù)培訓(xùn)班 SPEOS培訓(xùn)班DSP培訓(xùn)班R語言臨床試驗數(shù)據(jù)實戰(zhàn)培訓(xùn)課程 Android Framework & HAL軟硬整合技術(shù)班 SPSS課程 icepak課程 ANSYS課程 疲勞分析培訓(xùn) 可靠性分析培訓(xùn)班 fluent課程班 DSP培訓(xùn)班班 CFD在建筑/環(huán)境領(lǐng)域的應(yīng)用培訓(xùn) PDPS仿真培訓(xùn)課程班（Tecnomatix Process Designer & Simulate培訓(xùn)） LACS培訓(xùn) FLOW培訓(xùn) 培訓(xùn)dsp Labview課程 SPSS培訓(xùn) Indesign培訓(xùn) Flotherm培訓(xùn) 培訓(xùn)Labview 虛擬實驗室培訓(xùn) 培訓(xùn)虛擬實驗室 Labview培訓(xùn) 培訓(xùn)虛擬實驗室課程 Labview培訓(xùn)機(jī)構(gòu) 培訓(xùn)Abaqus 培訓(xùn)有限元 Flexsim培訓(xùn)

 

 

曙海教學(xué)優(yōu)勢

　 本課程，秉承二十一年積累的教學(xué)品質(zhì)，以項目實現(xiàn)為導(dǎo)向,面向企事業(yè)項目實際需要,老師將會與您分享設(shè)計的全流程以及工具的綜合使用經(jīng)驗、技巧。課程可定制,線上/線下/上門皆可,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)30萬+。曙海培訓(xùn)的課程在業(yè)內(nèi)有著響亮的知名度。

 

最新課程列表

• ?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(jī)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(qiáng)調(diào)用于呈現(xiàn)和評估結(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
  • 評估和改善模型
  • 化簡數(shù)據(jù)集
  • 改善模型性能??
  • 詳細(xì)課程提綱：

  • 課程要求

    MATLAB 基礎(chǔ)

    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

    ?

  
  
  

  ---
• 咨詢在線客服