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　 本課程，秉承二十一年積累的教學(xué)品質(zhì)，以項(xiàng)目實(shí)現(xiàn)為導(dǎo)向,面向企事業(yè)項(xiàng)目實(shí)際需要,老師將會(huì)與您分享設(shè)計(jì)的全流程以及工具的綜合使用經(jīng)驗(yàn)、技巧。課程可定制,線上/線下/上門皆可,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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