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    使用MATLAB 進(jìn)行機(jī)器學(xué)習(xí)課程培訓(xùn)

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
化簡數(shù)據(jù)集
改善模型性能
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