Lists of My Targeted Machine Learning Algorithms (TBC)

1.PrimaryAlgorithm List

• Linear regression （线性回归）：
• Logistic regression（逻辑回归,"评估模型"）：
• Decision tree learning（决定树学习）: C4.5/C5.0 and ID3 algorithms.
• k-Means（k-平均算法）: clustering algorithm.
• SVM(Support Vector Machines，支持向量机):
• Apriori （先验算法）:  rule extraction.
• EM(Expectation Maximization, 最大期望算法):
• PageRank（佩奇排名算法）: graph-based problems.
• AdaBoost:  family of boosting ensemble methods.
• KNN(k-nearest neighbor): instance-based method.
• Naive Bayes: Simple and robust use of Bayes theorem on data.
• CART(Classification And Regression Trees): tree-based method.
• RBFN(Radial Basis Function Network)
• Random Forest
• CNN
• LASSO
• Logistic
• MARS
• Collaborative filtering;
• (Bonus) Inference via graphical models;
• Latent factor models based on low-rank matrix factorization like the Singular value decomposition or simple alternating least squares;
• Bonus: MCMC methods (Markov chain Monte Carlo) for graphical models;

2.Examples of Algorithm Lists To Create Projects (T.B.C.)

Below are 10 examples of machine learning algorithm lists that you could create projects in these days:

• Regression algorithms
• SVM algorithms
• Data projection algorithms
• Deep learning algorithms
• Time series forecasting algorithms
• Rating system algorithms
• Recommender system algorithms
• Feature selection algorithms
• Class imbalance algorithms
• Decision tree algorithms

 

3.More

下面几篇博文，我觉得写得很好，可以作为梳理ML知识点的map:

Why you should be Spot-Checking Algorithms on your Machine Learning Problems

CRAN Task View: Machine Learning & Statistical Learning

How To Get Started With Machine Learning Algorithms in R by Jason Brownlee

A Tour of Machine Learning Algorithms by Jason Brownlee

Github:Practical Machine Learning

Github:Practical Machine Learning Project

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