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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