what are some macine learning algorithms that you should always have a strong understanding of and-优快云博客

本文介绍了几项核心机器学习技术，包括回归、分类、聚类及协同过滤等，并深入探讨了每种技术的重要概念与应用场景，如线性回归、支持向量机、随机森林、K-means++聚类及基于低秩矩阵分解的协同过滤模型。

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this airtcle is wrote by Sean Owen,Director ,Data Science @Cloudera

I think one needs to have a competent knowledge of 1-2 techniques in:

Regression
Classification
Clustering
Collaborative filtering
(Bonus) Inference via graphical models

Certainly, it's valuable and important to understand simple Linear regression .

Gradient descent is important because it underpins common classifier techniques like Logistic regression . Also: the Support vector machine .

I also strongly enc ourage people to have a working knowl edge of Random forest classification / regression. It's inherently an ensemble method, effective, and has different properties from the above.

K-means++ clustering is a must.

For collaborative fi ltering, neigh borhood metho ds are simple enough that almost don't deserve me n tion. I would try t o understand latent fa cto r models based on low-ran k matrix facto rizati on like the Singular value decomposition or simple alternating least squares ( http://yifanhu.net/PUB/cf.pdf )

Bonus: MCMC methods ( Markov chain Monte Carlo ) for graphical models.