开发者学堂课程【机器学习入门-概念原理及常用算法：总结与练习】学习笔记，与课程紧密联系，让用户快速学习知识。
课程地址：https://developer.aliyun.com/learning/course/355/detail/4187
总结与练习
内容简介：
一、总结
二、练习
三、参考书籍
一、总结
Summary
·Algorithms Grouped by Learning Style∶ 监督、非监督.
·Algorithms Grouped By Similarity∶ 回归、分类
·Machine Learning when/how/why? Try to implement a ML algorithm
·Learning= Representation+Evaluation+Optimization + Data + Algorithm + System...
二、练习
·使用http∶//gitlab.alibaba-inc.com/jun.zhoujun/ml-base/wikis/criteo_100000_data，10万条 criteo 数据，尝试 logistic regression，decision tree 等算法，进行数据处理、特征分析，建立模型
·分析 loss、收敛等情况，绘制收敛曲线
·Check 模型是否有效，并分析各个特征的重要性
三、参考书籍
（一）Machine learning Platform
·Parameter Server：http://help,aliyun-inc.com/internaldoc/detail/34553.html
·PAI：http//help.aliyun-inc.com/internaldoc/detail/34571.html?spm=0.0.0.0.xjPVc2
·XLB: http://help.aliyun-inc.com/internaldoc/detail/34566.html?spm=0.0.0.0.VhzaNp
·Spark-MLib: http://spark.apache.org/docs/latest/mllib-guide.html
Tensorflows：www.tensorflow.org
MXNet: http://mxnetreadthedocs.io/en/latest/
·Caffe ：
http://cafe.berkeleyvision.org//Kaldi:kaldi-asr.org/ Keras:htps://keras.io/ Scikit-learn:http://scikit-learn.org/
Theano: http://deeplearning.net/software/theano/
Weka: http:/www.cs.waikato.ac.nz/mi/weka/
（二）参考书籍
·[PRMLC. M. Bishop,Pattern Recognition and Machine Learning, Springer, 2006
·[Elements] T. Hastie,R. Tibshirani &J.Friedman, The Elements of Statistical earning:Data Mining, Inference, and Prediction (2nd ed, Springer,2009
·Yaser S.Abu-Mostafa, Malik Magdon-Ismail,Hsuan-Tien Lin, Learning From Data, 2012
（三）引用资料
·http/whatthefis.ml
·https://www.csie.ntu.edu.tw/~htlin/course
·www.cs.nyu.edu/mohrimls
·http//wwwcs.cmu.edu/~10601b/s15_Lecture.html
·http://www.cs.cmu.edu/~epxing/Class/10701/lecture.html
·http://www.cs.cmu.edu/afs/cs/academic/class/15780-s16/www/#schedule
·https://www.quora.com/How-do-learn-machine-learning-1
·http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
·https://gist.github.com/marcelcaraciolo/1321575
·http://aimotion.blospot.com/2011/10/machine-learning-with-python-linear.html
·http//archive.icsuci.edu/ml/
·https://people.eecs.berkeley.edu/~russell/classes/cs1994/f11/lectures/CS194%20Fall%2020 11%20Lecture%2006.pdf
·http://aimotion.blogspot.com/2011/11/machine-learning-with-python-logistic.html
·http://wwwcs.cmu.edu/~10601b/slides/LR.pdf
·http://www.cs.cmu.edu/~epxing/Class/10701/slides/LR15.pdf
·https//see.stanford.edu/materials/aimlcs229/ml-advice.pdf
·http://disp.ee.ntu.edu.tw/~pujols/Machine%20Learning%20Tutorial.pdf
·http://pages.cs.wiscedu/~dpage/cs760/evaluating.pdf
·http/wwwcomp.dit ie/bmacnamee/materials/ml/lectures/Evaluation.pdf
·http://web.cecs.pdx.edu/~mm/MachineLearningWinter2010/pdfslides/EvaluatingHypothe ses,pdf
·http://www.slideshare.net/sachinnagargoje1/introduction-to-machinelearningatsapthgiricollegebangalore
·https//ufal.mffcunicz/~zabokrtsky/courses/npfl104/html/feature_engineering.pdf
·http://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/
·https://www.quora.com/What-are-some-best-practices-in-Feature-Engineering
·https://people.eecs.berkeley.edu/~jordan/courses/294-fall09/lectures/feature/slides.pdf
·https://ww.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer
·http//blog.echen.me/2011/04/27/choosing-a-machine-learning-classifier/
·http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-learing-classifier
·https//ww.quora.com/What-are-the-typical-use-cases-for-different-machine-learning-algorithms
·https//www.quora.comMhat-are-the-advantages-of-dfferent cassfication-algorithms
·http//www.cS.cmuedu/~10601b/slides/clustering.pdf
·http://wwcs.cmuedu/~10501b/slides/DTree kNNpdf
·http://wwslideshare.net/twdsconf/practical-isues-in-machine-learning
