Machine Learning With Python

step 0:​  Decide on a project that will force you to put it all together  ● labs.five.com  ● arxiv.org/abs/1503.07077  ● github.com/jbornschein/draw  ● github.com/hexahedria/biaxial­rnn­music­composition  ● Exploratory Data Analysis    step 1:​  Install ​ Anaconda​  and learn Pandas  ● 10 min to pandas  ● pandas data structures  ● example notebook    step 2:​  Get comfortable with Statistical Learning  ● k­nearest neighbor  ● k­means  ● naive bayes  ● kaplan­meier  ● gradient descent  ● matrix factorization  ● logistic regression & sgd  ● principal​  component analysis  ● model selection and validation    step 3:​  Go Deeper  1. Linear Algebra  ○ github.com/rougier/numpy­100  ○ coursera.org/course/matrix  2. Neural Networks  ○ courses  ■ Aaron Courville’s Course  ■ Hugo Larochelle's Course  ○ theano  ■ github.com/goodfeli/theano_exercises  ■ github.com/Newmu/Theano­Tutorials  ○ abstractions  ■ lasagne  ■ keras  ■ blocks  ○ overview  ■ iamtrask.github.io/2015/07/12/basic­python­network  ■ github.com/syhw/DL4H  3. Datasets  ○ Kaggle​  ­ ​ mlwave.com/kaggle­ensembling­guide  ○ Open Data  ○ Scraping  4. Books  ○ Pattern Recognition and Machine Learning 

5.

○ Elements of Statistical Learning  ○ Learning with Kernels  ○ Reinforcement Learning: An Introduction  ○ Model Based Machine Learning  More Topics  ○ NLP  ■ bugra.github.io/work/notes/2015­02­21/topic­modeling­for­the­uninitiated  ■ radimrehurek.com/2014/02/word2vec­tutorial  ■ github.com/cgpotts/cs224u  ■ honnibal.github.io/spaCy  ○ Tree Methods  ■ quora.com/how­do­random­forests­work  ■ rf example  ■ github.com/dmlc/xgboost  ■ xgboost example  ○ Hyperparameter Optimization  ■ johnmyleswhite.com/notebook/2012/07/21/automatic­hyperparameter­tuning  ■ github.com/hyperopt/hyperopt  ■ github.com/craffel/simple_spearmint  ■ jmlr.org/papers/volume13/bergstra12a/bergstra12a  ○ Reinforcement Learning  ■ David Silver’s Course  ■ github.com/rlpy/rlpy  ■ github.com/spragunr/deep_q_rl  ■ jmlr.org/proceedings/papers/v37/schaul15.pdf  ■ ijcai.org/papers15/Papers/IJCAI15­638.pdf  ○ Transfer Learning  ■ iro.umontreal.ca/~lisa/pointeurs/UTLC_LISA  ■ http://arxiv.org/abs/1411.1792  ■ arxiv.org/pdf/1206.6407  ■ idsia.ch/~ciresan/data/ijcnn2012_v9  ■ icml­2011.org/papers/342_icmlpaper