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MachineLearning

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Implementations of machine learning algorithm by Python 3

MachineLearning 최신버젼 다운로드

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# Machine Learning
Implementations of machine learning algorithm by Python 3

The repository provides demo programs for implementations of basic machine learning algorithms by Python 3. I hope these programs will help people understand the beauty of machine learning theories and implementations.

I will enrich those implementations and descriptions from time to time. If you include any of my work into your website or project; please add a link to this repository and send me an email to let me know.

Your comments are welcome.
Thanks,

|Algorithm|Description|Link|
|------|------|--------|
|[Decision Tree](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/DecisionTree)|By measuring information gain via calculating the entropy of previous observations, decision tree algorithm may help us to predict the decision or results|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/DecisionTree/TechnicalSpecification-%5BDecisionTree%5D-%5B1.1%5D-%5B20160929%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/DecisionTree/DecisionTree.py)|
|[Fast Map](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/FastMap)|An approach for dimensions reductions|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/FastMap/TechnicalSpecification-%5BPCA_FastMap%5D-%5B1.0%5D-%5B20160929%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/FastMap/FastMap.py)|
|[Gaussian Mixture Models (GMMs)](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/GMM)|GMMs are among the most statistically mature methods for data clustering (and density estimation)|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/GMM/INF552-TechnicalSpecification-%5Bk-means_EM-GMM%5D-%5B1.2%5D-%5B20170515%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/GMM/GMM.py)|
|[Hierarchical clustering (HAC)](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/HAC)|HCA seeks to build a hierarchy of clusters from bottom up or top down. This is a bottom up implementation.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/HAC/HAC.py)|
|[Hidden Markov model (HMM) and Viterbi](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/HMM)|HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states. The Viterbi algorithmis used to compute the most probable path (as well as its probability). It requires knowledge of the parameters of the HMM model and a particular output sequence and it finds the state sequence that is most likely to have generated that output sequence. It works by finding a maximum over all possible state sequences. In sequence analysis, this method can be used for example to predict coding vs non-coding sequences.|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/HMM/INF552-TechnicalSpecification-%5BHMM%5D-%5B1.0%5D-%5B20161203%5D.pdf) and [Viterbi Algorithm Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/HMM/HMM-Viterbi.py)|
|[K-Means](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/K-Means)|One of most famous and easy to understand clustering algorithm|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/K-Means/K-Means.py)|
|[Neural Network](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/NeuralNetwork)|The foundation algorithm of deep learning|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/NeuralNetwork/INF552-TechnicalSpecification-%5BNeuralNetwork%5D-%5B1.0%5D-%5B20161104%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/NeuralNetwork/NeuralNetwork.py)|
|[PCA](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/PCA)|An algorithm for dimension reductions. PCA is a statistical technique, via orthogonal transformation, convert dataset that some of them may correlated to a new data space that is linearly uncorrelated set of values. This new set of data call principal components. PCA is sensitive to the relative scaling of the original variables, so before applying PCA, data pre-processing step is very important and we should always do. Mean normalization (xi - mean of the feature) or feature scaling (xi - mean)/max(xi) or (xi - mean)/(Standard deviation of x) then replace xi by the new value for each feature are required. |[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/PCA/INF552-TechnicalSpecification-PCA_FastMap-%5B1.0%5D-%5B20161011%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/PCA/PCA.py)|
|[Neural Network and Long Short Term Memory (LSTM) on Tensorflow](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/TensorFlow)|This is a project which implemented Neural Network and Long Short Term Memory (LSTM) to predict stock price in Python 3 by Tensorflow|[Project Report](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/TensorFlow/ProjectReport.pdf) and [MLP Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/TensorFlow/StockPriceForecasting-MLP.py), [LSTM Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/TensorFlow/StockPriceForecasting-LSTM.py)|
|[Linear regression on scikit-learn](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/scikit-learn)|Linear regression is a linear modeling to describe the relation between a scalar dependent variable y and one or more independent variables, X. This example shows how to use scikit-learn package.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/LinearRegression/sklearn-LinearRegression.py)|
|[Logistic regression on scikit-learn](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/scikit-learn)|logit regression. It is different to regression analysis. A linear probability classifier model to categorize random variable Y being 0 or 1 by given experiment data. Assumes each of categorize are independent and irrelevant alternatives. The model p(y=1\|x, b, w) = sigmoid(g(x)) where g(x)=b+wTx. The sigmoid function = 1/1+e^(-a) where a = g(x). This example shows how to use scikit-learn package.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/LogisticRegression/logistic_regression.py)|
|[Gaussian Mixture Models (GMMs) on scikit-learn](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/scikit-learn)|GMMs are among the most statistically mature methods for data clustering (and density estimation). It assumes each component generates data from a Gaussian distribution. This example shows how to use scikit-learn package.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/KMean_GMM/k-means_EM-GMM.py)|
|[K-Means on scikit-learn](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/scikit-learn)|One of most famous and easy to understand clustering algorithm. This example shows how to use scikit-learn package.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/KMean_GMM/k-means_EM-GMM.py)|
|[PLA on scikit-learn](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/scikit-learn)|Perceptron Learning Algorithm. A solver for binary classification task. This example shows how to use scikit-learn package.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/PLA/sklearn-Perceptron.py)|
|[Support Vector Regression (SVR), Neural Network, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/Competition/McKinseyAnalyticsPrediction) | This task is to perform prediction for number of vehicles by given data. This is a demo program to leverage mutile models from existing libraries in one challenge. The final result can be improved by some emsemble techniques like Bootstrap aggregating (bagging), boosting, and stacking to get better performance.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/Competition/McKinseyAnalyticsPrediction/NumberOfVehiclesPrediction.ipynb)|

## Reference
* [Machine Learning at Coursera](https://www.coursera.org/learn/machine-learning/)
* [Machine Learning Course CS229 at Stanford](https://see.stanford.edu/Course/CS229)
* [Machine Learning Foundations at NTU](https://www.youtube.com/watch?v=nQvpFSMPhr0&list=PLXVfgk9fNX2I7tB6oIINGBmW50rrmFTqf)
* [Machine Learning Techniques at NTU](https://www.youtube.com/watch?v=A-GxGCCAIrg&list=PLXVfgk9fNX2IQOYPmqjqWsNUFl2)
* [Hidden Markov model (HMM)](https://en.wikipedia.org/wiki/Hidden_Markov_model)
* [Viterbi Algorithm](http://homepages.ulb.ac.be/~dgonze/TEACHING/viterbi.pdf)
* [LSTM](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
* [PCA at wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis)

## Disclaimer
Last updated: March 13, 2018

The information contained on https://github.com/Cheng-Lin-Li/ website (the "Service") is for general information purposes only.
Cheng-Lin-Li's github assumes no responsibility for errors or omissions in the contents on the Service and Programs.

In no event shall Cheng-Lin-Li's github be liable for any special, direct, indirect, consequential, or incidental damages or any damages whatsoever, whether in an action of contract, negligence or other tort, arising out of or in connection with the use of the Service or the contents of the Service. Cheng-Lin-Li's github reserves the right to make additions, deletions, or modification to the contents on the Service at any time without prior notice.

### External links disclaimer

https://github.com/Cheng-Lin-Li/ website may contain links to external websites that are not provided or maintained by or in any way affiliated with Cheng-Lin-Li's github.

Please note that the Cheng-Lin-Li's github does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

## Contact Information
Cheng-Lin Li@University of Southern California

chenglil@usc.edu or

clark.cl.li@gmail.com

MachineLearning

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