Update README.md

Author: Cheng-Lin-Li

README.md

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-# Machine Learning
+ # 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.
@@ -19,11 +19,11 @@ Thanks,
 |[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|Linear regression is a linear modeling to describe the relation between a scalar dependent variable y and one or more independent variables, X.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/LinearRegression/sklearn-LinearRegression.py)|
-|Logistic regression|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).|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/LogisticRegression/logistic_regression.py)|
-|Gaussian Mixture Models (GMMs)|GMMs are among the most statistically mature methods for data clustering (and density estimation). It assumes each component generates data from a Gaussian distribution.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/KMean_GMM/k-means_EM-GMM.py)|
-|K-Means|One of most famous and easy to understand clustering algorithm|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/KMean_GMM/k-means_EM-GMM.py)|
-|PLA|Perceptron Learning Algorithm. A solver for binary classification task. |[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/scikit-learn/PLA/sklearn-Perceptron.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)|