Correction misspelling

Author: Digusil

README.md

@@ -82,14 +82,14 @@ Also the options can be change:
 model.setOptions(options = {'multiH':True, 'scaleKernel':True, 'kernel':'epanechnikov2', 'powerList':[1/2, 2]})
 ```
 #### Learning
-The method ```.learnModel``` runs the learn algorithmus. The method provieds also the option to update the learn options.
+The method ```.learnModel``` runs the learn algorithm. The method provides also the option to update the learn options.
 
 ```python
 model.learnModel(options = {'multiH':True, 'scaleKernel':False, 'kernel':'cauchy', 'powerList':[1]})
 ```
 
 #### Estimate values
-To estimate values from the model some informations are needed. The main information are the points, where the model estimate the function. The point matrix have to be configurate like the input vector. To handle normalized and unnormalized data set the options ```normalInput=False``` and ```normalOutput=False```. To choose the targets use the option ```i_target = 'all'```. Valdi values for ```i_target``` are:
+To estimate values from the model some informations are needed. The main information are the points, where the model estimate the function. The point matrix have to be configure like the input vector. To handle normalized and unnormalized data set the options ```normalInput=False``` and ```normalOutput=False```. To choose the targets use the option ```i_target = 'all'```. Valid values for ```i_target``` are:
 * 'all': means all target will be estimated
 * number: means this target only, e.g: [1]
 * list: means all listed target only. e.g: [2,3]
@@ -99,14 +99,14 @@ estimatedTarget = model.estimateFunction(x = points, normalInput = False, normal
 ```
 
 #### Validate model
-Currently is one value and one graphical based method implemanted to validate the model. The method ```.goodnessOfFit``` calculates the goodness of fit based on the test subset. The option ```i_target = 'all'``` (equal as by the method ```.estimateFunction```) can be used to set the interesting targets. If more than one target is choosed, the returned goodness of fit represents the value ofer all choosed data.
+Currently is one value and one graphical based method implemented to validate the model. The method ```.goodnessOfFit``` calculates the goodness of fit based on the test subset. The option ```i_target = 'all'``` (equal as by the method ```.estimateFunction```) can be used to set the interesting targets. If more than one target is chose, Nadaraya the returned goodness of fit represents the value offer all chose data.
 
 ```python
-r2 = modell.goodnessOfFit(i_target = 1)
+r2 = model.goodnessOfFit(i_target = 1)
 ```
 
-The graphical method ```.plotRegression``` provides the option to plot the relation between the true and the estimated data. Also the gooddness of fit will be shown in the titel. As option the diffrent subsets ```dataset = 'all'``` and targets ```i_target = 'all'``` (equal as by the method ```.estimateFunction```) can be choose.
+The graphical method ```.plotRegression``` provides the option to plot the relation between the true and the estimated data. Also the goodness of fit will be shown in the title. As option the different subsets ```dataset = 'all'``` and targets ```i_target = 'all'``` (equal as by the method ```.estimateFunction```) can be choose.
 
 ```python
-modell.potRegression(dataset = [2,3], i_target = 2)
+model.potRegression(dataset = [2,3], i_target = 2)
 ```