Fix modules/ typos

Found using `codespell -q 3 -S ./3rdparty -L activ,amin,ang,atleast,childs,dof,endwhile,halfs,hist,iff,nd,od,uint`

backporting of commit: ec43292e1e

modules/ml/doc/ml_intro.markdown

@@ -433,7 +433,7 @@ Logistic Regression {#ml_intro_lr}
 ML implements logistic regression, which is a probabilistic classification technique. Logistic
 Regression is a binary classification algorithm which is closely related to Support Vector Machines
 (SVM). Like SVM, Logistic Regression can be extended to work on multi-class classification problems
-like digit recognition (i.e. recognizing digitis like 0,1 2, 3,... from the given images). This
+like digit recognition (i.e. recognizing digits like 0,1 2, 3,... from the given images). This
 version of Logistic Regression supports both binary and multi-class classifications (for multi-class
 it creates a multiple 2-class classifiers). In order to train the logistic regression classifier,
 Batch Gradient Descent and Mini-Batch Gradient Descent algorithms are used (see

modules/ml/include/opencv2/ml.hpp

@@ -1760,7 +1760,7 @@ Note that the parameters margin regularization, initial step size, and step decr
 
 To use SVMSGD algorithm do as follows:
 
-- first, create the SVMSGD object. The algoorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(),
+- first, create the SVMSGD object. The algorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(),
   setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower().
 
 - then the SVM model can be trained using the train features and the correspondent labels by the method train().

modules/ml/test/test_emknearestkmeans.cpp

@@ -614,7 +614,7 @@ protected:
 
         if( data.empty() )
         {
-            ts->printf(cvtest::TS::LOG, "File with spambase dataset cann't be read.\n");
+            ts->printf(cvtest::TS::LOG, "File with spambase dataset can't be read.\n");
             ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
             return;
         }