[docs] fix typo: one-hot coding should be one-hot encoding (#3898)

* Update Python-Intro.rst

* Update docs/Python-Intro.rst
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

docs/Python-Intro.rst

@@ -97,7 +97,7 @@ In LightGBM, the validation data should be aligned with training data.
     train_data = lgb.Dataset(data, label=label, feature_name=['c1', 'c2', 'c3'], categorical_feature=['c3'])
 
 LightGBM can use categorical features as input directly.
-It doesn't need to convert to one-hot coding, and is much faster than one-hot coding (about 8x speed-up).
+It doesn't need to convert to one-hot encoding, and is much faster than one-hot encoding (about 8x speed-up).
 
 **Note**: You should convert your categorical features to ``int`` type before you construct ``Dataset``.