Update learners.ipynb

tutorials/learners.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Learners\n",
+    "# Custom learners in Orange\n",
     "Orange contains many learners which can be used to fit models for classification, regression and other tasks. But it is also very simple to write your own learner. To start, define a subclass of the `Orange.classification.Learner` base class and implement either one or both of the fit methods: `fit` works on data matrices represented as numpy arrays, while the more general `fit_storage` uses the encapsulating `Orange.data.Storage` object (or a subclass such as `Orange.data.Table`).\n",
     "After the necessary computations, the learner should produce a fitted model object, derived from the `Orange.classification.Model` base class, which needs to implement `predict` or `predict_storage`."
    ]