fixing the spelling errors in documentation

book/content/_build/html/starting.html

@@ -337,7 +337,7 @@
 <h1>Getting started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h1>
 <section id="demo-dissimilarity-based-selection-synthetic-data">
 <h2>Demo - Dissimilarity Based Selection Synthetic Data<a class="headerlink" href="#demo-dissimilarity-based-selection-synthetic-data" title="Permalink to this headline">¶</a></h2>
-<p>In this tutorial, we are going to show how the <code class="docutils literal notranslate"><span class="pre">QC-Selector</span></code> work with 2D synthstic data. Each axis represents one feature. For each example, we will have two different synthstic datasets where the first one can be seen as randomly generated data points and the latter generates data points beloing to different clusters. By using 2D feature data points, it makes visualization easy.</p>
+<p>In this tutorial, we are going to show how the <code class="docutils literal notranslate"><span class="pre">QC-Selector</span></code> work with 2D synthetic data. Each axis represents one feature. For each example, we will have two different synthetic datasets where the first one can be seen as randomly generated data points and the latter generates data points belonging to different clusters. By using 2D feature data points, it makes visualization easy.</p>
 </section>
 <section id="generating-synthetic-data">
 <h2>Generating Synthetic Data<a class="headerlink" href="#generating-synthetic-data" title="Permalink to this headline">¶</a></h2>

book/content/_build/jupyter_execute/starting.py

@@ -3,7 +3,7 @@
 # # Getting started
 # ## Demo - Dissimilarity Based Selection Synthetic Data
 
-# In this tutorial, we are going to show how the `QC-Selector` work with 2D synthstic data. Each axis represents one feature. For each example, we will have two different synthstic datasets where the first one can be seen as randomly generated data points and the latter generates data points beloing to different clusters. By using 2D feature data points, it makes visualization easy.
+# In this tutorial, we are going to show how the `QC-Selector` work with 2D synthetic data. Each axis represents one feature. For each example, we will have two different synthetic datasets where the first one can be seen as randomly generated data points and the latter generates data points belonging to different clusters. By using 2D feature data points, it makes visualization easy.
 
 # ## Generating Synthetic Data