There are various cheat-sheets&#x2F;decision trees to suggest which machine learning method e.g.<p><a href="http:&#x2F;&#x2F;www.saedsayad.com&#x2F;data_mining_map.htm" rel="nofollow">http:&#x2F;&#x2F;www.saedsayad.com&#x2F;data_mining_map.htm</a><p><a href="http:&#x2F;&#x2F;peekaboo-vision.blogspot.co.uk&#x2F;2013&#x2F;01&#x2F;machine-learning-cheat-sheet-for-scikit.html" rel="nofollow">http:&#x2F;&#x2F;peekaboo-vision.blogspot.co.uk&#x2F;2013&#x2F;01&#x2F;machine-learni...</a><p><a href="https:&#x2F;&#x2F;azure.microsoft.com&#x2F;en-us&#x2F;documentation&#x2F;articles&#x2F;machine-learning-algorithm-cheat-sheet&#x2F;" rel="nofollow">https:&#x2F;&#x2F;azure.microsoft.com&#x2F;en-us&#x2F;documentation&#x2F;articles&#x2F;mac...</a><p>However, if you want to really understand how things fit together you&#x27;re probably best reading one of the standard intro textbooks: Murphy&#x27;s <i>Machine Learning</i>, Bishop&#x27;s P<i>attern recognition and machine learning</i>, Hastie et al&#x27;s <i>The Elements of Statistical Learning</i>, or Wasserman&#x27;s <i>All of statistics</i>.<p>Or Barber&#x27;s textbook, which is freely available online and has some nice mind-maps&#x2F;concept-maps&#x2F;trees at the start of each section: <a href="http:&#x2F;&#x2F;web4.cs.ucl.ac.uk&#x2F;staff&#x2F;D.Barber&#x2F;pmwiki&#x2F;pmwiki.php?n=Brml.HomePage?from=Main.Textbook" rel="nofollow">http:&#x2F;&#x2F;web4.cs.ucl.ac.uk&#x2F;staff&#x2F;D.Barber&#x2F;pmwiki&#x2F;pmwiki.php?n=...</a>