<story><title>Machine Learning Books That Helped Me Level Up</title><url>http://www.datastuff.tech/data-science/3-machine-learning-books-that-helped-me-level-up-as-a-data-scientist/</url></story><parent_chain></parent_chain><comment><author>Tycho</author><text>I think the data scientist hiring frenzy may soon collapse as large companies run out of patience with their recently formed data science departments that struggle to deliver ROI (for many reasons, often not the fault of the data scientists). But I think we are not yet at the final iteration of the job market for these sorts of skills. Companies usually don&amp;#x27;t really want someone who specializes in model tuning and algorithm creation, they want something like a &amp;quot;full-stack data analyst&amp;quot; - someone who acknowledges that the modeling may be 2% of the effort and the rest is business analysis, data wrangling, engineering, stakeholder management, building tools for users&amp;#x2F;operators, etc., and rolls up their sleeves to deliver an end-to-end solution. There does not yet exist a catchy name for this role, but I bet that in a few years it will be what everyone wants to hire. So skate to where the puck will be...</text></comment>
<story><title>Machine Learning Books That Helped Me Level Up</title><url>http://www.datastuff.tech/data-science/3-machine-learning-books-that-helped-me-level-up-as-a-data-scientist/</url></story><parent_chain></parent_chain><comment><author>remilouf</author><text>I really recommend Murphy’s “Machine Learning: a probabilistic perspective”. Murphy’s lays the groundwork for understanding how the algorithms work, why and how they could be adapted to the problem you’re dealing with. It takes you from complete beginner (with a reasonable math level) to one step above `import sklearn as sk`.&lt;p&gt;The other books I read make the field look like a bunch of heuristics that just happen to work.</text></comment>