<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>geebee</author><text>I think you&amp;#x27;re in a position to learn a lot in a week.  My advice would be to create a simple data set and go through what you learned in Andrew Ng&amp;#x27;s course with scikit-learn (even better, since you already prefer Python).&lt;p&gt;&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;scikit-learn.org&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;scikit-learn.org&lt;/a&gt;&lt;p&gt;Much of what you learn in Ng&amp;#x27;s course is how to implement these algorithms - there&amp;#x27;s less (no?) emphasis on using existing libraries in R or Python.  I think that implementing your own code base for logistic regression, neural net, random forest, and so forth is an extremely valuable exercise, but I&amp;#x27;d recommend you put that aside just for the moment.  Instead, try using some of the existing libraries.&lt;p&gt;For instance, use scikit-learn&amp;#x27;s logistic regression, neural net, and random forest (not covered in Ng&amp;#x27;s class) libraries to do a classification.  You don&amp;#x27;t want to use these with no understanding of how they work, but you&amp;#x27;ve done the coursera, so see if you can use your knowledge of how these algorithms differ to create dataset that will highlight the benefits each approach (i.e.., can you create a dataset that works great for logistic regression but poorly for neural nets, or random forest?)  Think about how you&amp;#x27;d use an unsupervised approach to classification, and run it through k-means.  I really think that applying different techniques to the same dataset, on a high level, combined with general knowledge of what&amp;#x27;s going on under the hood, can be a great way to understand how&amp;#x2F;when to use these algorithms.</text><parent_chain></parent_chain></comment><story><title>Ask HN: How does a beginner best spend one week of learning machine learning?</title><text>I&amp;#x27;ll spend all my time next week studying machine learning. However, I know that it&amp;#x27;s easy to get lost in overly theoretical material when learning ML.&lt;p&gt;So I&amp;#x27;m interested in some advice on how to best spend my time, given that I&amp;#x27;d like to get as much practical knowledge as possible.&lt;p&gt;Here is the level I&amp;#x27;m at now:&lt;p&gt;I&amp;#x27;m currently halfways though Andrew Ng&amp;#x27;s ML course on Coursera, and will probably finish this within the week. I love the mix of theory an practice this course is based around.&lt;p&gt;I&amp;#x27;ve also done the Udacity - Intro to Machine Learning, but found it too theoretical.&lt;p&gt;I kind of understand the basic principles of linear &amp;amp; logistic regression, cost functions and gradient descent &amp;amp; the normal equation.&lt;p&gt;By the end of the week, I hope to be able to do linear regression using gradient descent on an actual dataset. If so, the week has been very well spent!&lt;p&gt;My preferred language is Python.&lt;p&gt;All tips and suggestions are highly appreciated :)</text></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>chollida1</author><text>Honestly, with only a week you can&amp;#x27;t really learn a lot so I&amp;#x27;d recommend doing something practical.  See the canonical answer here:&lt;p&gt;&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;norvig.com&amp;#x2F;21-days.html&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;norvig.com&amp;#x2F;21-days.html&lt;/a&gt;&lt;p&gt;Go to Kaggle and do one of the competitions.&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.kaggle.com&amp;#x2F;competitions&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.kaggle.com&amp;#x2F;competitions&lt;/a&gt;&lt;p&gt;and reference the wiki for help:&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.kaggle.com&amp;#x2F;wiki&amp;#x2F;Home&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.kaggle.com&amp;#x2F;wiki&amp;#x2F;Home&lt;/a&gt;&lt;p&gt;In my opinion, the only way to learn machine learning without a strong foundation is to page in learning.  That means you&amp;#x27;ll need to pick a practical task and just jump into it and find out what you don&amp;#x27;t know.&lt;p&gt;If you&amp;#x27;ve never done anything practical, great you&amp;#x27;ll need to learn about the basic data structures of machine learning.&lt;p&gt;Find out what a data frame is, what time series analysis is and what data structures you need to do it.&lt;p&gt;Are you going to do classification or prediction?  Pick one and learn some of the basic tools used for it.</text><parent_chain></parent_chain></comment><story><title>Ask HN: How does a beginner best spend one week of learning machine learning?</title><text>I&amp;#x27;ll spend all my time next week studying machine learning. However, I know that it&amp;#x27;s easy to get lost in overly theoretical material when learning ML.&lt;p&gt;So I&amp;#x27;m interested in some advice on how to best spend my time, given that I&amp;#x27;d like to get as much practical knowledge as possible.&lt;p&gt;Here is the level I&amp;#x27;m at now:&lt;p&gt;I&amp;#x27;m currently halfways though Andrew Ng&amp;#x27;s ML course on Coursera, and will probably finish this within the week. I love the mix of theory an practice this course is based around.&lt;p&gt;I&amp;#x27;ve also done the Udacity - Intro to Machine Learning, but found it too theoretical.&lt;p&gt;I kind of understand the basic principles of linear &amp;amp; logistic regression, cost functions and gradient descent &amp;amp; the normal equation.&lt;p&gt;By the end of the week, I hope to be able to do linear regression using gradient descent on an actual dataset. If so, the week has been very well spent!&lt;p&gt;My preferred language is Python.&lt;p&gt;All tips and suggestions are highly appreciated :)</text></story>