Popular Machine Learning Data Skills
Machine learning is a rapidly growing segment of computer science and engineering. The discipline combines skills from multiple areas including programming, computer science, information technology, data science and engineering. The general idea behind machine learning is to give computers the ability to learn without being explicitly programmed. They are valuable for any business and customer base. Here’s a list of 3 skills and software packages that are popular machine learning data skills.
Apache Spark + MLlib
Apache Spark is one of the best and features rich tools to utilize for machine learning applications. If you are serious about developing machine learning applications, you should install spark. Spark got its start as an open source cluster computing framework.
It is important to note you will require the MLlib library for machine learning applications. MLlib is phenomenal due to its ease of use. It can be used in Python, R, Java, and Scala. It simply plugs into Spark’s APIs and proceeds to interoperate with ease. Additionally, MLlib provides high-quality algorithms that run 100 times faster than MapReduce. This is because Spark was designed for and excels at iterative computing. This enables MLlib to run lightning fast. Simultaneously, MLlib was designed with algorithmic performance in mind. MLlib contains high-quality algorithms that leverage iteration that yields better results than the one-pass approximations associated with MapReduce. Ease of deployment is also a strength of Spark and MLlib. Both are capable of using existing Hadoop clusters and data.
A large part of machine learning is knowing how to make sense from big data. This is done by using statistics to draw conclusions from previous experiences in order to predict future outcomes. R is an open source and free software environment for statistical computing and graphics, so it excels at this type of data analysis. It also is widely considered one of the most powerful software packages for applied machine learning. What makes R a great skill to have for machine learning projects is its compatibility with other languages. Users can write C, C++, Java, .NET or Python code to manipulate R objects directly. R also has a much stronger object-oriented programming features than the majority of other statistical computing languages. Additionally, R is supported by just about every IDE that is available. A short list includes Eclipse, Emacs, Notepad++, Studio, R Tools for Visual Studio, LyX and much more. It also supports MLlib as previously mentioned, which is a huge bonus.
SQL (pronounced “sequel”) deserves an honorable mention in the top 3 machine learning skills because it is the most common way that large amounts of data are stored. It goes without saying that a good computer scientist that has an emphasis on machine learning should be able to manipulate large amounts of data. There are numerous SQL packages available for use. MySQL, Oracle, Microsoft SQL Studio, and much more. Many times, a machine learning algorithm will be pulling data sets from SQL tables, so understanding the nuts and bolts behind SQL is paramount for any machine learning application. SQL is one of the easier languages to learn as it contains only 13 unique operators. With these 13 operators, a programmer can run queries to select data from the database based upon certain conditions. An example would be the following code snippet:
WHERE Sex = male
ORDER BY name;
This code would select all of the males from the table named “Passengers” and order them by name for further data analysis. It is also possible to manipulate data tables with the INSERT operator to expand datasets used for machine learning.
A great place to start learning SQL would be the courses provided by W3Schools listed below:
A great example SQL server for beginners that is free is Microsoft SQL Studio Express. It can be downloaded online.
Machine learning data skills are valuable to get your tech working for you instead of you having to spend hours on setting it up. It will be important for you to get customer data and sort through it. Big data will teach you everything you need to know about consumers, and machine learning is how you can easily gather it.
About the author:
Finn Pierson is a freelance writer and entrepreneur who specializes in business technology. He is drawn to the technological world because of its quickly paced and constantly changing environment. He believes embracing technology is essential to capturing success in any business and strives to inspire and encourage top technological practices in business leaders across the globe. He’s a fan of podcasts, bokeh and smooth jazz. His time is mostly spent learning the piano and watching his Golden Retriever Julian chase a stick.