Tools to determine algorithm selection

It is definitely not easy to select the algorithm that is best suited for your data and your challenge. Luckily, the market is beginning to recognize that in order to move forward, tools need to exist to help with algorithm selection. How do you choose the right model? It is a difficult problem. While overfitting may be one problem, a more serious problem is that models lose accuracy over time. Therefore, you have to continuously retrain the model as the data changes. Selecting the right algorithm can be best accomplished by automating the selection of an algorithm. Take the example of a classification algorithm. There are as many as 40 different classifier algorithms. These different algorithms can be combined depending on the approach the data scientist is using. Therefore, you can have hundreds of combinations to choose from. If your data scientists need to test for potentially valid algorithms, it could take a long time to pick the best ones. Using an automation tool enables your scientists to more quickly determine the best combination of algorithms that will provide the highest score and the best fit for your data. Automation tools are important not just because of the complexity of the algorithms but also because you have to make sure that the algorithms you select to build your models will not impact data latency and data consistency.

Approaching tool selection

A variety of open-source tools are intended to help data scientists select the right algorithm. These tools are often tied directly to the language (Python, R, Java, and so on) being used. Why should data scientists use tools for algorithm selection? Many different machine learning models may all be useful in solving problems. If a data scientist can experiment with different algorithms, he will be able to improve the ability of models to predict outcomes and create models that will scale.

Getting Educated

Because machine learning is an emerging market, there is a great demand for skilled personnel to help support organizations’ efforts. It is becoming clear that companies can’t wait to find all the skilled professionals they need. This means there is a great opportunity for IT professionals to up their game and become experts in data science and machine learning techniques. Luckily, there are a lot of resources out there that can help you learn. In this section, we provide a list of resources that are available to give you a great start.

Medium: Inside Machine Learning

This site gives you deep-dive articles on a wide range of machine learning topics. From weather predictions to robots, you can explore the top machine learning case studies and get insights from industry experts. Visit medium.com/inside-machinelearning for more information.

CognitiveClass.ai

Visit https://cognitiveclass.ai to build data science and cognitive computing skills for free today. Classes are based on an IBM community initiative. Courses include “Machine Learning with Apache SystemML.”

Coursera online learning

Coursera is an online learning platform that offers courses and degrees in a variety of areas, including machine learning. It works with universities to offer more than 2,000 courses. Sign up today at www.coursera.org/learn/machine-learning.

Udacity courses on machine learning

Udacity is a for-profit educational organization that offers Massive Open Online Courses online (MOOCs). You can find it at www. udacity.com/course/intro-to-machine-learning–ud120.

Galvanize

Immersive data science curriculum includes a dive into machine learning and working on real problems in classification, regression, and clustering by utilizing structured and unstructured data sets. Students discover libraries like scikit-learn, NumPy, and SciPy, and use real-world case studies to root understanding of these libraries to real world applications. Learn more at www.galvanize.com/san-francisco/data-science.

edX courses

edX is an MOOC provider. It hosts online university-level courses. Some of the courses are even offered at no charge. Visit www.edx. org/course/machine-learning-data-science-analyticscolumbiax-ds102x-1 to find out more about the online “Machine Learning for Data Science and Analytics” course.

MITOpenCourseware

MIT has set up a site that includes all of its courses. It is offered at no cost to participants. You can learn more about machine learning at http://bit.ly/1tP7pPU.

Google Research Blog

Google researchers publish a variety of papers on topics related to machine learning and deep learning. You can learn more about deep learning here: research.googleblog.com/2016/01/teachyourself-deep-learning-with.html.

Kaggle Wiki

The Kaggle Public Wiki is a resource for learning statistics, machine learning, and other data science concepts. It offers tutorials as well as a platform for data science competitions. Visit www.kaggle.com/wiki/Home today.

KDnuggets

KDnuggets is a popular site that provides a vast amount of information on analytics and a variety of information on data science. Check out the content at www.kdnuggets.com/about/index.html.

Data Science Central

Data Science Central is an online site for big data practitioners. It includes a community platform with technical forums for information exchange and technical support. Head to www.datasciencecentral.com for more information.