Top 5 Challenges in Machine Learning

This video represent a list of top 5 challenges that are common to many in the machine learning and data science community.

  • Scarcity of Data

The more data that can be used for modeling and predictions, the better.

This isn’t a problem for big companies, such as Facebook and Google. However, for many others, lack of sufficient data can limit their results rendering machine learning less productive.

  • Unclear Question

Vague questions will not result in substantive results. Data science is about recognizing patterns. So, clear questions are fundamental to defining what types of patterns to analyze.

  • Unclear Representation of Data

For a data scientist, the resulting work needs to be represented to the end user in meaningful ways. There is more room for libraries to make life easier to better represent data.

  • Expensive Resources

Computing millions of lines of code over and over can be expensive. But it should get less expensive in the future.

  • Machine learning Algorithm selection

Currently, the biggest challenge in data science is the selection of the right algorithms. It’s important to understand the algorithms as well as possible to determine which ones will most benefit your project.