Melissa Larsen

Jul 06, 2022

Importance of ML for Data Scientists

Machine Learning is the core subarea of artificial intelligence. It makes computers get into a self-learning mode without explicit programming. When fed new data, these computers learn, grow, change, and develop by themselves.

The concept of machine learning has been around for a while now. However, the ability to automatically and quickly apply mathematical calculations to big data is now gaining a bit of momentum.

So, How Drastically is Machine Learning Revolutionizing Data Analysis Avenue?

Data Analysis has traditionally been characterized by the trial and error approach – one that becomes impossible to use when there are significant and heterogeneous data sets in question. It is for this very reason that big data was criticized for being overhyped. The availability of more data is directly proportional to the difficulty of bringing in new predictive models that work accurately. Traditional statistical solutions are more focused on static analysis that is limited to the analysis of samples that are frozen in time. Enough, this could result in unreliable and inaccurate conclusions.

Coming as a solution to all this chaos is Machine Learning proposing smart alternatives to analyzing vast volumes of data. It is a leap forward from computer science, statistics, and other emerging applications in the industry. Machine learning can produce accurate results and analysis by developing efficient and fast algorithms and data-driven models for real-time processing of this data.