09 Jul The Importance Of Machine Learning For Data Scientists
Let’s start by defining what machine learning is, machine learning is simply the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
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.
Machine learning has been used in a number of places like the self-driving Google car, the online recommendation engines, friend recommendations on Facebook, offer recommendations from Amazon, and in cyber fraud detection.
Why Machine Learning?
The machine learning field is constantly evolving and along with evolution comes a rise in demand and importance. There is one crucial reason why data scientists need machine learning, and that is: ‘High-value predictions that can guide better decisions and smart actions in real time without human intervention’.
Machine learning as technology helps analyze large chunks of data, easing the tasks of data scientists in an automated process and is gaining a lot of prominence and recognition. Machine learning has changed the way data extraction and interpretation works by involving automatic sets of generic methods that have replaced traditional statistical techniques.
So, how drastically is machine learning revolutionizing the data analysis avenue?
Data analysis has traditionally been characterized by the trial and error approach, one that becomes impossible to use when there are large and heterogeneous data sets in question. It is for this very reason that big data was criticized for being overhyped. 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. Obviously enough, this could result in unreliable and inaccurate conclusions.
Coming as a solution to all this chaos is Machine Learning proposing clever alternatives to analyzing huge volumes of data. It a leap forward from computer science, the statistics, and other emerging applications in the industry. Machine learning is able to produce accurate results and analysis by developing efficient and fast algorithms and data-driven models for real-time processing of this data.