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The world is experiencing exponential growth in data generation on an ever-increasing scale. According to IDC (International Data Corporation)By 2025, the world will produce 180 zettabytes of information (or 180 trillion gigabytes), compared to less than 10 trillion gigabytes in 2025. zettabytes in 2015.
As defined by Gartner, 'Big data encompasses massive volume, high velocity and wide variety that requires a specialized environment to process it, allowing for better decision making and more efficient and optimized processing'.
But, the study of Big Data has become a demanding problem. The full exploitation of the potential endowed by Big Data depends on the improvement of long-established approaches.
According to Jagdish 'analytics lays the foundation for the Big Data revolution'. Data analytics involves methodologies, algorithms, approaches, tools and technologies for business intelligence, predictive analytics, visualization and statistical inference. In this article, we explore the potential of Big data from a machine learning perspective. According to McKinsey Global InstituteThe Big Data revolution is driven and advanced by Machine Learning..
Since the last decade, companies are increasingly adapting to a data-driven approach to improve the services they offer and their business performance.
► You may be interested in: Big Data applied to business
Machine Learning focuses on sorting information and learning patterns and behaviors through historical data to make future predictions. The performance of Machine Learning methods goes hand in hand with how well the available data represents the problem to be solved, which usually involves handling a huge amount of data.
Despite the rapid advances in the field of Machine Learning, the developed algorithms have difficulties in terms of efficiency to handle a considerable amount of data. In turn, in real problems it is common for data to be full of inconsistencies, incomplete or misrecorded information, or other errors that present a major challenge in information processing.
► You may be interested in: The 5 Challenges of Big Data in Machine Learning
Machine Learning is a highly interdisciplinary field of computer science that focuses primarily on building models based on learning algorithms that impact almost all scientific disciplines, from bioinformatics to information retrieval to statistics. Machine Learning algorithms can be divided into three categories: supervised, unsupervised and reinforcement learning.
The supervised learning makes decisions based on logic provided by an algorithm that takes 'labeled' input data. Supervised learning performs classification and regression data processing tasks using algorithms like SVM (Support Vector Machine), Naive Bayes or computational and statistical classifiers.
Often, these supervised learning algorithms face the following challenges that can affect the efficiency of learning tasks:
The unsupervised learning algorithms discover patterns and behaviors in the data to segment the information and learn more about it. Algorithms such as k-means, or self-organizing maps (SOM) are part of unsupervised learning. These algorithms face the following challenges:
Reinforcement learning (RL) is inspired by behavioral psychology on the idea of providing a reward or punishment for actions performed by software agents in a given environment. The challenges often faced by algorithms The reinforcement learning methods are:
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