What is the relationship between Big Data and Machine Learning?

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.

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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. 

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The 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.

Categories of the Machine Learning algorithm

1. Supervised 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:

• Bias-variance relationship: due to this problem, the supervised learning algorithm cannot generalize beyond the training set provided, as it is impractical for a model that can capture regularities in the training data and at the same time generalize to previously unseen test data.

• Dimensionality of the input space: most Machine Learning algorithms degrade in performance and accuracy as the number of variables increases.

• Heterogeneity and redundancy: with increased heterogeneity and redundancy of data, algorithms do not show efficiency and accuracy.

• Presence of nonlinearity and interactions: poses a challenge for many of the Machine Learning algorithms as Kiang illustrated in his study: 'the performance of neural networks and logistic regression are pessimistically affected by nonlinearity'.

2. Unsupervised Learning

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:

• Similarity criteria: several similarity measures are available such as Euclidean, Manhattan or Chebyshev. This measure affects how the similarity between two cases in the data set is measured.

• Selection of initial centers: the selection of different initial centers for a given partitional clustering algorithm, such as k-means, may produce different results.

• It is often difficult to analyze when a hierarchical clustering algorithm should terminate.

3. Reinforcement Learning (RL)

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:

• Based on the complexity of a problem it can be very costly to keep the values of each state in memory. 

• Wrong assessments drastically hinder the performance of a learning task.

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