Machine Intelligence
Topological Mathematics and Machine Learning Simplify & Expedite Data Analysis
Technology
Find Hidden Critical Insights, Faster & Easier than Before
Why Machine Intelligence?
Organizations across all sectors are heavily reliant on data analytics to guide critical business decisions. Ranging from improving manufacturing processes, analyzing the result of clinical trials, to predicting customer behavior, enterprises have an expanding requirement of specialized analyses. However, the data at disposal is not only voluminous, but also highly complex, which outpaces the traditional statistical methods and techniques. Enterprises need to adopt up-to-date tools for harnessing the energy of this wave of data.
The prevailing scenario has led analysts and data scientists to look for new and innovative approaches to glean insights from the variegated data troves. The need of the hour is to implement tools & capabilities that would analyze data effectively and at a faster magnitude. Machine Intelligence is one such technique which extracts insights at a greater depth and scale, thereby empowering organizations to strengthen their core business and explore new avenues for growth.
Machine Intelligence
How TDA Enhances the Performance of Machine Learning Algorithms?
TDA aims to bridge the gap of lack of availability of machine learning skill set. It presents the data in the form of a shape, so that the relevant machine learning algorithms can discover the sub-groups and anomalies in the data set, without any human intervention.
Clustering
Clustering maps an input data point to a cluster.
Dimensionality Reduction
Dimensionality Reduction maps an input data point to a lower dimensional data point
Regression & Classification
Supervised Learning algorithms map an input data point to a predicted value.
TDA uses the power of machine learning techniques, algebraic topology, and data visualization, for identifying critical patterns and trends in data, thereby substituting the human expertise required for employing & handling various algorithms. It primarily aids in breaking down highly complex data having numerous dimensions & variables, into smaller data sets with lesser complexity, lower dimensional data point, and fewer variables. These, in turn, can be translated into valuable business insights, for exploring new opportunities, increasing productivity, combating bottlenecks, and enhancing the bottom line.
The Essence of Machine Intelligence Lies in the Seamless Integration of Topological Data Analysis & Machine Learning
While Topological Data Analysis gives a shape to the data, Machine Learning algorithms explain the pattern behind the shape. The combination of both these elements gives rise to machine intelligence, a powerful platform, which simplifies and accelerates analysis of complex, multi-dimensional, and multi-variate data troves.
Topology and Topological Data Analysis
Topology is a branch of mathematics that studies the various properties of shape. Topological Data Analysis implies the application of this study on complex and multi-variate data, for uncovering the hidden relationships, associations, and patterns, which are otherwise not apparent by traditional tools & methods. The technique is based on the ideology that every data has a shape, and each aspect of this shape, be it, edges, corners, cones, or voids, renders a specific meaning and significance. It studies these geometrical features of data to discover the persistent attributes, which can be interpreted in terms of high-value insights, for addressing business challenges & opportunities. Ranging from understanding customer behavior, identification of fraud, discovery of drugs, to anticipating health risks, TDA has a wide range of applications in the real world.
Machine Learning
Machine Learning enables computers to learn, adapt, and discover insights on complex data sets, without being explicitly programmed to carry out the intended tasks. It consists of a class of algorithms which learn from previous computations, apply mathematical calculations, and generate reliable insights, in a fast and repeatable fashion.
There are primarily two types of machine learning methods, supervised & unsupervised. While the first technique is used for predicting the likely future events based on historical data, the latter is applied for finding a structure or identifying segments on data having no labeled examples.
Though machine learning is not new to data analysis, yet it is gaining a fresh impetus in the corporate world, owing to its capability to produce accurate outcomes repeatedly, and at unprecedented speeds. The growing complexities and volumes of data have invoked companies to focus their energies on exploiting the potential of machine learning. Today, it is being utilized for a variety of activities, including pattern & image recognition, data mining, fraud detection, prediction of potential failures in equipment, credit scoring, and many more.
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