Machine Learning.

Find meaningful insights

Machine Learning

Machine learning is the automated process of analyzing data to recognize patterns using algorithms that can learn from their iterations.
The objective of machine learning is to find meaningful insights that are not evident or easy to find.




Importance of machine learning

With machine learning it is possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Business Applications


Fraud Detection

Through the use of sophisticated data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.

  • Financial Institutions
  • Health care
  • Network Security
  • Online Payments
  • Identity Verification


Internet of Things (IoT)

A proposed development of the Internet in which everyday objects have network connectivity, allowing them to send and receive data.

  • Predictive User Preferences
  • Location of Things


Big Data

Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

  • Marketing
  • Retail Stores

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Data Mining

Data mining can be considered a superset of many different methods to extract insights from data. It might involve traditional statistical methods and machine learning. Data mining applies methods from many different areas to identify previously unknown patterns from data. This can include statistical algorithms, machine learning, text analytics, time series analysis and other areas of analytics. Data mining also includes the study and practice of data storage and data manipulation.

Machine Learning

Machine learning has developed based on the ability to use computers to probe the data for structure, even if we do not have a theory of what that structure looks like. The test for a machine learning model is a validation error on new data, not a theoretical test that proves a null hypothesis. Because machine learning often uses an iterative approach to learn from data, the learning can be easily automated. Passes are run through the data until a robust pattern is found.

Deep learning

Deep learning combines advances in computing power and special types of neural networks to learn complicated patterns in large amounts of data. Deep learning techniques are currently state of the art for identifying objects in images and words in sounds. Researchers are now looking to apply these successes in pattern recognition to more complex tasks such as automatic language translation, medical diagnoses and numerous other important social and business problems.
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