While we talk about data is the next new oil, most of the data are in unstructured formats distributed in various forms over multiple mediums, these data sets are highly rich on information but the big challenge is to mine and analyze such unstructured data.

With stellar growth in cloud computing deep learning techniques, it is now possible to derive meaning out of these complex unstructured data using Machine learning tools and derive signals that can make a significant impact in business decisions and can also help in predicting future outcomes for both business & consumers.

We help our clients in filtering noise from such “unstructured data” of any forms such as ;

•    Text Data : Recognize handwritten contents, scanned data, message data, text files, emails, numerical or textual log  data, web pages, any other business documents including social media data distributed in any form including extracting  information from emoticons

•    Image Data : Identify moods and sentiment of individual or group of an individual by analyzing photographs and  images, gif files etc.

•    Video Data : Analyze real-time or static video data by utilizing advanced algorithms thereby turning video files into actionable intelligence and insights

•    Audio Data : Analyze audio files using Artificial Neural Networks and by classifying audio files such as mobile call data, call centre call records to decipher insights and improve on QA & QC

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Text analytics, also known as text mining or natural language processing, is a field of study that focuses on extracting insights and meaning from unstructured text data. It involves the use of machine learning algorithms, statistical analysis, and natural language processing techniques to identify patterns, trends, and relationships within large volumes of text data.

One common application of text analytics is sentiment analysis, which involves determining the sentiment (positive, negative, or neutral) of a piece of text. This can be useful in a variety of contexts, such as analyzing customer reviews to gauge satisfaction with a product or service, or analyzing social media posts to understand public sentiment about a particular topic or event. 

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Image processing and analytics is a fascinating field that involves the use of computer algorithms and techniques to extract meaningful information from images and video data. It is a key component of many modern applications, including facial recognition, object detection, medical imaging, and more. 

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Artificial intelligence (AI) and machine learning are related fields that involve the development and use of computer systems that can learn and adapt to new data.

AI refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. There are many different types of AI, including narrow or weak AI, which is designed to perform specific tasks, and general or strong AI, which has the ability to perform any intellectual task that a human can.

Machine learning, on the other hand, is a subset of AI that involves the use of algorithms and statistical models to enable a system to learn and improve from experience without being explicitly programmed. Machine learning algorithms can be trained on a dataset and then used to make predictions or decisions based on new data.

There are many applications for AI and machine learning in a wide range of industries, including healthcare, finance, marketing, and cybersecurity. Some common examples include recommendation systems, fraud detection, and image and speech recognition. 

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Big data analytics is the process of collecting, storing, and analyzing large and complex datasets to uncover insights and trends from varied, high-volume, high-velocity datasets. These datasets are characterized by the 3Vs: volume, variety, and velocity. It is often too large in volume to be stored and processed on a single computer or server and may come in a variety of different formats and types such as from structured (database tables, Excel sheets) to semi-structured (XML files, webpages) to unstructured (images, audio files). It is also generated at a high velocity, meaning that it is collected and generated at a rapid rate, making it difficult to process in real time using traditional systems. 

Big data analytics involves using advanced technologies and algorithms to extract valuable information from large volumes of data, and to use that information to make better-informed business decisions. One of the key tools used in big data analytics is Hadoop, a framework that allows for the distributed processing of large datasets across clusters of computers. Image Source

Welcome to the forefront of data science with our latest addition to the array of cutting-edge services – Large Language Modeling (LLM). In an era defined by the ever-expanding landscape of information, LLM stands at the forefront, promising a revolutionary leap in the way we interact with and extract insights from vast troves of textual data. Beyond the traditional realms of data analytics, LLM delves into the intricate nuances of language, enabling our clients to unlock the full potential of their textual data. Large Language Models, such as GPT-3.5, have emerged as game-changers, offering unparalleled capabilities in understanding and generating human-like text.

Large Language Modeling is not just about processing words; it's about understanding context, sentiment, and intent at an unprecedented scale. Imagine the power to unravel intricate patterns in massive datasets, comprehend the intricacies of user-generated content, and make data-driven decisions with linguistic precision. Whether you're deciphering customer sentiments, automating document summarization, or creating intelligent chatbots, our LLM services empower you to navigate the expansive world of language data effortlessly. As we usher in this new era of linguistic intelligence, our commitment to innovation ensures that your organization stays ahead, harnessing the true power of language to drive data-driven success.

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An analytics dashboard is a tool that displays important information and metrics in a visually appealing and easy-to-understand format. Dashboards are commonly used to monitor and track the performance of a business, website, or specific project. They can be customized to show data that is relevant to the user and are often interactive, allowing the user to drill down into the data for more detailed analysis.

Overall, creating an analytics dashboard requires a combination of data analysis and design skills. It is a valuable tool for tracking and monitoring the key performance indicators (KPIs) and other important data points that help a business or organization track and monitor its performance. 

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Frontend development refers to the process of building the user interface of a website or application. It involves creating the visual design and layout of the website or application, as well as implementing the functionality and interactivity of the user interface using HTML, CSS, and JavaScript.

Frontend developers work closely with designers to create the look and feel of a website or application, and with backend developers to ensure that the user interface is properly connected to the underlying server-side logic and data.

Frontend development has evolved significantly in recent years with the introduction of new technologies and frameworks. Today, frontend developers often use responsive design techniques to ensure that websites and applications are optimized for different devices and screen sizes, and may use tools such as React, Angular, or Vue to build interactive user interfaces.

Overall, frontend development is a crucial aspect of the web development process, and is responsible for creating the user-facing elements of a website or application that users interact with on a daily basis. 

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End-to-end analytics application development involves the process of creating an application that allows users to gather, analyze, and visualize data in order to make informed decisions. This process typically involves several steps, including gathering requirements, designing the application, implementing and testing the application, and deploying and maintaining it.

Analytics applications can be used in a variety of settings, including businesses, government organizations, and non-profits, to help make data-driven decisions and improve efficiency and effectiveness. Developing an analytics application requires a combination of technical skills, such as coding and data analysis, and design skills, such as user experience design.  

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Mobile data science refers to the field of performing data science tasks, such as data analysis, machine learning model inference, and other heavy computational operations, directly on mobile devices like smartphones or tablets. This approach enables the execution of complex data science algorithms and models locally on the device, without the need for constant internet connectivity or reliance on remote servers.

In the context of performing tasks like object detection, image classification, or natural language processing on mobile devices, mobile data science involves deploying optimized machine learning models that are specifically designed to run efficiently on resource-constrained mobile hardware. This may involve techniques such as model compression, quantization, and architecture optimization to reduce the computational and memory requirements of the models while maintaining acceptable performance.

Our services include leveraging mobile data science techniques to perform complex data analysis and machine learning tasks directly on your users' devices. By optimizing machine learning models for mobile hardware, we enable real-time processing, enhanced privacy, and reduced reliance on external servers. From object detection to natural language processing, we bring the power of data science to the palm of your users' hands.

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Welcome to the frontier of artificial intelligence with our latest cutting-edge services – Reinforcement Learning. In an age defined by the relentless march of technology, RL stands poised to revolutionize the way we approach complex decision-making processes and autonomous systems. Beyond traditional machine learning paradigms, RL delves into the realm of dynamic decision-making, offering unparalleled capabilities in learning and adapting to diverse environments. 

RL is not merely about processing data; it's about learning from experience, optimizing actions, and maximizing long-term rewards. Picture the ability to train agents to navigate complex environments, master complex tasks, and even surpass human performance in specific domains. Whether you're optimizing resource allocation in supply chain management, developing Agents for financial Trading, or enhancing recommendation systems, our RL services empower you to harness the full potential of autonomous decision-making. 

With RL, you can unlock new possibilities in automation, optimization, and adaptive control. Imagine the efficiency gains from autonomous systems that continually learn and adapt to changing circumstances, leading to improved performance and cost savings. As we embark on this journey into the future of artificial intelligence, our commitment to innovation ensures that your organization remains at the forefront, leveraging the transformative power of RL to drive unprecedented success in an increasingly complex world. 

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