Structured data is easy to decipher and analyze for extracting valuable information. And, a lot of research and investment have already been placed to make structured data the foundation ground for analytics. However, the rise of Big Data has thrown in new challenges for data analytics teams. It’s linked to the part of data analysis that deals with unstructured data in the form of unattributed texts, images, videos and ghost content in the form of wireless contact information, email IDs and non-government currencies.
The ‘goldmine’ information is hidden there.
Data scientists find these unstructured data as the unrequited agenda in Big Data. This is where Deep Learning and Data Analytics work with Natural Language Processing (NLP) to advance further data processing and data extraction from various human and machine-generated unstructured data.
What is Deep Learning and Role in NLP
Deep learning, also referred to as Deep Neural Network) is a subset of Artificial Intelligence (AI) that are built in hierarchical structures of increasing complexity. Analytics training in Bangalore works with highly-complex and convoluted deep learning algorithms that are necessary to extract accurate information from a pool of unstructured data.
NLP represents the machine-level analytics of data from human language, triggered by a wide range of voice-activated applications like phone, search engines and the new-age assistant devices like Alexa and Siri, to name a few. The coming of age for Data Analytics with the convergence of new technologies like AI, Machine Storytelling, Voice activation, Text and Image processing, and IoT connectivity have made Deep Learning the central component for working with Natural Language processing in commercial applications.
What may NLP with Data Analytics Training deliver?
One of the primary applications of Data analytics training in Bangalore is optimizing Big Data refresh cycles by leveraging Cloud computing and quantum computing based on statistical models created directly from Deep Learning’s own iterative and predictive models.
Use cases for NLP with data analytics are seen embedded into commercial and non-commercial applications, including in Natural Language translations, Social media intelligence and monitoring, Sales and Marketing Assistants and chatbots, Medical diagnosis or AI Doctors, stock market trading signals and image identification, and much more.
Applications of NLP powered by Deep Learning and Data Analytics
- Named Entity Recognition – NER, for analyzing customer reviews, testimonials, address verification, etc.
- Text Processing, for research papers, summarization of literary work, translation and identifying fake content
- Sentiment Analysis, for Marketing intelligence and the attribution, to understand the reaction of customers across various marketing, advertising, and social media channels.
- Reinforcement Learning, for intelligent machine-based assistants and robots to process analytics and learning models with predictive actions and intelligence.
- Image Processing, for medical science leveraged by AI doctors, surgeons and telemedicine platforms.
By using traditional data processing concepts, data analysts and scientists approach a never-ending trial and error process … This is where NLP helps to work with a large amount of unstructured data deliver on its results, making trial and error processes shorter and accurate with generalized output every time.