Why You Should Get Serious About NLP with Data Science?

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Why You Should Get Serious About NLP with Data Science?
Natural Language Processing (NLP) software development is one of the fastest-growing segments in the Data Science market. Today, NLP software is used in multiple industries and has found its way into advanced AI and Machine Learning applications like Computer Vision and Personalized Voice Assistant platforms too.
So, let’s get started.

Who are the top NLP software makers?

Global Natural Language Processing market is heavily favoring the data science professionals who have worked in at least one open-source project in the last 18 months. The competitive landscape includes big enterprise companies as well as open-source projects and AI startups.

Some of the leading NLP makers are:

- IBM Watson
- Microsoft
- Google
 - Amazon Alexa
- Facebook
- Intel
- Stanford Core
- Monkey Learn
- Apache Open NLP
- GenSim
- Text Blob

What kind of foundation do I need to excel with NLP Data Science Online Course?

NLP is the foundation of many AI and Voice-based applications and technologies currently doing the rounds in the market. The leading adoption centers or industries are:

- Marketing and Sales Automation
- Voice Chat Assistants and Call Analytics
- Customer Experience Management
- Audio Description for Deaf
- Patient Monitoring and Self-assessment mobile apps
- Mobile gaming and tracking apps
- Mobile Payments
- In car music experience and control

The best thing about learning NLP tools and techniques is its ease of deployment and scaling into other computer-controlled tools such as IoT, Smart device management, Security and Risk assessment dashboards, and Content discovery. For example, Google Assistant, YouTube voice, and Alexa can all determine the subject of your query and analyze the input signal to find the most relevant content. The final outcome is based on ML based search automation, content discovery, web crawling / data crawling, and sentiment analysis.

Advanced NLP can even predict your future search options and recommends your “next search” options beforehand.

The answer is short and simple- Accessibility to the latest tools, techniques, and libraries to build top class NLP projects. Open source NLP projects, in particular, are the best resources for professionals looking for practicality and accessibility to the state of the art NLP libraries, in addition to working with numerous other tools, including for:

- Named Entity
- Speech to Text Recognition
- Tokenization
- Voice Encryption
- Sentiment analysis and so on.

To get a job call from a hiring agency, you should know really about NLP data science projects and what they have more to do with the current adoption trends. Best NLP blogs are available here:

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