Natural Language Processing In Data Institutes In Bangalore deals with artificial intelligence that deals with making computers understand the natural language of humans is called Natural Language Processing. Today, this concept is very common and popular in the field of data science, because it aims at making computers understand all the natural language data to process them better than before. The Natural Language Processing In Data Institutes In Bangalore could be in any format – speech, text, images and more. The computer will now understand a simple spoken word with the same ease as the human brain, thanks to NLP.
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Digital assistance, voice-activated remote controls, voice-controlled GPS systems, chatbot customer support, etc. are some examples of NLP software that use on a daily basis. Thanks to their quick and accurate performance, NLP is used extensively across all businesses today to improve overall employee productivity, increase overall profitability margins, bring in automation wherever necessary and smoothen the process of making critical, data-driven decisions.
Most popular NLP operations
Though Natural Language Processing is used in many projects for many purposes, the functions of this software can broadly be put under the following operations:
- Speech Recognition – Voice data is translated to text format here; people speak at different speeds, accents, styles and pronunciation patterns. So, speech recognition, though very commonly used today, is one of the most challenging operations of NLP software.
- Grammatical Tagging – This operation is where the software identifies the part of speech of a particular word based on its meaning & context of usage. For example, one word can both be a verb and a noun based on the way it is used in a sentence.
- Disambiguation – Semantic analysis is performed to select the correct meaning of the word according to the context of usage.
- Named Entity Recognition – This is where a proper noun or any words or phrases are recognized as names, places, animals, or things.
- Co-reference resolution – This involves establishing relationships between the pronoun and its noun in a sentence, or identifying the meaning of words that imply a different meaning than the direct meaning (like idioms or proverbs).
- Sentiment Analysis – This tool draws emotional insights from data that are in a text format. It involves identifying emotions like joy, anger, suspicion, sarcasm, etc.
- Natural Language Generation – In this function, the NLP software is used in reverse mode, where it can generate natural languages from a given piece of structured data.
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Are you interested to know more about this interesting data science tool? Check these NLP training courses right away, and choose the one that suits your requirements the best. Remember, NLP is going to be the next “in thing” in data science. Therefore, getting a certification course in this field will definitely add a lot of value to your career, as many companies are looking for experts to make sense of voice instructions, reviews, and other forms of data lying before them.
NLP Courses in Bangalore
Knowing how to use NLP and its latest techniques in Python is one of the best skills you can have as a data scientist today. With Artificial Intelligence and Machine Learning already ruling the roost in this field, it is only natural that you master the art of Natural Language Processing techniques and makes the systems almost mimic the human brain. The following are some of our recommendations if you plan to enroll in an NLP course in Bangalore.
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Data Science Course in India – 360digiTMG
- A 4-month course which teaches you all the fundamental concepts of data science like Python, SQL, Data Science Preliminaries, CRISP Machine Learning, Exploratory Data Analytics, Feature Engineering, Power BI, Data Mining, Data Clustering, Math Fundamentals, Text Mining, NLP and more
- Topics covered in Text Mining & NLP module include data sources, word clouds, corpus-level word clouds, positive & negative word clouds, sentiment analysis, concepts of unigram, bigram & trigram, Extraction and understanding of user reviews from sites like Amazon, Sentiment Extraction, Topic Modeling, Latent Dirichlet Allocation, Lexicons & Emotion Mining and more
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- All core concepts are covered in 184 hours of online & offline sessions, 150 hours of practical assignments and over 120 hours of hands-on experience on live and Capstone projects
- Course Duration – 4 months
- Course Fee – Rs.55000
All of these NLP techniques are also covered in the 8-month professional data science course offered by 360digiTMG. You can compare the course content, fee and duration of these courses before deciding on the one that fits your requirements the best.
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NLP Certification Course – Intellipaat
- Topics Covered – NLP and Text Mining Overview, Text mining, cleaning and pre-processing of data, Text classification, Sentence Structure, Sequence Tagging, Sequence Tasks & Language Modeling, Semantics & Vector Space Models Introduction and Dialog Systems
- Hands-on experience on projects after completion of each module on topics like reading & writing different formats of files to/from the local drive, using Python’s expressions to understand word tokenization, named entity recognition, working with count vectorizer, inverse document frequency and more
- You will master the use of Python’s NLTK (Natural Language Toolkit) upon completion of this course
- Course Fee – around Rs.17000 for the self-paced program; around Rs.30000 for a 20-hour instructor-led session
Natural Language Processing Training – Simplilearn
- Course Fee – Rs22470 (for a flexible 90-day program)
- Gives you a thorough understanding of the basic terminologies used in the field of NLP, some of which are:
- Tokenization – the process of splitting words, phrases, idioms and the like
- Disambiguation – explains the real meaning and the sense of words
- Tf – idf – Stands for Term Frequency and Inverse Document Frequency
- Semantic Analytics – makes a comparison of words, phrases and more in a sentence to express their meaning clearer than before
- Topics covered – NLP Approach to Text data, introduction to the major NLP libraries like NLTK, Sckikit Learn and TextBlob, Modules to load the content and category, using various tools like a bag of words, count vectorizer class signature, model training, Naïve Bayes Classifier, Grid Search and Multiple Search Parameters and more.
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