What is Natural Language Processing (NLP)?

What is Natural Language Processing (NLP)?

It basically strives to build machines that understand and respond to text or Voice data- and respond with text or speech of their own as humans do.

What is Natural Language Processing (NLP)?

NLP is a branch of computer science and more specifically is a branch of Artificial Intelligence (AI) which gives computers the ability to understand the text and the spoken words in the same way as humans can.

NLP combines computational linguistics — rule- grounded modelling of mortal language with statistical, machine literacy, and deep literacy models. Together, these technologies enable computers to reuse mortal language in the form of textbook or voice data and to ‘ understand ’ its full meaning, complete with the speaker or pen’s intent and sentiment.

NLP drives computer programs that restate textbooks from one language to another, respond to spoken commands, and epitomise large volumes of textbook fleetly — indeed in real time. There’s a good chance you ’ve interacted with NLP in the form of voice- operated GPS systems, digital sidekicks, speech- to- textbook dictation software, client service chatbots, and other consumer conveniences. But NLP also plays a growing part in enterprise results that help streamline business operations, increase hand productivity, and simplify charge-critical business processes.

NLP tasks

Mortal language is filled with inscrutability that makes it incredibly delicate to write software that directly determines the intended meaning of textbook or voice data. Homonyms, homophones, affront, expressions, conceits, alphabet and operation exceptions, variations in judgement structure — these just a many of the irregularities of mortal language that take humans times to learn, but that programmers must educate natural language- driven operations to fete and understand directly from the launch, if those operations are going to be useful.

Several NLP tasks break down mortal textbook and voice data in ways that help the computer make sense of what it’s ingesting. Some of these tasks include the following

Speech recognition, also called speech- to- textbook, is the task of reliably converting voice data into textbook data. Speech recognition is needed for any operation that follows voice commands or answers spoken questions. What makes speech recognition especially gruelling is the way people talk — snappily, warbling words together, with varying emphasis and accentuation, in different accentuations, and frequently using incorrect alphabets.

Part of speech trailing, also called grammatical trailing, is the process of determining the part of speech of a particular word or piece of textbook grounded on its use and environment. Part of speech identifies ‘ make ’ as a verb in ‘ I can make a paper aeroplane , ’ and as a noun in ‘ What make of auto do you enjoy? ’

Word sense disambiguation is the selection of the meaning of a word with multiple meanings through a process of semantic analysis that determines the word that makes the utmost sense in the given environment. For illustration, word sense disambiguation helps distinguish the meaning of the verb’ make’ in ‘ make the grade ’( achieve)vs. ‘ make a bet ’( place).

Named Reality recognition, or NEM, identifies words or expressions as useful realities. NEM identifies ‘ Kentucky ’ as a position or ‘ Fred ’ as a man’s name.

Coreference resolution is the task of relating if and when two words relate to the same reality. The most common illustration is determining the person or object to which a certain pronoun refers(e.g., ‘ she ’ = ‘ Mary ’), but it can also involve relating a conceit or an expression in the textbook(e.g., a case in which’ bear’ is not a beast but a large hairy person).

Sentiment analysis attempts to prize private rates — stations, feelings, affront, confusion, dubitation — from textbooks.

Natural language generation is occasionally described as the contrary of speech recognition or speech- to- textbook; it’s the task of putting structured information into mortal language.

NLP use cases

Natural language processing is the driving force behind machine intelligence in numerous ultramodern real- world operations. Then are a many exemplifications

Spam discovery You may not suppose of spam discovery as an NLP result, but the stylish spam discovery technologies use NLP’s textbook bracket capabilities to check up emails for language that frequently indicates spam or phishing. These pointers can include overuse of fiscal terms, characteristic bad alphabet, hanging language, unhappy urgency, misspelt company names, and more. Spam discovery is one of a sprinkle of NLP problems that experts consider’ substantially answered'( although you may argue that this does n’t match your dispatch experience).

Machine Restatement Google Translate is an illustration of extensively available NLP technology at work. Truly useful machine restatement involves further than replacing words in one language with words of another. Effective restatement has to capture directly the meaning and tone of the input language and restate it to a textbook with the same meaning and asked impact in the affair language. Machine restatement tools are making good progress in terms of delicacy. A great way to test any machine restatement tool is to restate the textbook to one language and also back to the original. An hourly- cited classic illustration Not long ago , rephrasing “ The spirit is willing but the meat is weak ” from English to Russian and back yielded “ The vodka is good but the meat is rotten. “At the moment, the result is “ The spirit solicitations, but the meat is weak, ” which is n’t perfect, but inspires much further confidence in the English- to- Russian restatement.

Virtual agents and chatbots Virtual agents similar to Apple’s Siri and Amazon’s Alexa use speech recognition to fete patterns in voice commands and natural language generation to respond with applicable action or helpful commentary. Chatbots perform the same magic in response to compartmented textbook entries. The stylish of these also learn to fete contextual suggestions about mortal requests and use them to give indeed better responses or options over time. The coming improvement for these operations is question answering, the capability to respond to our questions anticipated or not with applicable and helpful answers in their own words.

Social media sentiment analysis NLP has become an essential business tool for uncovering retired data perceptivity from social media channels. Sentiment analysis can dissect language used in social media posts, responses, reviews, and further to prize stations and feelings in response to products, elevations, and events – information companies can use in product designs, advertising juggernauts, and more.

Text summarization Text summarization uses NLP ways to digest huge volumes of digital textbooks and produce summaries and synopses for indicators, exploration databases, or busy compendiums who do not have time to read a full textbook. The stylish textbook summarization operations use semantic logic and natural language generation( NLG) to add useful environment and conclusions to summaries.

Natural language processing and IBM Watson

IBM has founded in the artificial intelligence space by introducing NLP- driven tools and services that enable associations to automate their complex business processes while gaining essential business perceptivity. These tools include

Watson Discovery- Surface high- quality answers and rich perceptivity from your complex enterprise documents tables, PDFs, big data and more with AI hunt. Enable your workers to make further informed opinions and save time with real- time hunt machines and textbook mining capabilities that perform textbook birth and dissect connections and patterns buried in unshaped data. Watson Discovery leverages custom NLP models and machine literacy styles to give drugs with AI that understands the unique language of their assiduity and business. Explore Watson Discovery

Watson Natural Language Understanding( NLU)- dissect textbook in unshaped data formats including HTML, webpages, social media, and more. Increase your understanding of mortal language by using this natural language tool to identify generalities, keywords, orders, semantics, and feelings, and to perform textbook bracket, reality birth, named reality recognition( NER), sentiment analysis, and summarization. Explore Watson Natural Language Understanding

Watson Assistant- Ameliorate the client experience while reducing costs. Watson Assistant is an AI chatbot with an easy- to- use visual builder so you can emplace virtual agents across any channel, in twinkles. Explore Watson Assistant

Purpose- erected for healthcare and life lores disciplines, IBM Watson Commentator for Clinical Data excerpts crucial clinical generalities from natural language textbook, like conditions, specifics, disinclination and procedures. Deep contextual perceptivity and values for crucial clinical attributes develop further meaningful data. Implicit data sources include clinical notes, discharge summaries, clinical trial protocols and literature data.

Understanding Python for Machine Learning – Part 1 (Data Types in Python)

Understanding Python For Machine Learning

Machine learning has become one of the most sought after fields in the current times. With advancements in both software and hardware, building complex mathematical and computational heavy models have become easier and quicker. Introduction of TPU (Tensor Processing Units) have further expedited the process of machine learning models. While all the advancements in the field of hardwares have enabled this change, one notable change has been in the use of python for machine learning algorithms. 

Marking a shift from traditionally used language R (which is still preferred by statisticians), python has gained popularity in recent times amongst many python developers and python consultants. Many new 3rd party machine learning tools now provide python support out of the box. Infact, many companies have now started calling themselves python development companies.

Hence, through this series of articles, we will understand python for machine learning and how python consultants can leverage python to build robust machine learning models. As a first article in this series, we will understand different data types in python and their syntax in python.

Let us begin!

There are majorly 7 types of data in python

TextString (str)
NumericInt, Float, Complex
SequenceList, Tuple, Range
SetSet, Frozenset
BooleanBool (True/False)
Binarybytes, bytearray, memoryview

We will now understand each of the above types in detail.

  1. Text

Strings in python are a stream of characters, contiguous in nature. They are often represented in between single quotes. Strings allow slicing on them, that is, we can use parts of a string to extract substring out of it. One important thing to note is that unlike R, indexes in python start from 0. 

Syntax and Output:

We can also use + operator to concatenate the strings.

  1. Numeric

This type of data includes use of integer (signed), float (real values of floating point) etc. These are number inputs. It is important to note that all integer types in Python 3 are long. Hence, there is no separate long type in Python 3.

Syntax and Output:

  1. Sequence

This is probably the most frequently used data type in Python. It finds several applications at different places. The basic data types like numeric and text are also used inside the sequence data type. That is, a sequence data type can contain data of type integer and float. To understand the concept better, let us understand what is a list, tuple and a range. 

  1. List

As mentioned above, since sequence data type can contain other data types as well, it is also called a compound data type. Lists are very versatile. List stores a sequence of values. It can be accessed using ‘[ ]’  and can be manipulated using operators like ‘+ and *’.

Syntax and Output:

  1. Tuple

Similar to lists, except the values in the tuple cannot be changed/updated. The size of the tuple can also not be changed once created. In other words, tuples can be thought of as “read-only lists”. One change also lies in the syntax of tuple and list. Tuples are formed using parentheses ( ‘( )’ )

Syntax and Output:

  1. Range

Range is often used in loops, to define a set of numbers over which the counter will iterate. It is used to create a sequence of numbers, with start(included) and end(not included) as its parameters. 

Syntax and Output:

  1. Mapping (Dictionaries)

Dictionaries are key-value pairs in python. Like hashtables, we have a key which corresponds to a particular value. When we want to access the value, we use the key corresponding to the value and the value is retrieved. 

The key is generally an integer or a string, though they can be any of the python data types. The value can be any python object.

Syntax and Output:

With this, we come to the end of this article. In this article, we learnt how python developers and python consultants can use different data types in different scenarios. A python development company, which is planning to expand into machine learning and AI, should be able to understand and deliver on this basic premise of data types in python. This base can then be used to build great machine learning models.

As promised, as a multi-series tutorial, we will cover “Linear Regression in Python” in the next post.

Until then, bye bye!

Object Recognition Software

Object Recognition Software

Do you know the difference between cat and cactus ? There are chances that you may or your team may , but this is not the most efficient way you utilise your time . That is where object recognition technology  are acting as a time saver for many business organisation . It can also facilitate add on functionality for your customer , specially when you are dealing in app which is concern about image categorisation and content management .\

What Is Object Recognition ? 

In simple words , we can define object recognition as technology which has capability to recognise objects in picture or videos with the help of AI software . If you have ever used Google Lens or have used Google Photos in your mobile phone , then tan tana… !! You are already familiar with object recognition .
Right from identification of cover of book to identification of plans in wild , object recognition technology has come a long way . 
As it is new technology , many business owners really did not know how this technology exactly works and hence had made an assumption that the access to this technology is only possible by heavily investing on this technology . However this is not the case with object recognition . In fact it has tools which can easily be incorporated by even small and mid sized business in their software .
These tools facilities identification of single or multiple objects in photoset or image . Although Facial recognition is also one type of object recognition technology , but is always treated separately due to its complexity in nature and legal concerns .

How Does Object Recognition Work ? 

Before proceeding with how object recognition works , let’s quickly look at the difference between object detection and object recognition . Object detection is concern about locating object in image , wherein object recognition is being used to identify object in given image .

Object recognition is being used to find out more specific categories of objects , such as dog or cat . In some cases , object recognition can even be performed without using AI technology . In those cases template matching or image segmentation is being used to recognise objects .

Even more advance object recognition can be done by following one of the two approaches : machine learning and deep learning , both of the technology have their own pros and cons .

Also Read : What Is Machine Learning & Its Applications ? 

Machine Learning 

We can define machine learning is training of software to perform certain activity , as in this case recognition objects in photo . You have to provide software set of images and then list feature which can later be used in recognition of object .
Major advantage of machine learning is it necessarily does not require large datasets or computing power , and hence cost of software development is always low .
Disadvantage of this technology is it require more efforts of your team to set up completely . If you have inserted limited photos to include in your trading sets , then your software might not able to identify with accuracy as it has not access to broader set of images .

Deep Learning 

Deep learning has ability to adapt Manny different functions or activity , but at the same time it requires large data sets , substantial hardware as well as more computation power .  In this approach software has ability to train itself using artificial neural network .
In this images are simply to be labeled as ” Cat ” or ” No Cat ” , and then there will be no extra efforts to be put to train software to distinguish cat .
The downside of this approach is it requires massive datasets which may even contain millions of images which might not be easily accessible for smaller companies to access those information .

Why There Is A Need For Object Recognition Software ? 

Object recognition can actively be used in various industry to cater their needs for identification of objects and theory reducing human interaction and time . Human may or may not sort and analyse images at the speed that a well trained algorithm can .
Software also has capability of identifying objects that can’t be even be seen by humans . Object recognition software are also able to detect patterns . As a human being we can get tired of viewing same pattern again and again , while on the other hand software won’t . They have the ability to view more images in a year than human in their entire lifetime . 

Where To Find Vendor Solution ? 

As in case of any other software development , in object recognition also you necessarily need not to start finding solutions right from scratch . If you are not expertise in handling this technology or don’t have access to datasets , you can use TenserFlow Object Detection API from Google .
Apple also offers machine learning API which is called Core ML , which can actively be used to run object recognition model on iOS device or in cloud as well .

Hire Software Development Company For Your Object Recognition Software 

If your team does not have any previous experience with artificial intelligence , then it will be quite hard to crack machine learning  . That is why it is important to hire right software development team who have deep experience in handing variety of object recognition tools . Whether you are looking to create solution right from scratch or want pre-trained mobile app to fit for your purpose , Winklix can help you to find perfect solution for your need .