A Beginner introduction to Natural Language Processing

Today, with of Digitization everything, 80% the data being created is unstructured.

Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document’s texts processed in financial sectors are examples of unstructured data stored in Big Data.

Organizations are turning to Natural language processing (NLP) technology to derive understanding from the myriad of these unstructured data available online and in call-logs.

Natural language processing (NLP) is the ability of computers to understand human speech as it is spoken. Natural language processing is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Machine

Why Take This Course?

Over the Topics of this course, you’ll become an expert in the main components of Natural Language Processing(NLP), including speech recognition, sentiment analysis, and machine translation. You’ll learn to code probabilistic and deep learning models, train them on real data, and build a career-ready portfolio as an NLP expert!

Learn cutting-edge Natural Language Processing(NLP) techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, machine translation, and more!

By this Natural Language Processing(NLP) course you can work on the Most Cutting-Edge Applications of present days.You can analyze Text using Natural Language Processing(NLP) techniques & Text Mining

As Natural Language Processing(NLP) provides a tool for humans to communicate with computers effectively,NLP is at the center of the AI revolution.In Current days the industry is hungry for highly-skilled data specialists, and through this Natural Language Processing(NLP) course you’ll begin making an impact right away.

By taking this course master in Natural Language Processing(NLP) techniques with the goal of applying those techniques immediately to real-world challenges and opportunities. This is efficient learning for the innovative and career-minded professional AI engineer and getting a good grip on natural language processing(NLP).

You’ll learn how to build and code natural language processing(NLP) and speech recognition models in Python.

The most effective way to learn natural language processing(NLP) is by having your code and solutions analyzed by AI experts who will give you powerful feedback in order to improve your understanding.

What You Will Learn from this Natural Language Processing(NLP) course

Start mastering Natural Language Processing(NLP)!

Learn cutting-edge natural language processing(NLP) techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, machine translation, and more!

PART OF SPEECH TAGGING

Computing with Natural Language

Learn advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures.

MACHINE TRANSLATION

Communicating with Natural Language.Learn voice user interface techniques that turn speech into text and vice versa. Build a speech recognition model using deep neural networks in natural language processing(NLP).

SPEECH RECOGNIZER

We recommend our natural language processing(NLP) course as the perfect starting point for your deep learning education.

The advanced natural language processing(NLP) techniques allow the non-programmers to interact with the computing systems and obtain useful information from it. Using natural language processing(NLP) the common synonyms for the input phrases can be detected and match them with the right answers, it helps the users who are unfamiliar with the terminologies of the computing system. Spam filtering, language understanding, text classification, information extraction, question answering, Social website feeds, Voice recognition and speech-to-text are the other typical applications of natural language processing(NLP)

There are many open source Natural Language Processing (NLP) libraries and these are some of them:

• Natural language toolkit (NLTK)
• Gate NLP library
• Apache OpenNLP.
• Stanford NLP suite
• MALLET

NLTK is more popular and the leading platform for building natural language processing(NLP) applications, which is written in python. It provides an intuitive framework along with substantial building blocks, consistent interfaces and data structures.

By the end of this course you will:

• Have an understanding of how to use the Natural Language Tool Kit.
• Be able to load and manipulate your own text data.
• Know how to formulate solutions to text based problems.
• Know when it is appropriate to apply solutions such as sentiment analysis and classification techniques.

What is Natural Language Processing (NLP) ?

Natural Language Processing or NLP is “ability of machines to understand and interpret human language the way it is written or spoken”.

The objective of Natural Language Processing(NLP) is to make our computers or machines as much intelligent as the human beings are in understanding various types of language.

The ultimate goal of Natural Language Processing is filling the gap between natural language and machine language.

Natural language processing (NLP) is able to analyze, understand, and generate human speech. The goal of Natural language processing(NLP) is to make interactions between computers and humans just like the interactions between one humans to another human.
And when we say interactions between humans we’re talking about how humans communicate with each other by using natural language. Natural language is a language that is native to people. English, Spanish, French, and portuguese are all examples of a natural language.

On the other hand, computers have their artificial languages like SQL, Java, C,.C++ which were constructed to communicate instructions to machines.

Because computers operate on artificial languages, they are unable to understand natural language. This is the problem that Natural language processing(NLP) solves. With Natural language processing(NLP) , a computer is able to listen to a natural language being spoken by a person, understand the meaning then respond to it by generating natural language to communicate back to the person.

But there are also several complex steps involved in that process.Natural language processing(NLP) is a field of computer science that has been around for a while, but has gained much popularity in recent years as advances in technology have made it easier to develop computers with Natural language processing(NLP) abilities.

Why Natural Language Processing is so important ?

Natural language processing or NLP is important for different reasons to different people. For some, Natural language processing(NLP) offers the utility of automatically harvesting arbitrary bits of knowledge from vast information resources that have only recently emerged. To others, Natural language processing(NLP) is a laboratory for the investigation of the human use of language which is a primary cognitive ability and its relation to thought.

So, there is a question arise, who cares? Then let's consider this response: human civilization is drowning in data. In 2008, According to the Google reports, the web had one trillion pages. Today, it estimates the web at 30 trillion pages. Merrill Lynch projects that available data will expand to 40 zettabytes by 2020. These estimates include video and image data, as well as the structured data in databases.

With Natural Language Processing(NLP), it is possible to perform certain tasks like Automated Speech and Automated Text Writing in less time.

Due to the presence of large data or text around, why not we use the computers untiring willingness and ability to run several algorithms to perform tasks in no time.

These tasks include other Natural Language Processing(NLP) applications like Automatic Summarization and Machine Translation

NLP is one of the most important and emerging technology nowadays. Natural Language Processing(NLP) drives many forms of AI you're used to seeing. The reason to focus on this technology instead of something like AI for math-based analysis, is the increasingly large application for Natural Language Processing(NLP)

If we think about it this way. Every day, humans say millions of words that other humans interpret to do countless things. At its core, it's simple communication, but we all know words run quite deeper than that. And the context is we derive from everything someone says. Whether they imply something with their body language or in how often they mention any specific word or phrase. While Natural Language Processing(NLP) doesn't focus on voice inflection, it does draw on contextual patterns.

This is where it gains its value. Let's use an example to show just how powerful Natural Language Processing(NLP) is when used in a practical situation. When you're typing on mobile, you'll see word suggestions based on what you type and what you're currently typing. That's natural language processing(NLP) in action.

Free-form text or should I say the Unstructured data,comprises 70%-80% of the data available on computer networks. The information content of this resource is unavailable to governments, public services, businesses, and individuals unless humans read these texts or devise some other means to derive information value from them. Natural language processing(NLP) can be applied to characterize, interpret, or understand the information content of free-form text.

At present days, most natural language processing(NLP) aims to characterize text according to arbitrary notions of effective content or similarity as in sentiment analysis, text clustering, and document classification.Some natural language processing(NLP) efforts today aim to interpret free-form text to extract information with which to answer directed questions or populate databases as in information extraction, question-answering, and bioinformatics. This work requires processing with a comparatively more refined sensitivity for intended meaning.

It's such a little thing that most of us take for granted, and have been taking for granted for years, but that's why Natural Language Processing(NLP) becomes so important.

NLP then allows for a quick compilation of the data into terms obviously related to their brand and those that they might not expect. Capitalizing on the uncommon terms could give the company the ability to advertise in new ways.

As NLP develops we can expect to see even better human to AI interaction. Devices like Google's Assistant and Amazon's Alexa, which are now making their way into our homes and even cars, are showing that AI is here to stay.

The next few years should see AI technology increase even more, with the global Natural Language Processing(NLP) and AI market expected to push around $60 billion by 2025 (assumed). Needless to say, you should keep an eye on Natural Language Processing(NLP) and AI.

How is Natural language processing used today?

There are several different tasks that Natural language processing(NLP) can be used to accomplish, and each of those tasks can be done in many different ways. Let’s look at some of the most common applications for NLP today:

SPAM FILTERS

One of the biggest headaches of email is spam. Natural language processing(NLP) is used filtering the spam mails and messages to set up a first line of defense, services such as Gmail use Natural language processing(NLP) to determine which emails are good and which are spam. These spam filters scan the text in all the emails you receive, and attempt to understand the meaning of that text to determine if it’s spam or not using Natural language processing(NLP).

ALGORITHMIC TRADING

Wouldn’t it be amazing if you could master the stock market without having to do a thing? That’s what algorithmic trading is for. Natural language processing(NLP) comes here to help you in this case.Using Natural language processing(NLP), this technology reads news stories concerning companies and stocks and attempts to understand the meaning of them to determine if you should buy, sell, or hold onto certain stocks.

ANSWERING QUESTIONS

If you’ve ever typed a question in Google search, or asked Siri for directions, then you’ve seen this form of Natural language processing(NLP) in action. A major use of Natural language processing(NLP) is to make search engines understand the meaning of what we are asking, and then often times generating natural language in return to give us the answers we’re looking for.

SUMMARIZING INFORMATION

There’s a lot of information on the web, and a lot of that information is in the form of long documents or articles. Natural language processing(NLP) is used to understand the meaning of this information, and then generates shorter summaries of the information so humans can understand it quicker.

There are three different levels of linguistic analysis done before performing Natural language processing(NLP) -

• Syntax - What part of given text is grammatically true.
• Semantics - What is the meaning of given text?
• Pragmatics - What is the purpose of the text?

Natural language processing(NLP) deals with different aspects of language such as

1. Phonology - It is systematic organization of sounds in language.
2. Morphology - It is a study of words formation and their relationship with each other.

Here are the approaches of Natural language processing(NLP) to understand the semantic analysis

• Distributional - It employs large-scale statistical tactics of Machine Learning and Deep Learning.
• Frame-Based - The sentences which are syntactically different but semantically same are represented inside data structure (frame) for the stereotyped situation.
• Theoretical - This approach is based on the idea that sentences refer to the real word (the sky is blue) and parts of the sentence can be combined to represent whole meaning.
• Interactive Learning - It involves pragmatic approach and user is responsible for teaching the computer to learn the language step by step in an interactive learning environment.

The true success of Natural language processing(NLP) lies in the fact that humans deceive into believing that they are talking to humans instead of computers.

Many devices use Natural language processing(NLP) nowadays

Those are just a handful of the ways Natural language processing(NLP) is used today. But by looking at those few examples you might have spotted some patterns. Have you noticed that in all examples, Natural language processing(NLP) was used to understand natural language? And in most cases, it was also used to generate natural language.

In case the text is composed of speech, speech-to-text conversion is performed.

The mechanism of Natural Language Processing(NLP) involves two processes:

• Natural Language Understanding (NLU) and
• Natural Language Generation (NLG).

Some real world applications of Natural Language Processing(NLP)

Learning has helped computers parse the ambiguity of human language.

Apache OpenNLP, Natural Language Toolkit(NLTK), Stanford NLP are various open source Natural language processing(NLP) libraries used in real world application below.

Here are multiple ways Natural Language Processing(NLP) is used today:

• The most basic and well known application of Natural language processing(NLP) is Microsoft Word spell checking.
• Text analysis, also known as sentiment analytics is a key use of Natural language processing(NLP).
• Email filters are another important application of Natural language processing(NLP). By analyzing the emails that flow through the servers, email providers can calculate the likelihood that an email is spam based its content by using Bayesian or Naive bayes spam filtering.
• Call centers representatives engage with customers to hear list of specific complaints and problems. Mining this data for sentiment can lead to incredibly actionable intelligence that can be applied to product placement, messaging, design, or a range of other use cases using Natural language processing(NLP).
• Google and Bing and other search systems use Natural language processing(NLP) to extract terms from text to populate their indexes and to parse search queries.
• Google Translate applies machine translation technologies in not only translating words, but in understanding the meaning of sentences to provide a true translation.Another advantage of Natural language processing(NLP).
• Many important decisions in financial markets use Natural language processing(NLP) by taking plain text announcements, and extracting the relevant info in a format that can be factored into algorithmic trading decisions.
• Since the invention of the typewriter, the keyboard has been the king of human-computer interface. But today with voice recognition via virtual assistants,like Amazon’s Alexa, Google’s assistant, Apple’s Siri and Microsoft’s Cortana respond to vocal prompts using Natural language processing(NLP) and do everything from finding a coffee shop to getting directions to our office and also tasks like turning on the lights in home, switching the heat on etc. depending on how digitized and wired-up our life is.
• Question Answering - IBM Watson which use Natural language processing(NLP), is the most prominent example of question answering via information retrieval that helps guide in various areas like healthcare, weather, insurance etc.

Therefore it is clear that Natural Language Processing(NLP) takes a very important role in new machine human interfaces. It’s an essential tool for leading-edge analytics & is the near future.

How Much Does a Natural Language Processing(NLP) Make a Year ?

The salaries of Natural Language Processing engineers are also very attractive.

The average annual pay for a Natural Language Processing(NLP) Across the U.S. is $123,798 a year.

Data Scientist - $75K to $134K

Software Engineer - $70K to $142K

Machine Learning Engineer - $57K to $153K

Senior Data Scientist - $120K to $180K

Senior Software Engineer - $106K to $178K

Sr. Software Engineer / Developer / Programmer - $91K to $163K

Career Opportunities in Natural Language Processing and Companies using Natural Language Processing:

In Natural Language Processing(NLP) there are no. of jobs worldwide and the companies which are hiring Natural Language Processing(NLP) experts some are listed below

• Amazon
• Apple
• Google
• IBM
• Microsoft
• Intel
• Facebook
• Twitter
• Instagram
• Samsung
• AIBrain
• Bing
• Yahoo
• Anki
• Banjo
• CloudMinds
• Deepmind
• H2O
• iCarbonX
• Iris AI
• Next IT
• Nvidia
• OpenAI
• Salesforce
• SoundHound
• Klevu
• EnglishCentral
• Yummly
• Insight Engines
• MindMeld
• Desti
• MarketMuse
• Kngine
• NetBase

Now again coming to this course,So this Natural Language Processing(NLP) course is ideal for beginners to experts to learn the artificial Intelligence technology or those who are new to Natural Language Processing(NLP) engineering or who want to enrich their knowledge in Natural Language Processing(NLP) much more.

From this course you'll get

1. Introduction & Overview on Natural Language Processing(NLP)
2. Introduction to NLTK toolkit
3. Introduction to Machine Learning
4. Machine Learning for binary and multi class classification
5. Introduction to Word Embedding in Natural Language Processing(NLP)
6. Deep neural networks for word embedding – Word2Vec, GloVe
7. Document and Sentence Embedding in Natural Language Processing(NLP)
8. Sentiment Analysis in Natural Language Processing(NLP)
9. Classical and Deep ML
10. Classical and Deep ML
11. Neural Machine Translation in Natural Language Processing(NLP)

There are some free to preview of the course,so you can already put a glance over it before buying the course!

So what are you waiting for? Enroll in the course and get started with Natural Language Processing(NLP)

today!

You will get 30-day money-back guarantee from Udemy for this Natural Language Processing course.

If not satisfied simply ask for a refund within 30 days. You will get full refund. No questions whatsoever asked.

Are you ready to take your Natural Language Processing skills higher and career to the next level, take this course now!

You will go from zero to Natural Language Processing(NLP) hero in few hours.