Top 10 Applications of Natural Language Processing (NLP)

Top 10 Applications of Natural Language Processing (NLP)

Natural Language Processing (NLP): What it is and why it matters. What tasks can be solved with NLP? The scope is great and every day the number of tasks is increasing. In this post, you'll see top 10 Applications of Natural Language Processing. Natural Language Processing (NLP): Top 10 Applications to Know

Words, words, words… have you ever thought about how important they are? Communications, books, messages, telephone conversations, songs, movies… it is hard to imagine our world without language, isn’t it?

Just think about how many text and voice data we face every day. What about deriving meaning from this data and do something cool? Now we have systems that can do additional functions with our language. These systems are based on NLP — Natural Language Processing — the mixture of artificial intelligence and computational linguistics.

If it seems you have never encountered NLP, just open Google, click on access to voice match and say: “Ok, Google …” (other examples — Siri from Apple, Cortana from Microsoft). You will get needed information based on your voice request and all this due to the ability of NLP-based devices to understand the human language.

So, NLP is the machine’s ability to process what was said, structure the information received, determine the necessary response and respond in a language that we understand. So, how does NLP work, and what is NLP used for? I think everyone should be well-oriented in questions like this and for this reason, I made this post full of useful info.

Without further ado, let’s talk science!

How Does Computer Understand Text?

What do words and phrases mean to a computer, which can only understand zeroes and ones? It may seem not an easy task to teach machines to understand our communication. Well, yes and no. In a nutshell, the process of machine understanding using natural language processing algorithms looks like this:

1. A person says something to the machine.

2. The machine records sound.

3. The machine turns audio into text.

4. The NLP system parses the text into components, understands the context of the conversation and the intention of the person.

5. Based on the results of the NLP, the machine determines which command should be executed.

In short, it’s a process of creating algorithms that transform the text into words labeling them based on the position and function of the words in the sentence. For this, word embedding is a silver bullet to resolve many NLP problems. It transforms human language meaningfully into a numerical form. This allows computers to understand the nuances implicitly encoded into our languages.

The main idea here is every word can be converted to a set of numbers — an N-dimensional vector that stores information about the word’s meaning. Although every word gets assigned a unique vector/embedding, similar words end up having values closer to each other. For example, the vectors for the words ‘Man’ and ‘Boy’ would have a higher similarity than the vectors for ‘Boy’ and ‘Lion’.

Its goal is twofold: to improve other NLP tasks, such as machine translation, or to analyze similarities between words and groups of words. Of course, everything works well if the task is simple and straightforward. However, human speech is significantly different from the speech of a robot. The main difficulty for developers is the machine takes everything literally. Our language is very saturated and filled with poly-semantic words and hidden meanings.

Top 10 Applications of Natural Language Processing

What tasks can be solved with NLP? The scope is great and every day the number of tasks is increasing. Here are the most popular applications of NLP:

1. Machine Translation

Everyone knows what is a manual translation — we translate information from one language into another. When the same thing is done by a machine, we deal with “Machine” Translation. The idea behind MT is simple — to develop computer algorithms to allow automatical translation without any human intervention. The best-known application is probably Google Translate.

Google translate is based on SMT — statistical machine translation. It is not the work of word-for-word replacement alone. Google translate gathers as much text as it can find that seems to be parallel between two languages, and then it crunches data to find the likelihood that something in Language. And this is similar to us human, when we were children, we begin to assign semantic value to words, and abstract and extrapolate these semantic values given combinations of words.

But all that glitters is not gold and Machine translation is challenging given the inherent ambiguity and flexibility of human language. While human cognitive processes language interpretation or understanding, and translation on many levels, a machine processes data, linguistic form and structure, not meaning and sense.

2. Speech Recognition

Did you know that voice recognition technology has been around for 50 years? For half a century, scientists have been solving this problem, and only in the last few decades, NLP allowed to achieve significant success. Now we have a whole variety of speech recognition software programs that allow us to decode the human voice. It is a mobile telephony, home automation, hands-free computing, virtual assistance, video games, and etc.

All-in-all, this technology is being used to replace other methods of input like typing, clicking, or selecting text in any other way. Today, speech recognition is a hot topic that is part of a large number of products, for example, voice assistants (Cortana, Google Assistant, Siri, …). Everyone knows these apps are not so perfect. With a more complex task, NLP and neural networks do not cope well with their tasks.

But who knows, maybe this problem will be solved with time?

3. Sentiment Analysis

Sentiment analysis (also known as opinion mining or emotion AI) is an interesting type of data mining that measures the inclination of people’s opinions. The task of this analysis is to identify subjective information in the text. For example, this can be a movie review or an emotional state caused by this movie. Why do we need this? Companies use sentiment analysis to keep abreast of their reputation.

Sentiment analysis helps to check whether customers are satisfied with goods or services. Classical polls have long faded into the background. Even those who want to support brands or political candidates are not always ready to spend time filling out questionnaires. However, people willingly share their opinions on social networks. The search for negative texts and the identification of the main complaints significantly helps to change concepts, improve products and advertising, as well as reduce the level of dissatisfaction. In turn, explicit positive reviews increase ratings and demand.

4. Question Answering

Question answering (QA) is concerned with building systems that automatically answer questions posed by humans in a natural language. Sounds complicated? Well then here are the real examples of Question-Answering applications: Siri, OK Google, chat boxes and virtual assistants. I know that I have already mentioned these apps. But here is the point — all of them have a few NLP-applications or functions — to understand speech is only half of the path and another one naturally is to give a response.

5. Automatic Summarization

Going back to the amount of text data we face every day, information overload could be a real drawback but now we have Automatic Summarization. This is the process of creating a short, accurate, and fluent summary of a longer text document. The most important advantage of using a summary is it reduces the reading time. Here are some of the APIs you can try: Aylien Text Analysis, MeaningCloud Summarization, ML Analyzer, Summarize Text, Text Summary.

6. Chatbots

The first chatbots appeared in the 1960s, they were quite primitive: they basically rephrased what person spoke to them. Modern chatbots are not far from their ancestors. NLP has become the basis for creating chatbots, and although such systems are not so perfect they easily can handle standard tasks. Chatbots currently operate on several channels, including the Internet, applications, and messaging platforms. Businesses today are interested in developing bots that can not only understand a person but also communicate with him at one level. The latter, in truth, does not always work.

7. Market Intelligence

Marketers also use NLP to search for people with a likely or explicit intention to make a purchase. Behavior on the Internet, maintaining pages on social networks and queries to search engines provide a lot of useful unstructured customer data. Selling the right ad for internet users allows Google to make the most of its revenue. Advertisers pay Google every time a visitor clicks on an ad. A click can cost anywhere from a few cents to more than $ 50.

At its core, market intelligence uses multiple sources of information to create a broad picture of the company’s existing market, customers, problems, competition, and growth potential for new products and services. Sources of raw data for that analysis include sales logs, surveys, and social media, among many others.

8. Text Classification

Text classification is the task of assigning a set of predefined categories to free-text. Text classifiers can be used to organize, structure, and categorize pretty much anything. What is it? Suppose you distribute documents in certain categories. A new document arrives, and it is necessary to determine to which category it belongs. By using NLP, text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content.

9. Character Recognition

Character Recognition systems also have numerous applications like receipt character recognition, invoice character recognition, check character recognition, legal billing document character recognition, and so on.

10. Spell Checking

A spell checker is a software tool that identifies and corrects any spelling mistakes in a text. Most text editors let users check if their text contains spelling mistakes. One of the most vivid examples is the Grammarly app. It is an online grammar checker that scans your text for all types of mistakes, from typos to sentence structure problems and beyond.

Wrapping it up: What makes NLP Difficult?

The very nature of human natural language makes some NLP tasks difficult: not all laws can be effectively formalized, some phenomena are very abstract. For example, the task of automatically detecting sarcasm, irony, and implicatures in texts has not yet been effectively solved. NLP technologies still struggle with the complexities inherent in elements of speech such as similes and metaphors.

But, I think we shouldn’t wait perfect results right from the start. Today, NLP is great for solving tasks associated with morphological word processing: determining the initial form of words and all possible word forms. NLP is great for solving classification problems. The task of personal assistants, tuned to a specific area of services, is more or less well solved: book a table in a restaurant, buy a ticket for a plane and more. Let’s do not rush thongs and see what will be next.

Thanks for reading!

5 Python Online Courses for Beginners

5 Python Online Courses for Beginners

If you are thinking to learn a new programming language then also Python is a good choice, particularly if you are looking to move towards a lucrative career path of Data Science and Machine learning which has lots of opportunities. In this article, I am going to share some of the best online courses to learn Python in 2020...

Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options.

Python is relatively simple, so it's easy to learn since it requires a unique syntax that focuses on readability. Developers can read and translate Python code much easier than other languages. In turn, this reduces the cost of program maintenance and development because it allows teams to work collaboratively without significant language and experience barriers.

Additionally, Python supports the use of modules and packages, which means that programs can be designed in a modular style and code can be reused across a variety of projects. Once you've developed a module or package you need, it can be scaled for use in other projects, and it's easy to import or export these modules.

In recent years, Python has also become a default language for Data Science and Machine learning Projects and that's another reason why many experienced programmers are learning Python .

If you are thinking to learn a new programming language then also Python is a good choice, particularly if you are looking to move towards a lucrative career path of Data Science and Machine learning which has lots of opportunities. In this article, you will find free online courses in python programming, but not only will you find one, but you will also find 5 more courses on Python! I am going to share some of the best online courses to learn Python in 2020

They are high quality courses with more than 4 star rating (from 0 to 5 stars), that means if you are starting your career with the python programming language, these are the best courses that will take you step-by-step , to start and learn from scratch the fundamentals about this language that so professional and useful has been in recent years.

Top 5 Courses to Learn Python in 2020

1. Complete Python Bootcamp: Go from zero to hero in Python

This is one of the most popular course to learn Python on Udemy and more than 250,000 students have enrolled in it. That speaks volumes for the quality of the course.

This is a comprehensive but straight-forward course to learn the Python programming language on Udemy! and useful for all levels of programmers.

In this course, you will learn Python 3 in a practical manner. You will start by downloading and setting up Python on your machine and then slowly move on to different topics.

It's also a practical course where an instructor will show you live coding and explain what he does.

The course also comes with quizzes, notes and homework assignments as well as 3 major projects to create a Python project portfolio! which complements your learning.

2. 30 Days of Python | Unlock your Python Potential

In early 2016, Python passed Java as the #1 beginners language in the world. Why? It's because it's simple enough for beginners yet advanced enough for the pros.

You can not only write simple scripts to automate stuff but also create a complex program to handle trades. You can even use Python for it for IOT, Web Development, Big Data, Data Science, Machine learning and more.

This is a very practical course and useful not just for beginners but also for programmers who know other programming languages e.g. Java, C++ and want to learn Python.

In 30 days this course will teach you to write complex Python applications to scrape Data from nearly any website and Build your own Python applications for all types of automation. It's perfect for busy developers who learn by doing serious stuff.

3. Python for Beginners with Examples

This online Python course is taught by Ardit Sulce ,This Python course has everything you need to know to start coding in Python and not even that, by the end of the course you will know how to build complete programs and also build graphical user interfaces for your programs so you can impress your employer or your friends. This course will guide you step by step starting from the basics and always assuming you don't have previous programming experience or a computer science degree. In fact, most people who learn Python come from a vast variety of careers.

This course has all you need to get you started. After you take it you will be ready to go to the next level of specializing in any of the Python paths such as data science or web development. Python is one of the most needed skills nowadays. Sign up today!

4. Learn Python Programming Masterclass

This is another fantastic course to learn Python on Udemy. This course is taught by Tim Buchalka,I am a big fan of Tim Buchalka and have attended a couple of his courses.

This course is aimed at complete beginners who have never programmed before, as well as existing programmers who want to increase their career options by learning Python.

The fact is, Python is one of the most popular programming languages in the world – Huge companies like Google use it in mission critical applications like Google Search.

And Python is the number one language choice for machine learning, data science and artificial intelligence. To get those high paying jobs you need an expert knowledge of Python, and that’s what you will get from this course.

By the end of the course you’ll be able to apply in confidence for Python programming jobs. And yes, this applies even if you have never programmed before. With the right skills which you will learn in this course, you can become employable and valuable in the eyes of future employers.

5. The Python Bible™ | Everything You Need to Program in Python

This course was developed by Ziyad Yehia , a renowned instructor on Udemy. Currently, This course has nearly 78,000 students and excellent star ratings.

This is a project-based course and you will build 11 Projects int this Python Course.

If you enjoy hands-on learning while working on the project rather than learning individual concept then this course is for you.

This is a comprehensive, in-depth and meticulously prepared course and teaches you everything you need to know to program in Python. It delivers what is promised in the title, A-Z, it's all here!


That's all about the best courses to learn Python in depth. you can begin with these courses, don't need to buy all of them, just choose the one where you can connect with instructor.

These courses will give you a solid foundation and confidence to use Python in your project.


Thanks for reading

What NOT to Do in the Data Science Domains Industry

What NOT to Do in the Data Science Domains Industry

Data science is linked to numerous other modern buzzwords such as big data and machine learning, but data science itself is built from numerous domains, where you can get your expertise. These domains include the following: * Statistics *...

Data science is linked to numerous other modern buzzwords such as big data and machine learning, but data science itself is built from numerous domains, where you can get your expertise. These domains include the following:

  • Statistics
  • Visualization
  • Data mining
  • Machine learning
  • Pattern recognition
  • Data platform operations
  • Artificial intelligence
  • Programming
    Math and statistics
    Statistics and other math skills are essential in several phases of the data science project. Even in the beginning of data exploration, you'll be dividing the features of your data observations into categories:
  • Categorical
  • Numeric:
  • Discrete
  • Continuous
    Continuous values have an infinite number of possible values and use real numbers for the representation. In a nutshell, discrete variables are like points plotted on a chart, and a continuous variable can be plotted as a line.
    Another classification of the data is the measurement-level point of view. We can split data into two primary categories:
  • Nominal
  • Ordinal
  • Quantitative:
  • Interval
  • Ratio
    Nominal variables can't be ordered and only describe an attribute. An example would be the color of a product; this describes how the product looks, but you can't put any ordering scheme on the color saying that red is bigger than green, and so on. Ordinal variables describe the feature with a categorical value and provide an ordering system; for example Education—elementary, high school, university degree, and so on.

Visualizing the types of data
Visualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions. When it comes to communicating, this means describing your findings, or the way techniques work to audiences, both technical and non-technical. Different types of data have different ways of representation. When we talk about the categorical values, the ideal representation visuals would be these:

  • Bar charts
  • Pie charts
  • Pareto diagrams

Frequency distribution tables
A bar chart would visually represent the values stored in the frequency distribution tables. Each bar would represent one categorical value. A bar chart is also a baseline for a Pareto diagram, which includes the relative and cumulative frequency for the categorical values:

Bar chart representing the relative and cumulative frequency for the categorical values
If we'll add the cumulative frequency to the bar chart, we will have a Pareto diagram of the same data:

Pareto diagram representing the relative and cumulative frequency for the categorical values
Another very useful type of visualization for categorical data is the pie chart. Pie charts display the percentage of the total for each categorical value. In statistics, this is called the relative frequency. The relative frequency is the percentage of the total frequency of each category. This type of visual is commonly used for market-share

*Statistics *
A good understanding of statistics is vital for a data scientist. You should be familiar with statistical tests, distributions, maximum likelihood estimators, and so on. This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren't) a valid approach. Statistics is important for all types of companies, especially data-driven companies where stakeholders depend on your help to make decisions and design and evaluate experiments.

Machine learning
A very important part of data science is machine learning. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
Choosing the right algorithm**
When choosing the algorithm for machine learning, you have to consider numerous factors to properly choose the right algorithm for the task. It should not only be based on the predicted output: category, value, cluster, and so on, but also on numerous other factors, such as these:

  1. Training time
  2. Size of data and number of features you're processing
  3. Accuracy
  4. Linearity
  5. Number of possible parameters
    Training time can range from minutes to hours, depending not only on the algorithm but also on the number of features entering the model and the total amount of data that is being processed. However, a proper choice of algorithm can make the training time much shorter compared to the other. In general, regression models will reach the fastest training times, whereas neural network models will be on the other side of the training time length spectrum. Remember that developing a machine-learning model is iterative work. You will usually try several models and compare possible metrics. Based on the metrics captured, you'll fine-tune the models and run comparisons again on selected candidates and choose one model for operations. Even with more experience, you might not choose the right algorithm for your model at first, and you might be surprised that other algorithms can outperform the first chosen candidate, as shown:

Big data
Big data is another modern buzzword that you can find around the data management and analytics platforms. The big does not have to mean that the data volume is extremely large, although it usually is. learn more Data science online course
SQL Server and big data
Let's face reality. SQL Server is not a big-data system. However, there's a feature on the SQL Server that allows us to interact with other big-data systems, which are deployed in the enterprise. This is huge!
This allows us to use the traditional relational data on the SQL Server and combine it with the results from the big-data systems directly or even run the queries towards the big-data systems from the SQL Server. The answer to this problem is a technology called PolyBase:

Introduction to Face Processing with Computer Vision

Introduction to Face Processing with Computer Vision

In this Computer Vision tutorial, we’ll help you understand some of the Computer Vision, Face Processing with Computer Vision and Machine Learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more.

Ever wonder how Facebook’s facial recognition or Snapchat’s filters work? Faces are a fundamental piece of photography, and building applications around them has never been easier with open-source libraries and pre-trained models. In this talk, we’ll help you understand some of the computer vision and machine learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more.