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Tag: Machine Learning

Importance of R Programming As a Tool in the Machine Learning Spaces

Posted on by Imarticus Learning

With the announcement that, Data Science is one of the sexiest careers of the 21st century and the subsequent success of e-commerce giants with the help of data analytics, the world has seen a rise in the demand for data scientists. These professionals who are also known as Data Analysts are responsible for extracting this data, mining it, analysing it and drawing insights so as to add value to the firms.

A lot of companies across different fields have been hiring these professionals due to their specific skill set and the ability to turn numbers into growth and success. These professionals usually work with one or many data analytics tools like, SAS Programming, R Programming, Hadoop and Python and so on. Of these, R Programming is one tool, which has recently seen a lot of popularity and increased number of users, due to the fact that it is open sourced and easily accessible.

All of these data analytics tools are used by the professionals, depending upon what kind of job they want to accomplish.
Data AnalyticsR Programming is most popular choice, when it comes to thoroughly understanding data, by using various graphs and statistical methods. This tool becomes especially important when it comes to machine learning spaces, mainly due to its several packages and advanced implementations, useful for the top machine learning algorithms, which every data scientist is familiar with. All of these packages help in different kind of functions and are highly sought after, because they can be downloaded free of cost. Being an open sourced platform, R Programming has a huge community of contributors, the world over, who regularly add technical updates, which can easily be added to your projects.
These packages that are a part of R Programming can perform various specific functions like, take care of the missing values, allow you to partition your data, classify and combine, and find out the hidden layers in your data and so on. This vastness and variety in packages, makes for R’s strongest suit. Furthermore it is able to offer rich functionality, for the developers so as to enable them to build their own tools and analysing methods. Being open sourced, has given R a lot of leverage over the other data analytics tools, as the users can very well extend it without the need for any permission. What increases R Programming’s importance in the space of machine learning, is the fact that any new research in the field of data science, has a compatible package of R with it.
CIRP- Certification in R ProgrammingR Programming since its inception in around the late 90’s, has only seen major growth; and now, it is being assimilated into various commercial fields such as Oracle, IBM, MATLABS and others. There is a long list of companies in the data analytics industry, which have already declared their adoption of the platform. Adding to its popularity, it has been declared as the most popular platform for any successful practicing Data Scientist. As its popularity increases, there are a lot of institutes offering certification courses in this data analytics tool. Imarticus Learning is a leading education institute, offering industry-endorsed courses in R Programming, in both classroom and online format.

What Are The Application of Machine Learning in Medicine?

Posted on by Imarticus Learning

Medicine and healthcare are leading industries with altruistic goals. Smart applications of the Machine Learning are today playing a role in a variety of areas like billing, insurance claims, record-keeping, patient-care, staffing solutions and many more. Just over the last decade technology has changed much. So have the volumes of data and its complexities, the various smart devices being used, and the algorithms specific to medical care.

ML is the system of algorithms developed for specific tasks that use deep-learning and artificial intelligence to simulate the way in which the human brain works to execute and aid us in complex tasks. Hence Machine Learning Course is an evolving and very important field. The ability of the ML algorithms to accurately predict, self-learn and assist us with forecasts is truly amazing and way beyond what the human brain is capable of. The field of ML is lucrative too!

Let us look at some of the best applications of ML in the medical and allied fields.
1. Diagnosis on a global scale: ML algorithms and applications score in disease diagnosis, providing simple diagnosis and online treatment even in rural areas, identifying patterns in the disease progression that is hard to recognize, and creating a global repository of research to help better the healthcare segment. Some of these are cancer detection, treating underlying genetic conditions hard-to-diagnose like Parkinson’s, diabetes, etc, providing therapeutic aid, and predicting conditions like stress, depression, etc.

2. CAT scans and MRI aided Diagnosis: Deep Learning and ML have been responsible for the development of advanced imaging devices like the Computer-Aided-Tomography scans, Magnetic-Resonance-Imaging, and the non-invasive Doppler or ultrasound scans. These developments embody machine learning training, handling of large data volumes and the ability of AI and can easily be used to forecast accurately the development of tumours and aneurysms.

3. Manufacturing and discovery of new drugs: Research, discovery, and use of the latest drugs are very important today as we discover more diseases and strains that are traditional-drug-resistant. ML allows the learning in an unsupervised fashion and helps develop new drugs with optimum dosages which can even be personalized for cancer patients.

4. Modification behavior-therapy: Prevention being better than cure several startup firms have come out with gesture-control based therapy apps, symptomatic analysis, early cancer warning, and detection apps, sleep trackers and such behavior-sensing apps that can help even with predicting the susceptibility to genetic diseases. For ex: Angelina Jolie underwent mastectomy based on such a forecast of cancer.

5. App-based medical personalization: Currently medical predictions use biosensors, historical data, genetic information and symptoms to diagnose ailments which tend to have rather fixed treatment methods. The time is not far where ML can offer holistic personalized treatment options which can help faster recoveries by identifying the underlying cause, diets, optimum medicine dosages, and recovery paths in a moment. Even the very process of seeking doctor’s advice appears to be going online to reach out further into rural areas and the personalization of healthcare regimens.

6. Outbreak Prediction: ML, deep-learning and AI have succeeded in not only assisting in healthcare but can also accurately and through unsupervised learning predict and forecast epidemic outbreaks which can then be nipped in the bud.

7. Smart Record Keeping and insurance claims:
The better the maintenance of records the easier cashless hospitalization and treatment becomes. ML is taking very large strides with smart apps to enable smart health-records in real-time, better document classification and intelligent paperless claim settlements.

8. R and D and medical trials: ML has and bears high potential in these areas of medical care where innovation and data play a huge role.

9. Data Crowdsourcing: The smart apps powered through intelligent ML allows doctors globally to crowdsource their data resources making it easy to give a real-time diagnosis, treatment, and better healthcare facilities. Even operations and complex deliveries can now be conducted easily and safely.
10. Radiotherapy and medical imaging:
Radiology, laser treatments, pathology, gastroenterology, cosmetology and several disciplines of medicine are being innovatively modernized with smart algorithmic-based ML to make model-making of individual cases much easier to treat successfully.

In conclusion, ML is growing by the moment and now is the right time to get on board this transformation. If you want to learn Machine Learning and reap career benefits in the medical field then you need to have Machine Learning Training.

At Imarticus Learning, the mentored specialization in skill-oriented courses like ML is par excellence and comes with certification, skill-based training, personality development, and assured placements. Hurry!

How The Machine Learning Works Behind Your Favorite Google Meet Backgrounds?

Posted on by Imarticus Learning

Google Meet has been a lifesaver for many professionals and students who are unable to step out of their homes for the last few months. This increasing usage of such virtual meeting platforms has improved the technology reach. This and Google AI has now increased the need for Machine Learning training and opened up a whole new world in technology.

Google’s AI of Google Meet now allows the user to change the background and reduce the noise level as well. Instead of the boring or the interiors of the home as the background, Machine Learning has helped customize the backgrounds for such meetings.

The technology behind the backgrounds

Google uses MediaPipe Objectron to get the 3D dimensions of images on mobile devices. It is also useful for background changes as well. They came up with an in-browser version of the Machine Learning model that can blur or replace the backgrounds. With these combined efforts of the ML, MediaPipe, and the OpenGL technology, its performance is better even in the devices with low power available.

Google uses WebGL for rendering, ML such as TFLite, and ZNNPack for web-based interference.

How does it work?

The MediaPipe uses the new low-level format of the binary code of WedAssembly. This can speed up the processing faster than JavaScript and can improve the speed of the tasks as well. The instructions from the WedAssembly are converted into simpler code by the browser.

  • First of all the ML segregates the user and its background.
  • Now, the user is masked by the ML interference into a low-resolution component.
  • The mask undergoes processing to refine its edges to be a smooth blend with the new background.
  • A WebGL2 is used to get the final output for the video where the mask is suitable with the replaced or the blurred background.

The technology here uses a lighter interference that uses less power and smaller storage space.

Refining the results

Although the masking effect is refined so it makes it easier to blend with the background, it could still end up having a halo effect. The light wrapping disables this possibility. The composting technique refines the edges of the mask and also allows the background light to adjust itself to blend the user with itself. The technique allows the light from the background to spill all over the edges of the mask to conceal the halo effect. This results in the fine blending of the background with the foreground image.

Performance in various devices

In the high-end devices, the image transition through the ML system continues at a higher resolution but in the low-end devices, there is a slight change. In the latter, the working mechanism automatically switches through the lighter models of ML so as to maintain the performance speed. Here, it skips the image refining process to send the final output.

The flexible configuration of the MediaPipe enables it to choose the most effective processing method.

Google AI and ML

The regular updates on Google AI and algorithms have opened new scope in the field of Machine Learning and its various prospects. While the Machine Learning Course provides basic knowledge, there is more to it when it is learned properly.

artificial intelligence coursesSince the internet-based virtual meetings are not going to disappear anytime soon, more changes in the working are expected. With each change, there is more to learn which naturally increases the importance of Machine learning and AI.

Bottom Line

Seeing all these, it would be not a bad idea to enroll in a machine learning course to start with the basics. Though this is a field with no limits, there is sure a lot to learn.

How to Start a Career in Machine Learning?

Posted on by Imarticus Learning

The field of Machine learning is expanding fast nowadays with the application of smart algorithms being applied from apps to emails to as far as marketing campaigns. What this means is that machine learning or Artificial Intelligence is the new in-demand career option you can choose.
But being a new field comparatively, you may have many doubts and confusion as to how you can actually get yourself to adopt Machine learning as a career. Let’s ponder over some things you need to master to get your career in machine learning startup.

Understand the Field First

It is an obvious but important fact. Understanding the concept of machine learning and basic math behind it along with the alternative technology while also having hands-on experience with the technology is the key to dive into this field at first.

Covert Problems in Mathematics

Having a logical mind is imperative in machine learning. You need to be able to blend technology, analysis and math together in this field. Your focus on technology must be strong and you must possess curiosity along with openness toward business problems. The ability to pronounce a business problem into a mathematical one will take you long into the field only.

Background in Data Analysis

A background in data analysis is perfect for transitioning or getting into machine learning as a career. An analytical mindset is crucial for success in the field, which means one has to possess the ability to ponder over causes, consequences and discipline to search for the data and digging into it, understand the working and its consequences.

Gain Knowledge of The Industry First

Machine learning, like any other industry, possesses its own unique needs and goals. Therefore, the more you research and learn about your desired industry, the better you’ll do here. You have to study the basic and everyday working of the industry along with all the technicalities involved in it.

Where to Find Work as Machine Learning Expert

Job portals are a good way to find work in your starting days in machine learning. You can apply for a job in portals such as Indeed.com, Monster, Glassdoor, etc. You can sign up on some freelancing site (such as Upwork) too to get your starting assignment as a machine learning expert.

The Best Companies to Work For in The Field

Two types of companies can provide you machine learning job as present:  huge MNCs and established companies, or start-up businesses.  There are two basic markets at present for machine learning experts for you to tap on. First is the Cloud and the other is the logs, which allows companies or analytics to let customers create their own algorithms.
The large companies which dominate the data analysis and Machine learning field include Databricks and IBM Watson Analytics.  Google has also made forays into the AI recently while many of its partners are also looking for professionals to get their machine learning initiative started.

Related Post : What are The Skills You Need to Become a Machine Learning Engineer?

How Machine Learning Can Improve Customer Service

Posted on by Imarticus Learning

BPO means “business measure re-appropriating.” to put it plainly, it’s a business practice we see carried out when an association chooses to re-appropriate exercises like finance, HR, charging, and client support.

The best illustration of this is client support since we as a whole have encountered talking with somebody from an alternate nation when we’ve called a bank or objected to a Visa and required it settled.

We won’t invest any more energy examining BPO, yet our innovation discussion in this article will be centered around further developing client support. Presently, review an occurrence when you called your charge card organization. You were reasonably approached to squeeze 1 for English, press 2 for Spanish and afterward, a few alternatives were introduced before you at long last get a choice to press a number to converse with a genuine human.

Next came the check interaction where you needed to give your first and last name, then, at that point your date of birth, then, at that point your mysterious answer, or pin, or perhaps the last four digits of your federal retirement aide number. At last, a CSR (client support specialist) approves your personality and you have a chance to pose inquiries. Now, the client care specialist may have full admittance to your considered history and whatever other collaborations that you had with them before.

So what’s the job of AI in this?

Presently, envision a shrewd framework where you are consequently diverted to a savvy specialist (or a computerized specialist) who can say for sure that you are bringing in to converse with a client specialist since you were on the site or application searching for answers to a specific inquiry.

You even connected with the chatbot, yet your inquiry was not replied to. Your calling number and voice can be utilized to confirm your personality to look through your record as opposed to investing the energy to look into your data. There are machines behind the scene ingesting, handling, and examining this collaboration continuously and anticipating that you are going to call the client support.

AI (ML) takes the client contact point, tracks the action progressively, and predicts the following best activity dependent on client action. AI predicts client future necessities dependent on the set of experiences which results in up-selling and strategically pitching openings.

The framework even triggers hyper-customized notices to CSR to impart to the client while the client is as yet on the call like new items or administration offering since this client looked for that specific watchword before.

This is the only one-way organizations can utilize ML to further develop client support. Here are a couple of alternate ways you can use ML to further develop the client care insight:

• Shorten times to goal on your cases. Execute shrewd steering to the right line for people and furthermore use chatbots for those simpler, self-serve issues.

• Increase consumer loyalty by assisting clients with directing them to the best specialist. Then again, you can assist those specialists with being powerful by suggesting goals, articles or subjects relying upon the need of the client. What’s more, use ML to assist with surfacing significant client history to the client assistance agent.• Reduce cost by proactively messaging clients who seem as though they’re looking for things on your site.

• Perform main driver investigation. Attempt to mine information or investigate models to check whether you can — in view of models that can foresee something — dive into what is generally prescient and use it as an approach to work on an item or cycle.

So since we realize how to use ML in a client assistance setting, what does it truly take to construct a framework that uses ML?

As a matter of first importance, it takes shrewd individuals who follow cycles and use innovation to plan and fabricate savvy frameworks to give the best client experience conceivable. In light of my experience, the interaction assumes a key part and in this unique situation, I am discussing organizations focusing on computerized change by utilizing the most recent and arising innovations.

According to an innovation viewpoint, the excursion should begin with open source apparatuses and bundles with regards to planning your frameworks. The essential explanation behind using open source is a result of the wide scope of choices and that it helps minimize the expenses. Tensorflow, H2O.ai, and Microsoft Cognitive Toolkit are only a couple models.

Taking everything into account, connect business and innovation. Once, individuals, cycles, and stages are associated together, then, at that point driving ROI is simpler. A similar reality applies when endeavoring to further develop the client experience by utilizing AI.

Python Developer Salary in Terms of Job Roles

Posted on by Imarticus Learning

What is Python?

The second most liked Programming language in the world, Python is one of the widely used term in the web-development world.

Who are Python Developers?

The web-developers who design and code the software applications with the help of Python language are referred to as Python Developers.

Roles and responsibilities of Python Developers

 Python Developers as Data Scientists

Major businesses in today’s world require tools and skilled people for the data-related tasks such as data collection, data cleaning and processing.

Python Programming Course with Data ScienceData Scientists are the programmers who do these tasks for the organizations. Data Scientists gather a large quantity of data and convert it into a useful form, followed by recognizing data-analytics solutions for organizational growth.

Data Scientists encourage the data-driven approach in organizations to deal with complex business problems.

 Artificial Intelligence

In AI Python Developers create and implement the required Machine Language algorithms. They analyze the success and failure of the algorithm and rank them according to their performance for future use. Training and Retraining ML algorithms is one of the key tasks performed by Python Developers.

Salaries of Python Developers in India

The changing focus of the organizations on data-driven solutions is resulting in a manifold increase in the salaries of the Python Developers. In the coming years the demand for AI-skilled people will increase, and hence the salaries.

Major IT giants like Google, YouTube, Amazon etc. are adopting Python-driven systems and hence, manifold increases in the salaries of the Python Developers.

Python Programming Course with Data scienceThe entry level salary of a Data Scientist is approximately INR 500,000 per annum (Source: payscale.com) Data scientists with 5-9 years of work experience gets +100% more salary in India. With the experience and refinement of the skill, the salary of Data scientists shows exponential growth.

 An entry-level engineer who develops the ML Algorithms earns the average salary of approximately INR 700,000 annually. With further experience and refinement of the skills, the average salary of the Data Scientist shows exponential growth.

Factors affecting the Salaries of Python Developers

Job location: Considering the increasing demand of Python Developers, not only in India, the faster salary growth is visible in UK and in US.

Location

 Approximate Average Salary in INR

Gurgaon

 700,717

Bangalore

669,787

Delhi

600,000

Mumbai

579,728

Chennai

540,131

Hyderabad

 475,000

UK Python Developer annual salary

£67,000

US Python Developer annual salary

 $117,000

Experience:

Python Developer

Approximate Salaries in INR

Entry Level Python Developer

427,293

Med-Level Python Developer

 909,818

Experienced Python Developer

 1,150,000

Refined Skills:

Mere understanding of the Python is of no use, till is integrated with the problems and solutions. How one uses the well-known Python tools define the person’s skill set, which is a determiner of the salary.

Job Role:

Python Developer

Approximate Average Salary in INR

Data Scientists

700,000

ML Engineer

 670,000

DevOps Engineer

660,000

Software Engineer

500,000

Web Developer

 300,000

 

Python Programming Course with Data ScienceShould know more interesting things about Python programming training and Python career.

 

Top 3 Apache Spark Tutorials For Machine Learning Beginners!

Posted on by Imarticus Learning

Apache Spark is a well-known name in the machine learning and developer worlds. For those who are unfamiliar, it is a data processing platform with the capacity to process massive datasets. It can do so on one computer or across a network of systems and computing tools. Apache Spark also offers an intuitive API that reduces the amount of repetitive computing and processing work that developers would otherwise have to do manually.

Today, Apache Spark is one of the key data processing and computing software in the market. It’s user-friendly and it can also be used through whatever programming language you’re most comfortable with including Python, Java and R. Spark is open-source and truly intuitive in that is can be deployed for SQL, data streaming, machine learning and processing graphs. Displaying core knowledge of Apache Spark will earn you brownie points at any job interview.

To gain a headstart even before you begin full-fledged work in Apache Spark, here are some tutorials for beginners to sign up for.

  1. Taming Big Data with Apache Spark and Python (Udemy)

This best-selling course on Udemy has fast become a go-to for those looking to dive into Apache Spark. More than 47,000 students have enrolled to learn how to:

  • Understand Spark Streaming
  • Use RDD (Resilient Distributed Datasets) to process massive datasets across computers
  • Apply Spark SQL on structured data
  • Understand the GraphX library

Big data science and analysis is a hot skill these days and will continue to be in the coming future. The course gives you access to 15 practical examples of how Apache Spark was used by industry titans to solve organisation-level problems. It uses the Python programming language. However, those who wish to learn with Scala instead can choose a similar course from the same provider.

  1. Machine Learning with Apache Spark (Learn Apache Spark)

This multi-module course is tailored towards those with budget constraints or those who are unwilling to invest too much time, preferring instead to experiment. The modules are bite-sized and priced individually to benefit those just dipping their toes. The platform’s module on “Intro to Apache Spark” is currently free for those who want to get started. Students can then progress to any other module which catches their fancy or do it all in the order prescribed. Some topics you can expect to explore are:

  • Feature sets
  • Classification
  • Caching
  • Dataframes
  • Cluster architecture
  • Computing frameworks
  1. Spark Fundamentals (cognitiveclass.ai)

This Apache Spark tutorial is led by data scientists from IBM, is four hours long and is free to register for. The advantage of this course is that it has a distinctly IBM-oriented perspective which is great for those wishing to build a career in that company. You will also be exposed to IBM’s own services, including Watson Studio, such that you’re able to use both Spark and IBM’s platform with confidence. The self-paced course can be taken at any time and can also be audited multiple times. Some prerequisites to be able to take this course are an understanding of Big Data and Apache Hadoop as well as core knowledge of Linux operating systems.

The five modules that constitute the course cover, among other topics, the following:

  • The fundamentals of Apache Spark
  • Developing application architecture
  • RDD
  • Watson Studio
  • Initializing Spark through various programming languages
  • Using Spark libraries
  • Monitoring Spark with metrics

Conclusion

Apache Spark is leveraged by multi-national million-dollar corporations as well as small businesses and fresh startups. This is a testament to how user-friendly and flexible the framework is.

If you wish to enrol in a Machine Learning Course instead of short and snappy tutorials, many of them also offer an introduction to Apache Spark. Either way, adding Apache Spark to your resume is a definite step up!

What is Alpha Beta Pruning in Artificial Intelligence?

Posted on by Imarticus Learning

What is Artificial Intelligence?

Most of us are aware of the edge cutting technology i.e. Artificial Intelligence (AI). It is used to create machines that have their decision-making capability. They can learn from their work environment and can behave autonomously. in the initial stages, it is man-made but once it has learned and evolved, it can enhance itself.

For example, the University of California, Irvine developed an AI machine that could solve Rubik’s cube. The machine learns and trains itself through algorithms and now it can solve complicated Rubik’s cube in a fraction of a second. In this article, let us learn about Alpha Beta Pruning in AI.

What is Alpha Beta Pruning?

Before you learn about Alpha-Beta Pruning, one needs to know about the minimax algorithm. Minimax algorithm backtracks a scenario/game and finds the best move which will enhance the decision making or in terms of gaming, will maximize the chances of winning. It assumes that there is an opponent who is also trying to win, it tries to reduce the winning chances of the opponent and optimizing its steps to win.

Alpha Beta Pruning is an optimization technique that decreases the number of steps in the minimax algorithm. It helps in reducing the number of steps in searching/traversing. For example, if we are applying a minimax algorithm in a chess game, then Alpha Beta Pruning helps in finding those steps which will not result in winning, and then those steps need not be traversed.

The minimax algorithm prepares a search tree after backtracking, there are many nodes in this search tree. The redundant/useless nodes are eradicated with the help of Alpha-Beta Pruning. It helps in decreasing complexity and saves time. There are two main components in the minimax algorithm, first one is maximizer which tries to get the highest score and the minimizer does the opposite. Let us know about the two parameters ‘Alpha’ & ‘Beta’:

Alpha Parameter (α):

The best choice/decision found in the whole path of maximizer is called Alpha. Its initial value is (-∞). One can also say that the highest value along the path of maximizer is Alpha.

Beta Parameter (β):

The best choice/decision found in the path of minimizer is called Beta. It is the lowest value of all the values encountered in the path of the minimizer. The initial value of the Beta parameter is supposed to be (+∞).

 Note: Before Pruning one needs to check whether (α>=β). This is a necessary condition to run the Alpha Beta Pruning algorithm.

Why Alpha Beta Pruning is important?

There is no change in the result if we compare the outputs minimax algorithm and Alpha-Beta Pruning. Pruning helps in decreasing the number of steps thus making the algorithm faster and less complex.

Key points and terminologies in Alpha-Beta Pruning

  1. The child node is provided with the values of α & β.  While backtracking, the values of lower-order nodes are passed to the upper nodes in the search tree except for the child node.
  2. In some cases, the Alpha Beta Pruning algorithm fails to reduce the number of nodes. In such cases, more time is wasted because of α & β parameters and the number of steps comes out to be the same as the minimax algorithm.
  3. This scenario is called Worst Ordering. Ideal Ordering occurs when a lot of pruning happens and a lot of steps are decreased (especially on the left side of the tree).

Conclusion

AI is a budding technology and is expected to grow more. If you want to learn more about AI, then you can search for courses available online. One such good course is the PG program in Analytics & Artificial Intelligence in Imarticus Learning’s list of offered programs. This article was all about the Alpha Beta Pruning algorithm in AI. I hope it helps!

Solve Real-world Text Analytics Problems With NLP!

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Solve Real-world Text Analytics Problems With NLP!

Natural language processing (NLP) helps machines analyze text or other forms of input such as speech by emulating how the human brain processes languages like English, French, or Japanese. NLP consists of ‘natural language understanding’ and ‘natural language generation’ which help machines create a summary of the information or assist in taking part in conversations.

With the advent of natural language processing, services like Cortana, Siri, Alexa, and Google Assistant are finding it easier to analyze and respond to requests from users. This is opening up many new possibilities in human-machine interactions and helping improve existing systems and services.

In this article, we will cover how NLP is helping provide solutions for various requirements of text analytics in different sectors.

Significance of NLP in modern times

data analytics courses

NLP can analyze massive amounts of text-based data with consistency and accuracy. NLP courses help summarize key concepts from large unstructured complex texts. It also helps in deciphering or analyzing ambiguous statements or sentences. It can draw connections and also investigate deeper meanings behind seemingly normal data in the form of text.

With the massive amounts of randomized forms of textual data that is generated on a daily basis, automation is highly necessary for this field to analyze the large amounts of data from text efficiently and effectively. Ranging from text posted on social media to customer service, natural language processing is powering text analytics which is making life easier for both consumers and corporations. 

How text analytics along with NLP is helping businesses? 

Text analytics can be described as a process of analyzing a massive or specifically targeted volume of unstructured textual data and translating it into quantitative information to gain valuable insights through patterns and trends.

With the help of additional visualization of this data, text analytics allows corporations to understand the sentiments, deeper meaning, or compact information behind this data and helps them take data-backed or data-centric decisions for improved results through better performance or profit.

These companies collect massive amounts of unstructured textual data from sources like social media, e-mails platforms, chat services, and historic data from previous interactions or third parties. This could prove to be a challenge without the help of natural language processing which powers text analytics, helping analyze the massive amounts of data without the need to stop or for human interference. 

The same amount of data, being manually processed seems like an impossible, never-ending task. Manually processing even a tiny bit of the colossal amount of data that is generated daily would definitely take a lot of manpower. Hence, it is not cost-effective and would also lead to inaccuracy and duplication. This is where text analytics comes to the rescue.

With the help of text analytics, companies can excavate meaning and sentiments from unstructured textual data sourced from social media posts, content inside e-mails, chat services, and surveys or feedback. 

This helps businesses identify patterns and trends which lead to providing customers with improved experiences by analyzing service or product issues and customer expectations through market research and monitoring with text analytics.

Natural Language ProcessingHere are some real-world applications of text analytics and natural language processing:

Customer care service

Data generated from surveys, chats, and service tickets can help companies improve the quality of customer service by increasing efficiency and decreasing the time taken in resolving problems.

Illegal activity and fraud detection 

Text analytics helps in analyzing unstructured data from various internal or external sources to prevent fraud and warn governments or companies of illegal and fraudulent activities. 

Natural Language ProcessingSocial media analytics

Text analytics is being used by brands to analyze customer preferences and expectations through the extraction of sentiments and summarized opinions from textual data sourced from social media platforms like Facebook and Instagram. 

Text analytics and NLP are increasingly becoming more effective for companies to depend on and encouraging them to take more data-backed decisions. This need is making way for better, more accurate, and faster analytical tools and technologies in the future.

Data Science Job Opportunities Continue to Surge in 2022!

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Data science has revolutionized the functioning of almost all industries in the world today. The creation of data is the highest at the moment due to the widespread process of digitisation. Therefore data science tools and technological advancements are being deployed in order to push further productivity amidst all organizations.

With this, there is the provision of Big Data, Machine Learning, Data Analytics, Data Mining and Data Analysis thus creating large importance for this technological field.

All businesses and organizations require efficient and quick problem-solving methods. This is offered by data technology, having the ability to analyze and comprehend large sets of data in order to resolve a variety of problems in a fast-paced and accurate manner. This is a much more sought after a method as compared to the completely engineered solution.

The development of proficient machine language algorithms and a change of direction from analytics that were descriptive has resulted in driving progress. Predictive analytics and maintenance have slowly been gaining popularity amongst industries and this popularity only seems to be growing.

Data Science JobsThe demands for various data science services have been seeing a large surge all over the world as researchers for the market predict its magnification in the near future. Due to this increased demand, the path for various other talents and job aspirants is clearing. This would allow them to try their hand and work hard while in this genre of work. The vast number of technologies in relation to data are creating large opportunities for up and coming data professionals to seize.

With an estimated increase of over 1 lakh new job openings in the present year of 2020, which is a little more than a 60% increase from the previous year (2020), aspirants have a large number of openings to prove themselves with a data science career. Almost 70% out of these job opportunities are for budding professionals with experience less than or up to five years.

In a bid to remain in the fast-paced competition of today’s market and maintain relevancy, organizations, businesses and various other companies are taking up newly emerging technology. Due to a large amount of data that is being created, data technology and science is the answer to mining insights that are actionable for businesses.

There is thus a very large scope in this field for data science professionals set in the present year, 2022. This year has been the best year for Data science and furthering its opportunities.

Industries of energy, pharmacology, healthcare, media, retail, e-commerce, etc. are creating a large number of job opportunities in the field with average potential salaries going from 10 lakhs to even 14 lakhs per year.

The industry of data science had been previously (2022) facing a large shortage of skilled professionals which have increased in large numbers this year (2023).

By taking a data science course aspirants will be well equipped with all the necessary information in order to succeed in their future data science career.