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Tag: machine learning courses

How Companies Use Machine Learning

Posted on by Imarticus Learning
Reading Time: 2 minutes

How Companies Use Machine Learning

Machine Learning and data processing has changed drastically the way things work inenterprises and even our daily lives. Digital technology has been able to enablemachines with ML software and algorithms to process intelligently and unsupervised the large volumes of data generated. The advent of the internet and such limitless uninterrupted data processing has generated many an error-free gainful insight.

Businesses can now transform to the high-efficiency mode where profits increase by creative use of employee time in using the insights and forecasts provided by machine learning, data analytics, big data processing, and accurate predictive analysis.

What are companies using ML in?

Learning and Scanning data images, text and voice: Repetitive tasks and tasks that are labour-intensive are now a one-step zero-error machine process. Digitizing data has scored in the following areas.

  • Data entry, documentation and report generation: The way data is processed, the volumes of data available, used and predictive analysis of data analytics have impacted lives and businesses to upgrade and upskill for better efficiency and profits.
  • Image Interpretations: Complex insights are possible with accurate predictive insights which have huge ramifications in the film, media, health, banking, insurance sectors and more.
  • Previewing videos: Data previewing in video form can help to process in speeds far higher than humans could ever think of. They can also match the videos to preferences of people, match advertisements to these, edit and curate video footage in fractions of a second! The advertising, marketing, media, film, and video industry has been transformed forever. The revenues generated with accompanied efficiency and speed has led to collaborations of machines and humans in a positive manner.

Uncovering and forecasting insights: ML has truly transformed the way we function with computers and ML replacing routine, repetitive tasks. Notably, the following sectors have improved tremendously.

Monitoring Markets: Mining of big data can result in time-saving and provides lead time in relevant and urgent monitoring of opportunities. News channels, competing in the business world, taking corrective actions and strategising have become a matter of nanoseconds with ML.

  • Root cause analysis: This technique used in production lines can predict and forecast failures of tasks, identify the root cause of the issues, suggest strategy changes required and generate alerts in these conditions.
  • Predictive maintenance: This tool is most effective in its forecasting abilities and ensures there will be no downtime in functioning.
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  • Predictive modelling: ML has enabled matching customer profiles and preferences to products available and browsing history-making auto-suggestions a routine affair. The huge potential of generating through advertisements matched to such preferences can generate more efficiency and high revenues.

With the advent and use of ML in everything you do, there is an urgent need for collaborators who can tweak software, create new applications, use the predictive and forecasting alerts and insights gainfully to improve profits, efficiency and save time, effort and costs. It is still early days and the right time to upgrade and re-skill with machine learning courses that will enable smart and creative use of machine learning benefits mentioned above.

Big data Hadoop training courses are also required to help ML understand and use the mind-boggling quantities of data that is now usable. Without the will to effectively use data and the training needed to adapt you will be left far behind. The situation today is adapt, or stay behind! 

What Are The Advantages Of Business Competition With Deep Learning?

Posted on by Imarticus Learning

Reading Time: 2 minutesWe all produce data. Enterprises have their own data. But as the adage goes, the wise learn from the past. Today, machines, robots, and software are smart. Machine Learning has in the past decade transformed software to help machines learn unsupervised from data. Deep Learning is the subset which helps ML learn from data that is unstructured. Humans are limited by data.
ML process huge quantities of data and learns patterns and can thus give you recommendations on Facebook based on your browsing history and use or suggest interesting videos on YouTube on your smartphone. It is now time to use ML intelligently in your business enterprise or career and stay abreast with the latest upgrades or be left behind.

Advantages in Business:

Primarily three benefits accrue with Deep Learning.

  1. Time and Cost benefits: Most employees do the same repetitive job day in and day out. Neural networks have given artificial intelligence the brains to use data, learn both supervised and unsupervised from it and use it to perform such repetitive tasks. In terms of time saved the employees are now free to use their time on creative tasks. Money in hiring more employees to handle large data generated is saved. ML never sleeps or takes a holiday. With the potential to offer so much saving of time and money it is well worth the investment of ML.
  2. Quality scores with accurate results: Human emotions bias our results and output. ML, on the other hand, is error-free learning with no emotional bias. In processing data and for repeating tasks in a production line such errors can be costly. ML also needs no food, sleep or breaks. With highly accurate results that can be preset and traverses data with multi-variables and time constraints cutting across all departments and sources of data ML has the ability to improve quality and efficiency. The obvious outcomes are better organisations, speedy deliveries, accurate results, and high efficiency.
  3. Growth in jobs: ML needs humans to program them and to use the insights provided by them in newer applications. This definitely means more humans are required with an understanding of ML. But such human intervention and supervision need an in-depth knowledge of ML, data analytics, deep learning, and artificial intelligence.

    What Should You Do About It?

    If you are an employer, then it is time your employees were re-trained and learn how to use ML to the advantage of the enterprise creatively. Encourage employees to upgrade and up-skill with machine learning courses. This will offer them better prospects and pay packets because of increased efficiency.

    Why Do Machine Learning Courses In India?

    India today is an emerging hub of innovation with huge potential in terms of trained manpower and resources in training, expertise in software programming and high demand for good quality workers and software knowledge. Large enterprises need smaller businesses to get their tasks done and this, in turn, means job generation. If you have the right knowledge and the skill to use it your career will know no bounds. That’s why should know what is so trendy about Machine Learning courses is a step in the right direction for you!
    Growing your employees means business growth which becomes super-efficient and organised, offering better returns and results. Make your move today. It is a win-win situation for both the workers and the business.

7 Answers to the Most Frequently Asked Questions About Artificial Intelligence

Posted on by Imarticus Learning

Reading Time: 3 minutesOne of the most trending topics in the today’s world is Artificial Intelligence. From self-driving cars to Siri, the world is now overwhelmed with automation. Technically, Artificial Intelligence is seen as a robot possessing characteristics similar to humans. However, AI has a lot more to offer. IBM Watson or automated weapons, facial recognition or search algorithms, AI explores every aspect of human life.
Hearing so much of this much-hyped technology, it is obvious that a pool of doubts strikes your mind. From what to who and how you might be too curious to know what Al does and has to offer. Here, we outline 7 most of the frequently asked questions about Artificial Intelligence.
Questions:

  1. Why is the need to employ artificial intelligence?

 Ans: Artificial Intelligence is an abode of automation. Till date, programmers required to hardcode the instructions in files to have the robots working. But, with the advent of machine learning & Artificial Intelligence courses, IT Industry has taken a steep turn. Now, we expect the programs to learn rather than simply follow what it has been coded with. Myriads of tasks that seek human intelligence are effectively automated with Artificial Intelligence. It is simply a pool a data. The more, the merrier. Health care, education, agriculture, and business are few industries that have readily adopted this new technology and driving benefits too. Cutting unnecessary human labor by replicating them with AI made machines. Also, jobs that pose threat to human life can be efficiently done with the help of AI.

  1. Why do you need to study Artificial Intelligence?

 Ans: Before we head towards the prime need to study AI, let’s see it’s application. Siri or Google assistant are not just fun but cuts downtime of typing while surfing something. Playing chess with a virtual opponent seems amazing. Agree? Do you have an idea that Google translate employs AI or the spam blockers incorporating AI?
We all use it but are not aware of it. Studying AI would not just open up a pool of job opportunities for you, but on s lung run turn you more subjective towards things you notice or witness. You would be able to link technologies better. AI is such a subject that is ever evolving and hitting a job in such a domain would change your life.

  1. How can we apply AI?

 Ans: In order to use AI, you don’t need to be a programmer or work in any IT industry. We are presently using products of AI and they have genuinely made our life simpler and easier.

  • Siri: The smart assistants are used almost every day. Though they are a little slow, yet it is expected by 2025, they will gradually occupy the business world.
  • Chatbots: The most effective application of Artificial Intelligence is learning ways to build chatbots. Currently, chatbots are based on rules and do not employ AI. So, the near future would definitely look forward to the implementation of AI in the creation of Chatbots.
  1. What kinds of jobs are related to AI?

 Ans: There induction of AI has opened doors for Myriads of job opportunities. What we lack is the resources to fulfill the need. Few jobs directly related to the field of AI include:

  • Data Scientist
  • Research Scientist
  • Machine Learning Experts.
  • Deep Learning Experts
  • Software Engineers.

In the future, there would be a need for chat bots designer, business strategy consultant and many more opportunities for a job.

  1. Name some powerful agencies inducing Artificial Intelligence?

 Ans: Seeing such a massive growth in the field of AI, almost all companies are now thriving to rank one in the underlying field. However, the major ones include:

  • Google
  • Amazon
  • Microsoft
  • Facebook
  • IBM
  • Apple

Google leads all of the above mentioned and apple being the weakest. Amazon Alexa software makes it rank in after Google.

  1. What are the benefits of AI technology?

 Ans: AI is expected to change the life of all across the globe dramatically. It could be direct or indirect but the benefits are huge. Some of the key benefits are listed below.

  • AI is expected to facilitate better life reducing poverty.
  • Dangerous tasks can be efficiently performed by AI.
  • AI would automate vehicles, thereby easing travel.
  • AI would create a pool of opportunities for business professional.
  1. Would AI be a threat?

 Ans: This has a twofold answer. Where few consider it as a threat to eliminating the need for human sole believe it would create job opportunities. Some consider to would ease life, few think it would create endless issues. Now, what you believe depends on you. However, everything is a threat is over-utilized. Agree?

Household Electricity Consumption – Machine Learning Algorithm

Posted on by Imarticus Learning

Reading Time: 2 minutesPower supply, generation, and its billing generate a huge amount of data. ML actually makes it possible to learn from this data and use an algorithm to accurately predict future occurrences like volumes of load and its demand, snag identification, efficiency and power loss reduction, problems and logistics involved in metering and billing and everything in between from power generation to its billing and beyond.
Machine learning courses in India could teach you how to understand ML and data analytics, so you aid ML to perform at its best in predicting outcomes. The Algorithm in ML for household electricity consumption works on data drawn from smart meters, solar panels, and data regarding the usage of electricity at different times of the day.
This huge data comprises the multi-variable time-series, and the algorithm can successfully predict future consumption. In real terms, the ML algorithm can predict such information as to help make the power generation and supply system more efficient.
Obviously, there are many steps involved in helping the machine take data in its raw multivariate form and enabling it to arrive at the future consumption prediction. This is where Machine learning courses come in handy. You can learn the techniques of ML involving predictive strategies like the direct methods and the recursive ones.
A good idea is to also incorporate learning of Big Data Hadoop training courses that can help one understand strategies, working of ML and data analytics. The logic of the process of algorithm development would be developing

  • The framework development for evaluation of non- linear, linear, and ML ensemble algorithms.
  • Evaluation of ML as it uses the strategy of forecasting the time-series both by the direct daily method and the recursive method.

Again such processes involve

  1. Describing the problem.
  2. Preparing and loading the data set.
  3. Evaluating the model.
  4. Recursive forecasting.
  5. Multi-Step direct forecasting.

Through highly accurate predictions ML helps the algorithm to plan future power generation, reduce transmission losses, tweak the metering, billing and collection systems and so much more. Once you master such algorithms, ML and data analytics, the scope of applying ML to various and everyday issues on a real-time basis, open the wide world of opportunity and good remuneration to you.
Yes, ML and data analytics use Python framework which has immense scope for progress basically because it can predict the outcomes of simple and complex tasks, single and multi-variate tasks, and even makes single and complex predictions by learning from the data, filling in the missing values, creating new values and so on. And to learn an ML course is essential. Start today and soon you will be able to master such tasks quite easily.

Reference:
https://machinelearningmastery.com/multi-step-time-series-forecasting-with-machine-learning-models-for-household-electricity-consumption/

Developing ML Models in Multivariate, Multi-Step Forecasting of Air Pollution Time-Series

Posted on by Imarticus Learning

Reading Time: 2 minutes 

Machine Learning Courses in India

The ML algorithms can be applied forecast weather and air pollution for the subsequent 3-days. This is challenging because of the need to accurately predict across multivariate input with noisy dependencies that are complex and multi-step, multi-time input data while forecasting and performing the same prediction across many sites.
‘Air Quality Prediction’ or the Global Hackathon EMC dataset provides weather conditions across various sites and needs accurate predictions of measurement of air-quality to provide a 3-day weather forecast.

The Need for Machine Learning

The primary benefits of Machine learning courses are that with them you can learn to operate the tools from a Python open source library and gain expertise in

  • Providing for missing values, transforming the time-series data and successfully create models that are worked by the trained and supervised-learning algorithms.
  • Evaluate and develop both linear and nonlinear algorithms to handle the multivariate, multi-step multi-time series forecast.

The Need for Data Analytics

A real-time problem when working with this dataset is that of missing values and multiple variables drawn from many physical sites. This means integrating and helping the ML algorithm predict and forecast accurately. You will need data analytical skills to achieve this.
The Big Data Hadoop training courses can provide you with skills and learning in

  • Imputing values that are missing, helping algorithms with supervised learning by transforming the input data time-series and creating requisite number of models using the data and the algorithm.
  • How to evaluate and develop suites of nonlinear and linear algorithms for multiple-stepped forecasting of a time series.

The Entire Process

Developing this algorithm and making it successfully predict with accuracy the weather forecast over the next 72 hours in an environment that has multiple variables, multiple data sets, some missing data, lots of ways to develop the code on the Python platform has nine parts.
Namely,

  • Description of the problem.
  • Evaluation of models.
  • ML Model creation.
  • Data preparation using ML.
  • Creating a Test Harness for model evaluation.
  • Linear Algorithms evaluation.
  • Nonlinear Algorithms evaluation.
  • Lag Size tuning.

Benefits of ML, in this case, are handling features that are irrelevant, the ability to support between variables noise and noisy features, and the ability to support inter-variable relationships. ML forecasting provides both recursive and direct forecasts.
Benefits of data analytics relevant here are in preparing data, feature engineering, lag-tuning the meteorological variables, creating models across many sites, and tuning the algorithm itself.
Enrol in the most suitable course that will help you learn how to develop the algorithm for air pollution forecasting.
Reference:
https://machinelearningmastery.com/how-to-develop-machine-learning-models-for-multivariate-multi-step-air-pollution-time-series-forecasting/

How Machine Learning Helps in Psychiatric Epidemiology

Posted on by Imarticus Learning

Reading Time: 2 minutesIn India, where, as per medical surveys, every sixth child needs medical supervision for health conditions, schizophrenia is often left untreated and diagnosed. It can cause lifelong trauma and is a severely disabling illness with hallucinations, cognitive impairments, and delusions. Early diagnosis and the use of anti psychotic drugs are imperative. Predicting the course of the illness and treating it with a suitable drug is often by trial-and-error manual offline learning. That’s where the use of ML and AI in the epidemiology use in psychiatric illnesses holds immense potential and scope for growth.
Especially in our country with expensive treatment, lack of medical facilities dedicated to such mental illnesses and a huge population being rural poor being real deterrents.

ML In Predictive Analysis of Responses and Treatment

Improved MRI tools enable visualization of the smallest brain structures like sub fields in the hippo campus. The study is crucial in the treatment of psychiatric sicknesses like schizophrenia where the early recognition and assessment of the thickening or volumetric changes in these fields detected by neuroimaging can be used in morphometry and predicting cognitive declines in the pathology of the hippo campus.
AI is used in the diagnosis of schizophrenia reporting recent onset and not using treatment also known as (first-episode drug-naïve) FEDN. ML and suitable architectural frameworks help researchers evaluate and interpret these MRI scans and brain signals of the hippo campus.
The correlations of information between other cortical regions and the signals of the superior-temporal cortex got from resting-state MRIs are used by the algorithm to identify schizophrenia patients and the response to specific antipsychotic treatments very accurately.
You can now learn all about such ML, big data analytics and AI developments and uses through machine learning courses.

3d-Cnn Spatial Image-Classification

3D-CNN are convoluted neural networks used for 3D modelling, and LiDAR (light detection ranging) data classification under supervision. Cranial Imaging, occurrences of neural events and surveillance are now computer aided and should necessarily be part of Big data Hadoop training courses.

Machine Learning and Predictive Analytics

The best example is of the Alberta University study using an ML algorithm, and MRI visualized images of treated, diagnosed, untreated and healthy persons. Hippo-campus sub field volumes were used to predict responses using regression of support-vectors. The SVR-input was normalised to normal feed levels and split randomly in the module for cross-validation and datasets training in sci-kit. The prediction model and its features were accurately calculated using an inbuilt datasets training-LOOCV.
Machine learning courses in Indiainspired by the technological advancements and uses in psychiatric epidemiology are quickly adopting new content in an innovative move to use ML for predictive analysis.

Build Your Own AI Applications in a Neural Network

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Reading Time: 2 minutesToday Big Data, Deep Learning, and Data Analytics are widely applied to build neural networks in almost all data-intensive industries. Machine learning courses in India offers such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
The study on jobs in Data Sciences says that core skills in Python are preferred by recruiters and is requisite for jobs in data analytics. The challenge lies in formulating a plan to study Python and the need of a specialist to help understand the technical and practical aspects of this cutting edge technology.

Why do a Specialization Course for Beginners?

Not all are blessed with being able to learn, update knowledge and be practically adept with the Python platform. It requires a comprehensive knowledge of machine learning, understanding of data handling, visualization techniques, AI deep learning, statistical modelling and being able to use your expertise on real-time practical examples of data sets from various industries.
Machine learning courses and case studies on Python platform are conducted in flexible learn-at-your-own-pace sessions in modes like instructor-led classroom sessions at select locations, virtual online classes led by certified trainers or even video sessions with mentoring at pre-determined convenient times.
One can do separate modules or certificate Big data Hadoop training courses with Python to understand data science analytics and then opt for modules using AI for deep learning with Python or opt for a dual specialization by doing the beginners course and courses covering AI and Deep Learning with Python. The areas of Deep Learning and AI both require prior knowledge of Deep Learning, Machine Learning, and data analytics with Python.
An example of one such course is the AnalytixLabs starter classes in Gurugram and Bangalore as a speedy boot-camp followed by a package of two courses in AI Deep Learning with Python and the Data Science with Python. The prerequisites are knowledge of at least one OOPs language and familiarity with Python. Their 36 classes, 250-hour course offers dual specialisations, and 110 hours of live training using multiple libraries in Python.
Just ensure you choose the right course to allow your career prospects to advance and allows further learning in Python-associated specialised subjects.

How AIML Can Facilitate a Holistic Digital Transformation of SMEs

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Reading Time: 2 minutesUsing AI digitised mobility-efficient business management empowers SMEs to expand to any region globally with literally no associated monetary or infrastructural deterrents. Especially in processes like strategy-based planned sales, financial management, supply chain logistics, and marketing management where the focus should rightly be on the operational aspects rather than offline management of these which reduce enterprise efficiency.
Notable benefits of machine learning courses in India are learning better workflow management, enabling operational management to reach out, service and retain the all-important customer base. Increased cost-reduction, increased satisfaction levels of customers, doing away with time-consuming redundant offline process management and the obvious maximising of profit margins and enterprise efficiency result.

Role of Machine Learning-ML and AI

Issues are unique to every enterprise. Solutions should emerge from the workflow and be need-specific to the enterprise and its segment. Automating the logistics of the supply chain processes and sales can be optimised by ML and AI to build solutions meeting the needs and precise requirements of any business or industry with a high level of precision and customisation through the proper use of the huge data repository available with them.

Data and Challenges

Data is the backbone of automation and readily available with SME’s. Greater volumes in the database ensure tweaking for quickening and process efficiency. Big data Hadoop training courses help streamlining data, identifying and eliminating unnecessary recurrent processes and automating the process for fixed quicker and efficient outcomes is what ML, data analytics and AI intuitive combinations does when customizing processes and big data.
This indirectly frees-up the crucial time-component spent on customer interactions. ML and AI bring huge benefits in pattern recognition and predictive analysis. Their use helps deliver effective business solutions with quick outcomes by identifying and automating recurring procedures and patterns. Thus the digitization of marketing and sales drive profit and efficiency in the enterprise.

Customer Service Paradigms

In today’s scenario the pervasive use of the internet, use of digital tools, mobile apps and smart-phones create a huge database of young consumers under-35, who use and prefer digital methods to offline methods. Gainful insights are provided through their feedback, need for value-enhanced solutions, customer interaction and resolutions for customer satisfaction.
The success of SME’s depends on adapting and catering to this sector which forms nearly two-thirds of the total Indian population. Many shy away from building a digital infrastructure citing prohibitive costs involved. But, as per digital customers and a study by Google-KPMG, SMBs and SMEs have the potential to grow twice as fast with the adaptation of ML and AI.
Do we need to say anything more for machine learning courses?

Facts on Machine Learning and Statistics

Posted on by Imarticus Learning

Reading Time: 2 minutesAll machine learning courses in India need proficiency in statistics. However ML is not only statistics but definitely draws inspiration from analysis of statistics. This is so because data is their common factor. An ML-engineer though must and should have proficiency in statistics, while an ML-expert needs to only have sufficient knowledge of basic statistical techniques and data management. Let’s look into why this is so.

Overlaps of Machine Learning and Statistics

Machine learning courses of today borrow concepts like data analysis and statistical modelling to arrive at predictive models for ML. Machine Learning is a branch of computer science while statistics deals with the analysis of statistics in pure mathematics. However, they are interdependent mathematical applications both dealing with the analysis of data, data models, and problem-solving.
It goes without saying that statistics is the older sibling and yet today even statisticians use ML to achieve its end results with Big Data and for Predictive Analysis. Similarly, ML draws on statistical analysis though its aim is entirely different. That’s why Big Data Hadoop training courses also need knowledge of statistics and database management.
Mostly the overlap and confusion occur because both use algorithms and data to predict the end results. However, it is incorrect to equate the two, which are separate advanced fields, in two different branches. They are at best complementary interdependent fields which can aid each other much like siblings often do. Two separate individuals, completely different, in one environment, and with individual destinations. Sure they walk the same path at times!

Clearing the Confusion

Statistics uses a model with defined parameters fitting the data tested through classification and regression techniques to account for clustering and density estimation, to provide the best inference. ML works with networks, graphs and bar charts learning from general data through assigned weights using unsupervised learning techniques to give an accurate prediction of outcomes.
Looking very closely into the two one will notice that ML has no set rules, equations, parameters, variables or assumptions. It learns from the data input and provides a predictive outcome. In statistics, you get an inference unique to a small data set with fixed variables and based on strict regression and classification techniques of mathematical equations. Though older, statistics is pure math. ML is a carefree youngster, which uses and learns from past data, has no limit to data used or variables present and works with algorithms that govern data to give an accurate predictive outcome.
An ML Engineer and Statistician may have areas where their jobs overlap. They share a common path through the use of modelling and data and then branch out to their own destinations. Truly they are complementary in nature bring out the best in the other and helping each other achieve individual end results.

How Machine Learning Is Saving The Indian Vernacular ?

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Reading Time: 3 minutesIn a nation riddled with countless cultures, unending dialects and infinite separations, the term ‘melting pot’ comes to mind. It’s common for the typical Indian being confused with the local tongues when treading into unfamiliar territories.
Fortunately for the millions of Indians beguiled by such problems, machine learning courses and a number of data science tools is proving to be a much-needed relief for preserving and keeping those languages intact.

Connecting Data To Language

Big Data

This has significantly boosted the outlook for interdisciplinary research that has allowed researchers across the country to link the aspects of linguistics and fragment all dialects to a condensed format that can be edited easily.Until now, several companies have taken to using an aggregator system to create a platform that translates the language into any other without sacrificing minor details. Several years ago, a research project under the name Technology Development for Indian Language was created by the government to scrape all the major Indian languages for data science purposes.

  • One such platform that has been making strides is the e-Bhasha platform that is making content available for citizens in their language. It was created as a big data project in 2015 and has become a starting point for many linguistic researchers.
  • As the number of internet users in India grew more than 28 per cent and is expected to be a $6.2 billion industry per year, international groups are jumping on the bandwagon to appeal to the common man.

Playing With The Locals

Seeing the enormous benefits of tapping into local consumers, big groups like Google set out to create the Google Brain which is essentially an extensive neural network to develop human language from the get-go.

  • Aspects of this have been incorporated into Google Assistant as well, having translated content from more than 500 million monthly users and 140 billion words per day in as many 158 languages.
  • The craze began by the year 2013 when e-commerce was still taking root in the country and was challenged by the numerous languages that consumers had in the country.
  • Websites like Flipkart and Snapdeal dealt with local language content for mobile websites as far back as 2015.
  • Reports suggest that Marathi, Gujarati, Tamil, Punjabi and Malayalam represented over 75 per cent of searches on Google in the very same languages. What’s even more interesting is that more than 73% of people surveyed are willing to go completely digital if the system communicates in their own language.   
  • Facebook has raised the number of Indian languages for posting to almost 12 but still lacks regional pages that use the same kind.
  • Small firms in India are collecting as much textual Corpus for languages available using translation services like Reverie, Process9 and IndusOS.  

The Technology Used

  • Most companies would confess to the use of neural networks for developing such programs, but the primary machines behind such global endeavors has been some rather sophisticated algorithms.
  • The newest additions to the industry happen to be some enhanced versions of the Hadoop MapReduce extension. A significant feature of the software is the ability to find linguistic linkers between similar words and compound phrases which makes translations more concrete. Some stellar packaged additions to the SPSS Modeler system too have taken place that is helping companies handle large corpuses.
  • At the same time, marketing groups are using modified techniques to feed invoice data collected from average consumers which are being sent into what’s being called a ‘global corpus data set.’
  • Likewise, teams across the country in data collection firms are hiring data collection engineers to converse and accumulate conversational audio recordings both in rural and urban areas.
  • The main subject remains heavily invested in cross-directional neural networks many of which are using data analysis tools and machine learning tools like Tensor Flow from Google and IBM Watson.