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Tag: Artificial Intelligence

Linear Regression and Its Applications in Machine Learning!

Posted on by Imarticus Learning
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Machine Learning needs to be supervised for the computers to effectively and efficiently utilise their time and efforts. One of the top ways to do it is through linear regression and here’s how.

Even the most deligent managers can make mistakes in organisations. But today, we live in a world where automation powers most industries, thereby reducing cost, increasing efficiency, and eliminating human error. The rising application of machine learning and artificial intelligence dominates this. So, what gives machines the ability to learn and understand large volumes of data? It is through the learning methodologies such as linear regression with the help of a dedicated data science course. 

So, what is linear regression? Simply put, machines must be supervised to effectively learn new things. Linear regression is a machine learning algorithm that enables this. Machines’ biggest ability is learning about problems and executing solutions seamlessly. This greatly reduces and eliminates human error.

It is also used to find the relationship between forecasting and variables. A task is performed based on a dependable variable by analyzing the impact of an independent variable on it. Those proficient in programming software such as Python, C can sci-kit learn the library to import the linear regression model or create their custom algorithm before applying it to the machines. This means that it is highly customisable and easy to learn. Organizations worldwide are heavily investing in linear regression training for their employees to prepare the workforce for the future.

The top benefits of linear regression in machine learning are as follows.

Forecasting

A top advantage of using a linear regression model in machine learning is the ability to forecast trends and make feasible predictions. Data scientists can use these predictions and make further deductions based on machine learning. It is quick, efficient, and accurate. This is predominantly since machines process large volumes of data and there is minimum human intervention. Once the algorithm is established, the process of learning becomes simplified.

Beneficial to small businesses

By altering one or two variables, machines can understand the impact on sales. Since deploying linear regression is cost-effective, it is greatly advantageous to small businesses since short- and long-term forecasts can be made for sales. Small businesses can plan their resources well and create a growth trajectory. They will also understand the market and its preferences and learn about supply and demand.

Preparing Strategies

Since machine learning enables prediction, one of the biggest advantages of a linear regression model is the ability to prepare a strategy for a given situation well in advance and analyse various outcomes. Meaningful information can be derived from the forecasting regression model, helping companies plan strategically and make executive decisions.

Conclusion

Linear regression is one of the most common machine learning processes in the world and it helps prepare businesses in a volatile and dynamic environment. At Imarticus Learning we have a dedicated data science course for all the aspiring data scientists, data analysts like you.

Frequently Asked Questions

Why should I go for a data science course?

The field of data science has the potential to enhance our lifestyle and professional endeavours, empowering individuals to make more informed decisions, tackle complex problems, uncover innovative breakthroughs, and confront some of society’s most critical challenges. A career in data science positions you as an active contributor to this transformative journey, where your skills can play a pivotal role in shaping a better future.

What is a data science course in general?

Data science encompasses studying and analysing extensive datasets through contemporary tools and methodologies, aiming to unveil concealed patterns, extract meaningful insights, and facilitate informed business decision-making. Intricate machine learning algorithms are leveraged to construct predictive models within this domain, showcasing the dynamic intersection of data exploration and advanced computational techniques.

What is the salary in a data science course?

In India, the salary for Data Scientists spans from ₹3.9 Lakhs to ₹27.9 Lakhs, with an average annual income of ₹14.3 Lakhs. These salary estimates are derived from the latest data, considering inputs from 38.9k individuals working in Data Science.

How Do You Start Applying Deep Learning For My Problems?

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Deep Learning helps machine learn by example via modern architectures like Neural Networks. A deep algorithm processes the input data using multiple linear or non-linear transformations before generating the output.
As the concept and applications of Deep Learning are becoming popular, many frameworks have been designed to facilitate the modeling process. Students going for Deep Learning, Machine Learning course in India often face the challenge of choosing a suitable framework.
Machine Learning Course
Following list aims to help students understand the available frameworks in-order to make an informed choice about, which Deep Learning course they want to take.
1.    TensorFlow 
TensorFlow by Google is considered to be the best Deep Learning framework, especially for beginners. TensorFlow offers a flexible architecture that enabled many tech giants to embrace it on a scale; for example Airbus, Twitter, IBM, etc. It supports Python, C++, and R to create models and libraries. A Tensor Board is used for visualization of network modeling and performance. While for rapid development and deployment of new algorithms, Google offers TensorFlow which retains the same server architecture and APIs.
2.    Caffe 
Supported with interfaces like C, C++, Python, MATLAB, in addition to the Command Line Interface, Caffe is famous for its speed. The biggest perk of Caffe comes with its C++ libraries that allow access to the ‘Caffe Model Zoo’, a repository containing pre-trained and ready to use networks of almost every kind. Companies like Facebook and Pinterest use Caffe for maximum performance. Caffe is very efficient when it comes to computer vision and image processing, but it is not an attractive choice for sequence modeling and Recurrent Neural Networks (RNN).
3.    The Microsoft Cognitive Toolkit/CNTK
Microsoft offers Cognitive Toolkit (CNTK) an open source Deep Learning framework for creating and training Deep Learning models. CNTK specializes in creating efficient RNN and Convoluted Neural Networks (CNN) alongside image, speech, and text-based data training. It is also supported by interfaces like Python, C++ and the Command Line Interface just like Caffe. However, CNTK’s capability on mobile is limited due to lack of support on ARM architecture.
4.    Torch/PyTorch
Facebook, Twitter and Google etc have actively adopted a Lua based Deep Learning framework PyTorch. PyTorch employs CUDA along with C/C++ libraries for processing. The entire deep modeling process is simpler and transparent given PyTorch framework’s architectural style and its support for Python.
5.    MXNet
MXNet is a Deep Learning framework supported by Python, R, C++, Julia, and Scala. This allows users to train their Deep Learning models with a variety of common Machine Learning languages. Along with RNN and CNN, it also supports Long Short-Term Memory (LTSM) networks. MXNet is a scalable framework making it valuable to enterprises like Amazon, which uses MXNet as its reference library for Deep Learning.
6.    Chainer
Designed on “The define by run” strategy Chainer is a very powerful and dynamic Python based Deep Learning framework in use today. Supporting both CUDA and multi GPU computation, Chainer is used primarily for sentiment analysis speech recognition etc. using RNN and CNN.
7.    Keras
Keras is a minimalist neural network library, which is lightweight and very easy to use while stocking multiple layers to build Deep Learning models. Keras was designed for quick experimentation of models to be run on TensorFlow or Theano. It is primarily used for classification, tagging, text generation and summarization, speech recognition, etc.
8.    Deeplearning4j
Developed in Java and Scala, Deeplearning4j provides parallel training, micro-service architecture adaption, along with distributed CPUs and GPUs. It uses map reduce to train the network like CNN, RNN, Recursive Neural Tensor Network (RNTN) and LTSM.
There are many Deep Learning, Machine Learning courses in India offering training on a variety of frameworks. For beginners, a Python-based framework like TensorFlow or Chainer would be more appropriate. For seasoned programmers, Java and C++ based frameworks would provide better choices for micro-management.

NLP in Insurance Trends and Current Application

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Today the insurance industry is on the disrupt cusp having embraced NLP, text analysis and AI just like the customer-service and legal industries. The large volumes of data generated by insurance companies with their various products, a large number of marketing channels, a massive customer database, and a spread of market over diverse geographies is astounding. This data has of recent been leveraged to provide meaningful trends, data and insights that are transforming, simplifying and improving business in areas like claims, customer-service, product planning and management, marketing, pricing and everything in between.
Trends detected:
The Everest Company reports that analytics tools from third-party vendors are anticipated to grow four-fold by 2020. The value of the NLP market globally will be a whopping 16 billion$ by 2021 and tech titans like Salesforce, Google, Intel, Yahoo, and Apple already a large part of the investors.
Benefits of NLP to the Insurance industry:
Some of the accruing benefits are

  • Meaningful data streamlining to the proper agent or department immaterial of geographical location is now a snap.
  • Decisioning in various departments and by the agents is enabled by ensuring timely accurate and meaningful data helps them plan better while improving the C-Sat scores and user experiences.
  • SLA delivery and response times are reduced improving customer services and their experiences.
  • Fraudulent, multiple claims and account-activity can be effectively monitored and detected at the earliest.

The following segments of the Insurance chain benefit greatly

  • Policy underwriting, maintaining and actuarial
  • Relationship management of channels, clients, claims, finance, and HR.
  • Security, fraud and corporate management.

Challenge areas and improvements seen:
Text analysis and NLP is the new buzzword with virtual assistants, chat-bots and such are replacing the personal touch and face-to-face interaction. This has helped the market grow as it reaches out to the masses and improves response times of queries, policy issue times, generation of reports and receipts and more that mean better customer-service and experiences.
Enterprise data access across geographies is a click away with adaption to NLP. Health data, customer profiling, cashless treatment facilities, smart recommendations of policies and such are examples of the betterments seen in the insurance sector brought about by data analytics, NLP and conversational interfaces like Google, and smart use of data to grow the business.
Channel management is another area where proper allocation and tracking of the various channels have improved by digitization, use of text analysis, and NLP across agents, digital channels, direct sales and brokers involved. Better products based on customer preferences, insights into improving marketing channels, training of agents, workforce allocation, policy servicing, and many more areas have changed and benefitted.
Customer retention was a huge challenge that has improved considerably with technological adaptations. Faster claim analysis and use of captured data for verification have been a contributive factor. Quicker underwriting, informed actuarial practices, better policy management, elimination of large workforces and insurance jargon, reduced labour costs, usage of data in daily transactions and better tracking have been some of the huge payouts the insurance industry benefits from through such embracing of technology.
Fraud detection and multiple claimants went undetected for long and are almost 10% of the European claims according to Insurance Europe. That has changed dramatically now with technology to prevent frauds and cyber security using the latest blockchain technology with AI, NLP and text analysis.
In parting data which is effectively democratized, analyzed and used can actually improve business value and customer retention through better experiences. The NLP and technology of text analysis are responsible for the disrupt that is present in the insurance sector.

10 Interesting Facts About Artificial Intelligence!

Posted on by Imarticus Learning
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Artificial Intelligence has received a lot of focus and attention in the last couple of years. There has been a boom in the innovations that have artificial intelligence at its base. Obviously, the internet has played a crucial role in the development of artificial intelligence-enabled services.

Machine learning essentially an artificial intelligence technique, has been stirring new developments by creating new algorithms that mimic or support human behavior or decision-making capabilities, which are already in use, like Apple’s Siri, or the email servers which eliminate junk or spam emails. You can also see the use of machine learning in e-commerce websites that use it to personalize the search or use of the web experience of their customers.

It is interesting to comprehend the capabilities of machines. Very soon machines will have the capability to perform advanced cognitive functions, processing language, human emotions, the machines will be proficient in learning, planning, or performing a task as intelligent systems.

There is also a definite possibility that the tasks performed will be or can be more accurate than humans, thus artificial intelligence can boost productivity and accuracy, and impact economic growth. Imagine the impact it can have on medical procedures, the continued support it could lend to the disabled, increasing their life expectancy.

Artificial intelligence is a technology that can improve the world for the better, however, it also comes along with some challenges such as machine accountability, security, displacement of human workers, etc.


But right now before the possible alarming impact of artificial intelligence, we could in the today, the now, enjoy learning about some interesting facts.

 

Interesting Facts About Artificial Intelligence

  1. It is interesting to note that research on artificial intelligence is not only a few years ago, but the inception of AI also goes back to the 1950s. Alan Turning is coined as the father of AI, back in the day he invested a test based on natural language conversation with a machine.
  2. Did you know that a lot of video games that engage humans over time are based on a technique of artificial intelligence and is called Expert System? This technique is knowledge-based and can imitate areas of intelligent behavior, with a goal to mimic the human ability of senses, perception, and reasoning.
  3. Autonomous vehicles are no longer a thing of the far future. The knight rider might actually become a reality in as close as the next 2-3years or less. These cars are based on artificial intelligence to recognize the driving conditions and adapt the behavior. These cars are in the test phase, already developed and almost ready to hit the road.
  4. There is a race that is warming up between social media corporations over perfecting the use of artificial intelligence to enhance the customer experience. Facebook and Twitter are two companies essentially applying AI to match relevant content to the people. Leading this race is Google, coming across as one of the most preferred and reliable search engines.
  5. IBM has created a supercomputer based on AI, called Watson. One of the major challenges of creating Watson was the programming that needed to be done so that it could understand questions in most of the common languages and the ability to attend to those questions in real-time. The development is such that currently Watson is not only applied in various industries but was recently successful in teaching people how to cook.
  6. Sony created a robotic dog called Aibo, one of its first toys that could be bought and played with. It could express emotions and could also recognize its owner. This was the first of its kind, however, today you will find more expensive and evolved versions of the same.
  7. At the rate at which Artificial Intelligence is being adopted in various areas of our lives, it is predicted that it will replace 16% of our jobs over the next decade.
  1. Artificial Intelligence Training CoursesIt is a fact that with increased intelligence and ability to perform tasks with accuracy, over the next few years it is predicted that close to three million workers will be reporting to or will be supervised by “Robot-bosses”.

    With Machine learning and language recognition, it is no surprise that 85% of telephonic customer service jobs will be performed by computers and will not need human interaction.  By the dawn of 2020, it will be possible for all customer digital assistant to recognize people by face and voice.

Organizations and private sectors have recognized the opportunity that AI investments can have on the future of their businesses. Hence have set up major investments in the development of the same.

Finally, one must remember the anticipated impact of AI is on calculated assumptions and predictions.
However, one thing is clear, that AI in the future will impact the internet, its citizens, and economies.

Read More 
The Promises of Artificial Intelligence: Introduction

AI for IT Services Firms Backup Recovery And Cybersecurity

Posted on by Imarticus Learning
Reading Time: 2 minutes

The coming of the Age of Artificial Intelligence is an apt way to describe how IT services like cyber-security, recovery of data and backup of data has been impacted by AI developments globally. Any event on cybersecurity throws up newer requirements in cyber-security and a bouquet of innovative solutions using artificial intelligence, cloud storage, and data recovery tools.
Virus detection is a challenge-area that about 29 percent of surveyed professionals look to AI for as per ESG research. Besides speedy discovery, 27 percent of the surveyed professionals in the security field look to AI to hasten the response time of reported incidents.AI is being touted as today’s technological marvel that can analyze huge volumes of code in very short time periods. This is rightly true and the mind-boggling speeds and analysis of data have made AI all pervasive and the panacea to almost all ills in the IT sector.
AI Vs ML:
The terms AI and ML are being used oft interchangeably. In reality, both are useful tools that differ in their thinking abilities. ML uses algorithms to detect breaches in security which restricts their use to think outside the set framework. On the other hand, AI does not need algorithms or any further data when it comes to terms with any issue. It arrives at an unassisted intelligent conclusion.Both AI and ML techniques are the focus areas of dealing with advances in cyber threats. These techniques when applied can transform the scenario from defence to early detection and quick response to cyber infections.
Areas, where ML makes a huge difference, are

  • In scouring huge data volumes across thousands on nodes looking for potential threats.
  • In firewall applications, gateways, and APIs where traffic patterns need to be analyzed.
  • In classifying data objects and governance of data
  • In access-control and authorization systems and practices using auto-generate policies, and analysis of the regulatory measure, rules etc
  • In detecting anomalies and setting baselines for user behavioural analysis and SIEM events of cybersecurity.

Hacker’s too can use ML and AI:

Alongside the new developments in AI, cyber-security, and ML there is a the all real possibility of hackers also using the same infection-detection technology and malware samples to advance the technology of cyber-threats. It is reasonable to predict that the very same techniques are used by hackers to create modified code-samples depending on the way AI detects infections. This then leads to a situation where the infections last longer and since the code is smaller it becomes near undetectable.

Storage challenges and Cybersecurity:

The feasible option for safe storage of data today is by backing data to a reliable disaster recovery cloud which enables rapid recovery of files while ensuring the data stays protected, safe and encrypted. The market has many technological options like Avast, CA Technologies secure, Keeper, etc that can help keep data copies out of reach by hackers and yet available on an easy-to-use platform.
In conclusion, the use of ML and AI can help resolve issues and challenges faced in cybersecurity, data recovery, and storage. The evolution of threats and detection techniques continue in tandem in a seemingly unending fashion where users and hackers are both looking to AL and ML for solutions.

Exploring the Potential of AI in Healthcare!

Posted on by Imarticus Learning
Reading Time: 2 minutes

To begin with, let us start with AI and its potential. What exactly is AI?
Over the last two decades, we have built huge data resources, analyzed them, developed ML both unsupervised and supervised, used SQL and Hadoop with unstructured sets of data, and finally with neural and deep learning techniques built near-human AI robots, chatbots and other modern wonders with visualized data.

We have found, optimized pattern-based techniques, exploited tools of ML, deep-learning, etc to create speech, text, image applications used pervasively today in games, self-driven cars, answering machines, MRI image processing and diagnosis of cancer, creating of facial IDs, speech cloning, facial recognition tools, and learning-assimilation products like Siri, Google Assistant, Alexa and more.

AI potential in healthcare:
The AI potential to use intelligent-data both structured and unstructured makes it a very effective diagnostic tool. Their processing speeds, ability to find anomalies and volumes of data AI can handle makes it irreplaceable, un-replicable, and the most effective tool in the healthcare sector’s diagnosis and cost-effective treatment sectors for masses.

EyePACS aided-ably by technology has been used to integrate symptoms, diagnosis, and lines of treatment in diabetic types of retinopathy. Beyond diagnosis AI in the UK helps diagnose heart diseases, cardiovascular blockages, valve defects etc. using e HeartFlow Analysis and CT scans thereby avoiding expensive angiograms.

Cancer detection at the very cellular stages is a reality with InnerEye from Microsoft which uses the patient scans to detect tumors, treating cancer of the prostate, and even find those areas predisposed to cancers. Babylon health and DeepMind are other query-answering apps that have saved multiple doctor-visits by answering queries based on patient-records, symptoms, and other data.

All that AI needs to retransform healthcare services is the appropriate and viable infrastructure that is so vital but expensive today, keeping it out of reach of the masses at large.

Cautions with use of AI:
The first issue with AI is that its ability and potential is often misused. Some doctors fear AI may one day take over their roles. The use of laser-knives, surgical implements, tasers for immobilization, and deep-neural stimulation of the brain to prevent seizures are being used to take healthcare to the underprivileged masses. Harnessing the potential of AI is an issue of ethics, the patient’s privacy, and lives. Doctors need to use these tools to aid and not replace human-intervention in healthcare.

The issue of data transfers, privacy issues, legal responsibilities, misuse, and selling of data is of high importance. Another issue that looms large is the inertia to change and sufficient testing before large-scale adoption in healthcare for the masses. An approach that is amply cautious is always the best when millions are being spent on healthcare, and the lives of masses are at stake.

The right way to implement AI into the healthcare system would then hinge on education and training, sufficient testing and trial-runs before implementation, assuring and involving all stakeholders, and rediscovering ways and means of optimizing human-intervention in diagnosis, treatment, and care of patients.

The industrial sector will see thousands of opportunities thrown up which in turn when exploited create growth and employment opportunities galore. AI in healthcare is truly a panacea-producing tool. Perhaps in the near future immortality-quest will also take its place in the fields being explored. For now, the Government, doctors, researchers, industries, and patients are all set to accept the positive impact of AI on the healthcare sector readily.

Why Do People Often Use R Language Programming for Artificial Intelligence?

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Why Do People Often Use R Language Programming for Artificial Intelligence?

All over the world, machine learning is something which is catching on like wildfire. Most of the large organisations now use machine learning and by extension, AI for some reason or other – be it as a part of a product or to mine business insights, machine learning is used in a lot of avenues. Even the machine learning future in India seems all set to explode in the next couple of years.

All this has led companies to be on the lookout for proficient practitioners, and there are a lot of opportunities existing currently in this field. You might have started to wonder how you can make your mark in this science field – machine learning and AI are something which you can learn from your home, provided you have the right tools and the drive for it.

Many students have already started learning R, owing to the availability of R programming certification course on the internet. However, some are still not sure whether they want to learn R or go for Python like many of their peers are. Let us take a look at why R certification course is a great choice for machine learning and Artificial Intelligence programming and implementation. 

Features of R
R is a multi-paradigm language which can be called a procedural one, much like Python is. It can also support object-oriented programming, but it is not known for that feature as much as Python is.

R is considered to be a statistical workhorse, more so than Python. Once you start learning, you will understand that statistics form the base of machine learning and AI too. This means that you will need something which can suit your needs, and R is just that. R is considered to be similar to SAS and SPSS, which are other common statistical software. It is well suited for data analysis, visualisation and statistics in general. However, it is less flexible compared to Python but is more specialised too. 

R is an open source language too. This does not simply mean that it is free to use, for you – it also implies that you will have a lot of support when you start to use it. R has a vast community of users, so there is no dearth of help from expert practitioners if you ever need any.

One other thing that differentiates R and Python is the natural implementation and support of matrices, and other data structures like vectors. This makes it comparable to other stats and data-heavy languages like MATLAB and Octave, and the answer that Python has to this is the numpy package it has. However, numpy is significantly clumsier than the features that R has to offer.

Along with the availability of a lot of curated packages, R is definitely considered to be better for data analysis and visualisation by expert practitioners. If you think that you want to try your hand at machine learning and AI, you should check out the courses on machine learning offer at Imarticus Learning.

What is the Artificial Intelligence Markup Language?

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Reading Time: 2 minutesArtificial intelligence is the technology of the future. It has exploded onto the world ever since it was first developed, and the technology has since been implemented in a lot of fields, ranging from healthcare to warfare. AI looks all set to stay and is sure to play a huge role in how the future of humanity is shaped.

However, it should be noted that AI was not always developed using popular languages today. Currently, Python and R represent the most popular languages which are used in machine learning and consequently, in AI too. However, there are a lot of other languages and methods which were used at times to various ends.

AIML was one such language which was used in the development of early chatbots. Digital assistants or chatbots truly represent the dawn of a new chapter in the scientific advancements of humankind. Chatbots are now increasingly becoming a part of most companies, and most of the internet users have already interacted with a chatbot in some form or other.

Being an AI aficionado or a prospective practitioner, you can surely try to build a chatbot from scratch in order to gain some practice in Artificial Intelligence.

What is AIML?
Artificial Intelligence Markup Language or AIML was created by Dr Richard Wallace and is currently offered as an open source framework for developing chatbots. It is offered by the ALICE AI Foundation so that users can create intelligent chatbots for their use from scratch.

AIML is an extremely simple XML, just like HyperText Markup Language or HTML. It contains a lot of standard tags and tags which are extensible, which you use in order to mark the text so that the interpreter which runs in the background understands the text you have scripted.

If you want the chatbot to be intelligent, it is important to have a content interface through which you can chat. Just like XML functions, AIML also characterizes rules for patterns, and decide how to respond to the user accordingly. AIML has several elements in them, including categories, patterns, and templates.

Categories are the fundamental units of knowledge which are used by the AIML and is further divided into the two other elements mentioned above – templates and patterns. In layman’s terms, patterns represent the questions asked by the user to the chatbot, or what the chatbot perceives as questions which need to be responded to.

The templates are the answers which it remembers based on its training, and which are subsequently modified and presented as replies to the users. Template elements basically include text formatting for the responses, conditional responses taught to it including many if/else scenarios and random responses which always come in handy while interacting with a user.

AIML is now open source, and users can start to create a chatbot by learning the fundamentals of the language. If you find yourself yearning to know more about this and AI in general, you should check out the many artificial intelligence courses on offer at Imarticus Learning.

What is the Best Programming Language For Artificial Intelligence Projects?

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Reading Time: 2 minutesArtificial Intelligence is the hot topic of the last couple of years and is all set to be the science of the future. It has already opened up a realm of possibilities for humans, and by taking advantage of a machine and deep learning, it is no doubt going to play a huge role in the future of humanity. You can do almost anything with this technology – even build apps which can hear, see and react accordingly.

A lot of newcomers are beginning to get into programming for AI, considering how important it is turning out to be. However, with the plethora of options available, it can be difficult to choose a particular language for programming. Let us consider the many languages which are currently being used for AI development.

Python
Currently rising in popularity, it is one of the main languages which come up in how to learn machine learning. Being extremely simple to use and learn, it is preferred by many beginners. Compared to other languages like C and Java, it takes extremely less time for implementation.

Another advantage is that with Python, you can opt for procedural, objective oriented or functional style of programming. There are also a lot of libraries which exist for Python, which make programming considerably easier.

Java
A comparatively older option, it first emerged in 1995 – however, it’s importance has only grown at an unparalleled rate since then. Highly portable, transparent and maintainable, this language also has a large number of libraries to make it easier for the user.

Java is incredibly user-friendly and easy to troubleshoot and debug, and the user can also write code that runs on different platforms with ease. The Virtual Machine Technology implemented in Java is key to this feature, actually. Many Big Data platforms like Apache Spark and Hadoop can be accessed using Java, making it a great all-around option for you.
Julia
Developed by MIT, this language is meant for mathematical analysis and numerical computing to be done in a high-performance fashion. These features make it an amazing choice for AI projects since it was designed keeping the needs of Artificial Intelligence in mind. Separate compilation is done away with, too – however, it is only growing, so it does not have the same number of libraries as the others.

Haskell
Haskell, unlike Java, is a great choice for engaging and working with abstract mathematical concepts. You can create AI algorithms using the expressive and efficient libraries which come with the language, and the language is far more expressive compared to many others.

Probabilistic programming is also a cakewalk since developers are able to identify errors relatively quickly, even during the compile phase of iteration. However, you still cannot expect the same level of support that Java and Python offers.

You will need to learn some machine learning skills, if you are to have a long career in this field – in order to do that, you should check out the big data and machine learning courses on offer at Imarticus Learning.

How ML AI Is Allowing Firms to Know More About Customer Sentiment and Response?

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Reading Time: 2 minutesThe importance of customer service for any industry just cannot be stressed enough. A recent study done by Zendesk showed that 42% of customers came back to shop more if they had an excellent customer experience, while 52% never returned once they had a single bad customer service experience.

The implementation of Machine Learning powered artificial intelligence is fast becoming the next significant revolutionizing change within the customer service sector. So much so that several studies have indicated that over 85% of all communications involving customer service will be done without the participation of a human agent by 2020.

Customer Service with AI
Customer service has become one of the most critical applications of artificial intelligence and machine learning. Here, the basic concept behind the service remains the same, with the implementation of AI making it far more sophisticated, easy to implement, and way more efficient than conventional customer support models. AI-powered customer service today doesn’t just include automated call center operation but a mixture of services including online support through chat.

Along with the diminished costs associated with using an AI, the other main advantage is that AI can dynamically adapt itself to different situations. These situations can change according to each customer and their related queries. By monitoring an AI during its initial interactions with customers and correcting them every time a wrong step is taken, we can permanently keep “teaching” the AI what is right and wrong in particular interaction with a certain customer.

Due to the AI being able to “learn” in this way, it will have the capability to accurately determine what needs to be done to rectify a particular complaint and resolve the situation to the customer’s satisfaction.
The AI can be trained to identify specific patterns in any customer interaction and predict what the customer will require next after each step.

No Human Errors
Another advantage of AI is that human error, as well as negative human emotions like anger, annoyance, or aggression, are non-existent. AI can also be trained to escalate an issue if it is out of the scope of its resolution. However, with time and increased implementation, this requirement will quickly decrease.

In today’s fast-paced world, more and more people prefer not having to waste time interacting with another human whenever it isn’t essential. A recent customer service survey targeted at millennials showed that over 72% of them prefer not having to resort to a phone call to resolve their issues. The demand for human-free digital-only interactions is at an all-time high.

Thus, it would be no surprise to find that savings would increase drastically with the implementation of AI-powered chatbots. One research by Juniper Research estimated that the savings obtained through chatbots would increase from $20 Million in 2017 up to more than $8 Billion by 2022. Chatbots are also becoming so advanced that according to the same report, 27% of the customers were not sure if they had interacted with a human or a bot. The same story also added that 34% of all business executives they talked to believe that virtual bots will have a massive impact on their business in the coming years.

Hence, the large-scale implementation of AI in customer service is inevitable and will bring drastic improvements in customer satisfaction and savings shortly.