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Tag: Machine Learning

What Are The Prerequisites For Artificial Intelligence?

Posted on by Imarticus Learning
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Artificial intelligence keeps changing in its definition as does its scope and capabilities. A few decades ago, simple calculators were considered artificial intelligence since math problems were previously only solved by the human brain. Today, artificial intelligence powers home automation systems and gadgets like Google Home, Siri, and Alexa. We see new AI being released almost every week with juggernauts like Google and Facebook it improve the user experience. The auto-reply feature with suggested replies on Gmail is an example of artificial intelligence where the responses are ‘taught’ to the machine.
Having a good foundation is imperative if you want to foray into artificial intelligence. It isn’t as simple as attending a machine learning course to be a valuable employee in the field of AI. People who are interested in artificial intelligence can take several paths to learn the various AI skills necessary for the subject. Based on your previous knowledge and skill level, you should chart your own course.

The prerequisites of artificial intelligence will give you a good foundation to stand upon when you are learning the key concepts. You will have to have a good foundation in calculus, linear algebra, and statistics in order to help you to develop algorithms. You will also need a good knowledge of Python and Python for data science track as it is the predominant language used in machine learning.
Whatever math skills you might have already, you might want to brush up on them before foraying into Artificial Intelligence. There are many courses available online that will go into depth about the various concepts used in AI. If you are getting into AI to solve a problem, then you can rely on existing libraries to help you with the math required. However, if you are looking to get into research or deep into machine learning, you will have to get an in-depth knowledge of math.
The next steps involve learning and soaking up as much machine learning concepts and theory as you can. It will help you on many fronts including planning and collecting data, interpretation of model results, and creating better models.
The next step should focus on data cleaning, exploration, and preparation. As someone who will be working with machine learning, you will have to have a good quality of feature engineering and data cleaning on the original data you have. This is a very important step and will regularly feature in your work in the future. You should spend as much time as you can here, doing practice tests and runs.
For practice, you should participate in as many Kaggle competitions as you can. These are generally easy and will help you work with multiple scenarios and typologies. With machine learning, the more practice you have, the better you are.
As a beginner, these are the steps you will have to take in order to understand the basics of artificial intelligence. If you are interested in a deeper understanding of the subject, then you can opt of Deep Learning and Machine Learning with Big Data.

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How AI in The Energy Sector Can Help to Solve The Climate Crisis?

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How AI in the Energy Sector Can Help Solve the Climate Crisis
Have you not complained about the crisis that is looming large in our environment? The news reports of untimely floods, missing rain patterns, fires in forests, carbon emissions and smog affect each and every one of us. The Davos meeting of the World Economic Forum threw up some important measures that we need to take in enabling AI, ML and technology as a whole in symbiotically tackling the climate crisis of all times.
The main cause of the changes in climate is being attributed to emissions of carbon and greenhouse gases. And each and every person in tandem with AI, technology and the big industrial players have a bounden duty to support such measures and immediately move to reduce these emissions if we wish to halt such catastrophic climate changes. Noteworthy is the funding of nearly billion dollars in such ventures by Bill Gates and Facebook’s Mark Zuckerberg.
Here is the gist of the top suggestions. In all these measures one looks to technology and artificial intelligence to aid and achieve what we singularly cannot do. This is because the noteworthy improvements brought about by AI are
AI helps compile and process data:
We just are not doing enough to save our planet. The agreement between countries in Paris to be implementable means elimination of all energy sources of fossil-fuel. AI enabled with intelligent ML algorithms can go a long way in processing unthinkable volumes of data and providing us with the insight and forecasts to reverse the climatic changes, use of fossil fuels, reduction of carbon emissions, waste etc, and setting up environment-friendly green systems of operations.
AI can help reduce consumption of energy by ‘server farms’
The widespread use of digitalization has led to server farms meant to store data. According to the Project Manager, Ms. Sims Witherspoon at Deepmind the AI British subsidiary of Alphabet when speaking to DW said that they have developed a bot named Go-playing with algorithms that are “general purpose” in a bid to reduce the cooling energy of data centers of Google by a whopping 40%. This does amount to a path-breaking achievement when you consider that a total of 3 percent of the energy globally used is just used by the ‘server farms’ to maintain data!

Encouraging the big players to be guardians of the climate.

The industrial giants are using technology, AI and ML to reduce their footprints of carbon emissions. AI tools from Microsoft and Google are aiding maximized recovery of natural resources like oil, coal, etc. Though with no particular plans or place in the overall plan-of-action such measures do go a long way in preserving the environment through reduced emissions and set the trend into motion.

Using smartphone assistants to nudge for low-carbon climate-friendly changes.

The rampant use of smartphones and devices of AI makes this option possible and along with zero-click AI enabled purchases the virtual assistant bolstered through ML algorithms and tweaked infrastructure can be used to influence choices of low-carbon climatic and emission-reduction changes.

Social media can transform education and societal choices.

The biggest influencer of social change is the social media platforms like Instagram, Facebook, Twitter, etc these can be harnessed to publicize, educate and act on choices that help reduce such carbon emissions and use of resources.

The reuse mantra and future design.

Almost all designing is achieved through AI which can help us design right, have default zero-carbon designs, commit to the recycling of aluminium and steel, reward lower carbon footprints, grow and consume optimum foods and groceries and create green and clean smart cities.
Summing up the suggestions to be placed at the UN Global Summit for Good AI at Geneva, it is high time we realize that the future lies in data and its proper use through AI and empowering ML. We need new standards for use of the media and advertising digitally. All countries need to globally work to reduce the use of fossil fuels in automobiles and transportation. We must cut our emissions by half in less than a decade and this is possible through proper use of data, AI, ML, and digitization.
If you care enough to be a part of this pressing solution to environmental change, learn artificial Intelligence at Imarticus Learning, how AI has the potential to harness data and control the damage to our environment. Act today.
Reference:

What is the future of Artificial Intelligence?

Posted on by Imarticus Learning
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One of the biggest developments in the world of computer science has undoubtedly been Artificial Intelligence. The ability of your machine to learn and understand all about certain processes and then implement methods to improve the same is one of the most in-demand jobs today.

It becomes necessary to evaluate a company’s software and see how they can implement artificial intelligence methods.

There is so much that is possible while applying artificial intelligence in marketing. By 2020, more than 30% of the companies worldwide will use AI to help streamline their sales. This will help them increase efficiency and focus more on converting sales and rates.

Here are a few other places where AI will play a prominent role:

  1. Driverless vehicles:

Automated vehicles aren’t a dream anymore. The likes of Tesla have already started implementing driverless cars on the road. The U.S. Department of Transportation has gone ahead and released certain definitions and rules pertaining to the various levels of automation which can be implemented.

Uber was also acquired by Google in order to help scale their properties and capture the driverless market in time. AI could help save lives lost in accidents and potentially save close to 30,000 people in the United States every decade.

As it is a disruptive technology, it is expected to create some big changes. It can also automate many jobs which affect people. In the near future, it is expected to be used for opportunity more than threats.

       2. Process automation:
Robotic process automation refers to the use of machine learning to automate tasks dependent on rules. It will help individuals focus on certain crucial aspects of their work and leave the routine work to machines.

Automated projects will take up a bulk of the automation work in the world of machine learning and artificial intelligence. Companies are always looking to be cost-effective and automated machinery will help them achieve that goal over the long term.

     3. Sales and marketing:
Artificial Intelligence is also being employed in so many sales and marketing sectors. AI can be used as a useful tool to make repetitive tasks much easier. This includes tasks such as scheduling, paperwork and even timesheets to make it easier.

Marketing teams will also be able to weed out fake leads from genuine ones to make it easier to choose the right people to market to. They will be able to make the process simpler and allow everyone to get better at their daily tasks.

Overall, artificial intelligence is on the route to make the world a better and easier place to live and work in. It is a disruptive technology which will create dramatic changes. It can also be used to automate a multitude of jobs, especially in the production sector and make it easier for companies to become cost-effective.

Over the long run, where this will head to cannot be predicted but by the looks of it, it seems like a good place to be in. With Imarticus, you will be able to take up an artificial intelligence course that makes it simpler for you to succeed. In the battle between machine learning vs artificial intelligence, you are the real winner!

Deep Learning and its Application for Facial Recognition

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Deep Learning, ML and AI are all used to support facial recognition and used traditionally the Eigenvalues for vectors and spaces defining the features of the space projected by the face. In 2012 AlexNet tweaking and deep learning technologies like the DeepID, DeepFace, FaceNet, and VGGFace went beyond the human capacity to recognize faces by aligning, using feature extraction, detection, and recognition techniques. Thereby the use of verifying faces in a photograph under various lighting conditions, an aged face, with glasses or without facial hear was made possible by leveraging deep learning of face datasets and model representations.
The recognition software is biometric in nature and can accurately identify, authenticate and verify a face just by comparing the facial features and contours against very large databases.
It is widely used for: 

  • The enforcement of the law by the police and detection agencies.
  • In businesses for biometric logging in and out.
  • In banking to ensure KYC and restricted access to lockers.
  • In AR and VR applications for animated film making.

Authentication through facial recognition:
The most useful advantage of facial recognition is that facial contours do not change and can be captured from a distance. It never fails since faces cannot be replicated or imitated successfully. The technology itself is of a non-contact biometric type and has been successful in restricting entry, ensuring attendance, for crime prevention, law enforcement and as a security measure. The technology is also inexpensive and infallible when compared to other methods like fingerprinting, Retinal scans and such biometric methods which are contractual in nature needing the voluntary provision of data for further process.
Many devices need and work on authentication based on face photograph verification either taken from videos or still photos. Human beings are very good at this task and deep learning simulates the same process.
Deep Learning and ML use ConvNets for the analysis and identification processes. Such neural networks are highly intelligent, self-taught and have other applications sewn in like the NLP processor, video analyzer, recommender modules and such.
The four essential steps involved are:
1. Detection which involves detection and using a boundary box for the image face. It generally falls into two categories namely
Based on features and using hand-work filters based on knowledge of the domain.
Based on images and ML where neural networks work on extraction and location of the image.
2. Alignment tasks normalize the photometry, geometry and such parameters with the database since most photographs contain more than one face and need to be aligned. The alignment output depends on the following task categories.

  • Binary labels for class and probability.
  • Similarity parameters.
  • Category labels.

3. Extraction of facial features is used for the task of recognition. The tasks can be further classified as tasks for

  • Matching and finding the best results.
  • Similarity analysis for faces.
  • Feature transformation and generation of new similar face images.

4. Face Recognition itself consists of two main tasks to identify any given image. Namely,

  • Verification where features of the identified face are mapped to the given image.
  • Identification where a given image is mapped against the database.

ML has proved to be invaluable to Deep Learning solutions. The present-day technological advancements make facial recognition and such issues easy. One has to choose the algorithm and feed in the given face image or data. The built-in neural network and trained dlib models will then take care of analyzing the face, comparing it against its databases and giving us an accurate match of the face against it. Further, the face recognition software on Github is easy to use, has a great library and is a rapid install.
Conclusion:
Deep learning machine algorithms and neural networks can currently manipulate, detect and identify facial contours from very large databases very quickly and this ability is far beyond human capacities.
If you are interested in such specific applications you will need to do courses that are skill-oriented in ML, Neural networks, Deep Learning, handling databases and applications, AR, VR, and such futuristic technology. Most of these courses are offered by Imarticus Learning where learning is practically based and you are job-ready from day one. Who does not like able-mentorship from certified trainers, a widely accepted global certification and assured placements when looking to transition careers? Don’t wait too long. The route and opportunities are just right at the moment.

3 Ways in Which AI is Transforming Business Operations

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The business scenario today has evolved and kept pace with technological developments. And AI has been at the helm of the change experience impacting literally every area that affects growth and development. The changing economic, geopolitical and social environments are in a state of constant flux and need businesses to adapt very quickly to tide over the changes in organizational dynamics, critical business glitches like employee retention and hiring or landscape requirements like being scalable and Agile.
Artificial intelligence can help bridge over troubled waters in many areas where human intelligence and limitations fail. Let us explore some of these critical areas where AI has and still has the potential to improve the business scenario.
The successful customer and user experience:
The experience of the customer is what tells brands apart and this differentiator is best exploited through successfully harvesting of the data and changes brought about by AI. Research and use of Walker data suggest that large multinationals like Adobe, Intuit, and EMC have benefitted greatly by entwining the customer experience into their operational daily routines of marketing, sales, and operational routines. And AI makes it possible to offer those great user-experiences crafted from forecasts and gleanings of data on why the customer buys, when and for how much, how the competition fares and their latest parleys, or what the customer wants from you.
The arsenal of data forecasts and insights can personalize an individual’s experience to match his needs, budget, etc, through a more seamless integrated process that offers high satisfaction and customer loyalty. The results are most helpful in rapidly predicting markets, changing products, forecasting customer- behavior, and staying up to date with the latest offers of technology. Thus AI is the one tool that has immense potential in accumulating, understanding and changing the fortunes of business enterprises by forecasting touch-points, trends, brand preferences, pricing strategies and more.
Bettering the hiring process:
The acquisition of skilled talent is critical to all businesses. However, most processes like recruitments, interviews, talent hunting, employee-referrals, and assessments are subject to very many biases, nepotism, controls, and flaws.
For bettering the hiring process certain tasks are all important. Firstly, one has to cast the net wide. Secondly, the talents need to be matched to the job requirements and the process of pivoting in on the right candidate needs to be free of human errors and bias. Lastly, the holistic use of data using the latest developments needs to be deployed. Not surprisingly, AI aided assistants today can make short work of the recruitment process while ensuring a great supply database for recruitments and keeping in mind the specifics of talent growing into higher roles and reducing the pitfalls of employee migration and retention issues.
Retaining and engaging the employees:
Skill and talent lie at the core of the hiring process. With increased demand comes the problem of retention and employee engagement turning into a competitive minefield. Poor management practices, lack of growth on the job and employee engagement have turned into major contributors for lack of retention of employees as is evident from surveys conducted by SalesForce and Gallup.
AI has enabled cutting-edge technologies like analysis of employee sentiments, biometric trackers, and such AI-empowered techniques can aid in effective retention through timely motivation, employee empowerment, continued learning opportunities and ensuring deserving rewards, career growth, skill up-gradation and more. More engaged employees mean better retention, employee loyalty, and engagement.
Conclusion:
In parting, it is valid to note that AI helps the new operations in business which in turn can change the dynamics of a beyond satisfying customer-experience, growing engagement with employees, hiring and retention. People are assets to the company and the twist that AI and technology have brought in can easily transform companies through efficient dynamics, change and people management.
To learn all about futuristic technologies like adaptations of artificial intelligence, powering AI through effective Machine Learning, scouring the growing volumes of data through Deep Learning and beyond to futuristic technology like blockchains for fintech industries try the Imarticus Learning experience.
The Agile Scrum Tutorial are succinct with due emphasis on the practical applications of knowledge and concepts coupled with invaluable modules of self-development and soft-skill training. Besides, one gets the mentorship of certified and industry-drawn mentors and instructors. Go ahead and make the most of opportunities and jobs on offer in their placement program too. Why wait then?

Linear Regression and Its Applications in Machine 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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Reading Time: 2 minutes

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.

Which Skills are Required for Machine Learning Jobs?

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Machine language is generally related artificial intelligence, which provides the machine or computers with the ability to complete certain tasks like diagnosis, planning, prediction, recognition or robot control. It consists of different algorithms, which you can use to teach the machines to change and grow when exposed to new data.

The process of implementing machine language is somewhat similar to data mining because the process looks through data and searches for the same pattern. Now that you have an idea of what machine learning is, let’s have a look at the skills that are required to get a machine learning job.

Also Read: Future of Machine Learning in India

Computer Science & Programming Skills

Some of the fundamentals of computer science are essential when you are looking to learn machine learning. Concepts like data structure, algorithms, complexity and computability, along with computer architecture are essential for artificial intelligence. In fact, you should also have knowledge of programming languages like C, C++, Java, Python and R, among others. A little bit knowledge of assembly language doesn’t hurt either.

Probability & Statistics

Conditional probability, and its characteristics and the techniques derived from it plays a key role in the machine learning algorithms. Moreover, you should also know about the different terms of statistics like mean, median and mode along with variance and standard deviation. These are all necessary to not only observe the pattern but also validate the data that is received through different means. Some machine learning algorithms are in essence an extension of the common statistical operation procedures.

Applied Mathematics & Algorithms

You need to know not only how to solve a problem but also how to implement it in short executable steps when it comes to machine learning. Algorithms help you to understand how to break down a problem into executable steps, and that is why this is important. In addition, you also need to know about gradient, convex optimization and its application in daily life, so that you can implement it in machine learning.

Operating Systems

When it comes to machine learning, most of the coding is done in Linux or some version of it. So, you need to be versatile with Unix or a version of Linux, which is in use presently. You also need to know about the Linux tools, which will make your life easier in the long run. Some examples include grep, find, sort and tr.

Software Engineering & Designing of Systems

When you are designing a machine learning tool, you are also designing an advanced software. So, at the end of the day, you need to know how to design system, and how you can implement your ideas in that. You also need to understand how different algorithms interact with your system, and how you can speed up the process without compromising on the resource space.

Now that you know about the skills required for machine learning jobs, it is time to get started on acquiring these skills. In case you have some of these skills, make sure you hone them so that you can implement it and build a great system when the time comes. Good luck!

Related Article: What is The Easiest Way To Learn Machine Learning?

Want To Learn Artificial Intelligence And Machine Learning? Where Can You Start?

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Reading Time: 2 minutes

These are exciting times to be a part of the technology industry, what with newer and fascinating fields being discovered every day. With the tech companies leading the way, the newer fields like Artificial Intelligence and Machine Learning are finding takers in multiple domains. Probably, this is the reason the demand for machine learning courses is at an all-time high.
Every second person, in the technology domain, you interact with would wax eloquent about how they are going to learn machine learning or how they have been taking the latest and most difficult artificial intelligence courses. Hearing this can be quite an unnerving experience, especially if you are a newbie in the field and looking to find out some machine learning courses that can help you unravel this mysterious world.
But, fear no more, as in this article, we try to help you find your initial foothold in this domain and slowly but surely come up to speed in your quest to learn machine learning. The first thing that confuses newcomers and throws them off-track when they begin to learn machine learning is the different terms and their interrelationships. What do the terms – Artificial Intelligence, Deep Learning, Neural Network Programming, Machine Learning – mean and how are they related.
If you somehow can navigate through the maze of big data and machine learning courses and get a hang of these terms, another big question comes up. Do I need to learn programming, statistics as well as calculus? Is this the right direction for my career, even though I do not understand either of this? Is there a way to learn machine learning without being proficient in all these?

There are no straight forward answers to these questions. But below are a few pointers that may help you to take your first step as well as identifying the correct artificial intelligence courses for yourself.
First and foremost, we need to understand the interrelationship between artificial intelligence and machine learning. Artificial intelligence is mostly trying to mimic human intelligence and behaviour by the machines including creativity, learning and reactions to any situation. As you start to learn machine learning with the help of machine learning courses, you realize that machine learning is nothing but a subset of artificial intelligence, dealing with pattern recognition and self-learning.
Now, the next fundamental question – do you need to learn programming language or statistics to complete artificial intelligence courses? You definitely need to have some basics in both as the statistics help you understand what you are doing, and the programming language shows how it is done. In case either of these is unknown to you, it is recommended you start your quest to learn machine learning with the courses for these.
You can find some excellent courses to learn programming languages like R or Python, models and algorithms basics through – imarticus.org Or also can contact on – info@imarticus.com or 1-800-267-7679 Or else you can visit our different training locations in India – Mumbai, Pune, Thane, New Delhi, Banglore, Chennai, Gurgaon, Hyderabad and Ahmedabad.
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