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Tag: machine learning courses

How Machine Learning is Changing Identity Theft Detection?

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Debilitating data breaches and identity theft scenarios have left several high-profile firms across the globe scrambling to recover losses. In 2018 alone, in the US, over $1.48 billion worth of losses occurred, after 1.4 million fraud reports1. Of these reports, identity theft was a significant defining factor. Businesses and corporates alike are turning to machine learning and Artificial Intelligence (AI) in general for help. Current employees are also being upskilled for an artificial intelligence career through machine learning courses in order to prep for the future of machine learning.

Machine learning has already permeated everyday lives, from online recommendations on your favorite streaming site to self-drive cars that have awed the masses. When it comes to identity theft detection, machine learning has so much potential– especially since there are larger players and higher factors at stake.

Here are some ways in which AI and machine learning are being leveraged to detect, reduce and prevent identity theft:

Authentication Tests

With machine learning, identity documents including the likes of passports, drivers’ licenses, and PAN cards are scanned and cross-verified with an unseen database in real-time. An additional set of authentication tests can usurp theft to some extent– the use of biometrics and facial recognition being some of the more used ML-based tests. Other examples of authentication tests include microprint tests, OCR-barcode-magnetic strip cross-verification, and paper-and-ink validation2.

Real-Time Decision Making

Machine learning training has the power to operationalize and automate the process of data analytics, especially tasks that are mundane or prone to human error. Beyond speeding up the process of identity theft detection, machine learning enables real-time decision making to stop theft in its tracks or sound an alert in case of a potential threat. This is a boon for businesses both large and small who cannot afford to waste valuable human resources on mundane tasks. By detecting identity theft at speeds hitherto unmatched, machine learning allows analysts to make spot decisions before any damage is done.

Pattern Identification

An added benefit of using machine learning to revolutionize identity theft detection is pattern recognition. Since any machine learning algorithm is wired to a database with tonnes of data, these algorithms can scan through all the information available over the years to predict future threats and identify the source and patterns so that preventive measures can be taken in advance. This is beneficial in that it creates links between individual theft cases, allowing analysts to better assess what the best plan of action is in response.

Dataset Scaling

The more data that’s collected, the better machine learning algorithms are trained for a variety of situations. Unlike many other scenarios where lots of data mean more complexity, a wider database allows machine learning algorithms to be scaled and adapted as required. It also allows them to grow more accurate with every addition, make comparisons and identify genuine and fraud transactions in an instant– a true step up from the days of human involvement. However, a caveat– in training stages, it is crucial that analysts be monitoring the process because if the machine goes over an undetected fraud without flagging it, chances are it’ll learn to ignore that type of fraud in the future, opening up a big sinkhole in the system.

The final word

Machine learning is revolutionary in preventing billions of dollars being lost in fraud, theft and data recovery. Firms are increasingly allocating a huge chunk of their budget towards sound ML-based security systems– a testament to just how revolutionary the technology is in identity theft detection.

The Impacts of Robots in Regular Life!

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Reading Time: 3 minutes

Robots are used in many areas of our life, including 10 possible uses of robots in our daily life: Automated transport (autonomous robot) Automated transport (autonomous robot) The first major spread of visibility The use of mobile robots is seen through autonomous cars.

The advances in the development of automated autonomous vehicles over the past 10 or 15 years have been astonishing. New cars without robotics are like computers on wheels. But with robotics, they’re more efficient and dangerous. Autonomous robots are not robots that can drive cars. What it really means is that cars are built like robots and artificial intelligence is fed into those cars.

In a modern world like many countries in Europe and America, the automated autonomous vehicle is available like buses, trams and trains are automated, but vehicles like cars that circulate on the streets are not very general, but recently Audi, Mercedes, Google, they are presenting autonomous cars. The day is not far off when human drivers are not needed to drive vehicles.

As a result, accidents may not happen as many as there are today. Security, Defense and SurveillanceSecurity, Defense and Surveillance The work of security, defense, and surveillance robot is normal: it inspects the desired area. Immediately notify the owner if there has been any kind of malfunction. This type of robot is used in the military. This type of robot can also be used in everyday human life.

In the army, this type of robot does different types of work. . They are used to arm and defuse bombs. You will be sent to the desired area to monitor enemy activity, which is definitely a dangerous job for soldiers.

Robotics trainingFor use in everyday human life, this type of robot monitors your house. This robotics training helps people to monitor the sky, the ground, and the water from a remote location.

You can control this kind of robot from another location to send them to the desired location to monitor the activities of that location. Your home and property will not be damaged if you are not around to monitor them.

Cooking with Robots Cooking with Robots After spending a full day at the office, it becomes annoying to motivate yourself at home to cook a delicious meal for yourself.

The abbreviation is sometimes not healthy and tasty enough. But what if you had a robotic kitchen assistant to help you cook the food to your liking? There are many programmable robots that can prepare the food of your choice. All you have to do is set the number of food ingredients. The robot does the rest. A lot of robots are now being introduced that you can copy. You only need to cook in front of the robot once.

The movement of your body is registered by the camera, from then on the robot will copy your actions to prepare this meal for you, this type of robot kitchen helpers are being introduced in many hotels and homes, some companies are making these types of robots, including, Moley Robotics, Shadow Robot companies are quite famous.MedicalMedicineThe influence of robotics is undeniable in the medical field. Recently, engineers have successfully discovered surgical robots.

This success has resulted in a large financial investment in robots in medicine. Recently, Google and Johnson & Johnson worked together to develop a next-generation medical robotic system. While robots were only used as assistants in the clinical system in the recent past, they are now being introduced as an integral part of the clinical system.

Although not yet possible, it is not far from replacing the surgeon with robots in operations. The robotic system has established itself in clinics around the world. Therefore, engineers work hard to successfully invent micro and nanorobots.

Doing things that require precise and accurate performance in a way that a human cannot. For the drug delivery system, these robots can concentrate the therapeutic payload locally around the pathological sites so that they can reduce the dose of the drug delivery and the side effects they cause.

Education roboticsEducation Robotics is now known as an all-purpose technology. This means that it has the potential to change societies through its effects on economic and social structures.

So it is now natural to start discussing robotics in education. Many students suffer from different types of illnesses on a daily basis.

Therefore, they cannot physically attend classes. Because of this, the lessons are lost. Engineers have developed robots that can help students attend their classes remotely. The robot acts as a person in the classroom that is controlled by the person himself. His cameras are his eyes and the body is used for interaction.

What is The Language Used To Make Artificial Intelligence Programs?

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A.I. or Artificial Intelligence is considered to be the next big thing in the science community. Many people are worried about its lousy use; many have raised the fear of Skynet in the actual world through the artificial intelligence programs. Well, dangerous or not, when it comes to being in the league of new technology, big MNCs don’t like to fall back.

Having said that it is clear that people who have done big data and machine learning courses are the ones who are getting selected in big MNCs. While learning these courses, the main question that arises is: in which programming language are these Artificial Intelligence programs written? The answer is more than one. Let us list out the big guns of the artificial intelligence industries –

  • Python: Since its discovery, Python has gained significance as a popular programming language. Many artificial intelligence programs are written in this programming language. Python is liked among the personnel working in the artificial intelligence field due to its syntax simplicity and versatility. It is easier to learn than C, C++, Java, etc. It is more dynamic and supports neural networks, which makes it easier to develop NLP solutions in an ideal structure.
  • C++: C++ may be a little bit complex than Python, but it is one of the fastest programming languages out there. As artificial intelligence programs are vast numbers of lines of codes, the fast processing speed of C++ gives it an edge over the others. C++ allows the use of humongous algorithms while presenting statistical data. C++ is considered to one of the most suitable programming languages to learn for writing artificial intelligence programs.Investment Banking
  • Java: Java is popular among the programmers because of its ability to be a multi-paradigm programming language. The two main principles followed by Java are object-oriented programming and WORA principle. WORA or Written Once Read/Run Anywhere makes it possible to run Java programs over any system without recompiling it. It is best suited for Neural network and heavy-weight artificial intelligence programs.
  • LISP: LISP is not only a single programming language, but it is a family of programming language. It belongs to the category of the oldest programming languages in the market after FORTRAN. Although old LISP has evolved as the time passed to become one of the most influential programming languages used in artificial intelligence programs, it is favored0 in the account of the freedom it offers to the developers. LISP possesses an exceptional macro system which eases the work of implementation and investigation of specific intellectual intelligence programs.
  • PROLOG: PROLOG is the next oldest programming language in this list. It earned this position due to its underlying mechanisms which come in handy for various artificial intelligence programs. For example, PROLOG’s basic mechanisms include pattern matching, tree-based data structuring, automatic backtracking which are crucial for artificial intelligence programs.
  • Its mechanisms enable a flexible framework which is liked by the programmers. It is also known as a rule-based declarative programming language as its processing is done based on specific rules and elements which lay the very ground of artificial intelligence programs. PROLOG is one of the primary programming languages for artificial intelligence programs. It is used in some artificial intelligence enabled medical systems also.

So these were the first programming languages used in the industry to write artificial intelligence programs. These languages can be learned from various big data and machine learning courses. Organizations like Imarticus Learning are doing an excellent job in equipping people with the knowledge of these programming languages by their A-listed courses.

Take That Next Step Towards a Rewarding Data Science & Analytics Career With These Analytics Courses

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Take That Next Step Towards a Rewarding Data Science & Analytics Career With These Analytics Courses

Are you interested in completing a data science course in India but don’t know where to start? Then you’ve come to the right place as we’ll discuss the top data science courses in India and learn how they help you start a career in this fast-growing industry.

All of the courses below have reasonable data science course fees and you can choose according to your requirements and aspirations.

Data Science and Analytics CareerWe’ll discuss the data science course details of our programs in the following points:

Post Graduate Program in Analytics and Artificial Intelligence

Our post-graduate program in analytics and artificial intelligence is among the most popular data science courses in India. We offer this program with UCLA Extension. It is a data science course with placement assurance which means you will get access to our dedicated placement support to our private placement portal and additional services.

The program gives you a dual certification from UCLA Extension and Imarticus Learning. UCLA Extension is one of the oldest and largest higher education providers in the United States. Some of the key concepts you’ll study in this online data science course in India are Machine Learning Algorithms, Deep Learning, Computer Vision, and many more.

Machine Learning and Deep Learning Prodegree

Machine learning refers to the field of developing computer solutions that can perform tasks and learn from them without requiring human intervention. Our Machine Learning and Deep Learning Prodegree will help you learn the required skills to enter this field as a skilled professional.

We offer this program with IBM. The course teaches you machine learning, Python, IBM Watson, and deep learning through 16 in-class and industry projects with a Capstone project as well.

Post Graduate Program in Data Analytics

Our Post Graduate Program in Data Analytics teaches you data science from scratch. It is among the best data science courses for beginners as it covers all the required concepts.

You will learn the foundations of data science and its in-demand tools including Python, R, PowerBI, Tableau, Hadoop, SQL, and Spark. Like our other programs, it is a data science course with placement support to help you start your career right away.

Data Science Prodegree

We offer our data science prodegree with KPMG. The program is industry-aligned and teaches you the most in-demand skills in the industry. You will work on real business case studies and receive project mentorship directly from industry experts.

This online data science course in India teaches you SQL, programming, Tableau, statistics, R, Python, and many other important concepts. You will also work on a KPPG in India Capstone Project by the end of this data science course in India.

Conclusion

Starting a career in data science and analytics is quite simple. All you need is a little effort, commitment, and guidance and the rest is easy.

Now that you’re aware of our data science course details, you can start your learning journey right away. You can find out more information on our data science course fees and eligibility criteria on our website.

Career in Machine Learning – Check Job Profiles, Top Courses and Colleges, Fee Structure

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Professionals in the sphere that is Machine Learning are very important and sought after in the Information Technology Industries across the globe. Through Machine Learning, human work has reduced significantly and boosted efficiency.

Machine learning has also helped reduce errors and a large number of companies have begun automating their systems. Business systems are using machine learning training to reduce costs and improve productivity along with performance as a whole.

Data scientistThere are a variety of career parts to pursue in the sphere that is Machine Learning and the positions offered are very rewarding.

Career Paths for Machine Learning Aspirants

  1. Software Engineer: Software Engineering aspirants need to know the nitty-gritty of code writing fluently as candidates will be needed to create code that would support the creation of specific algorithms. Using principles from engineering with computer science within mathematics from an engineering degree, designing and developing software is what computer software engineers are responsible for.The candidate for these jobs is required to have skills in listening to and understanding clientele requirements on a more detailed level. Along with this, they are also required to create a system in accordance with clientele parameters.
  2. Software developer: The job of a person in software development entails creating flow charts to assist coders in their work. Software developers are known to be the true brains behind any computer program. They are responsible for creating models, illustrative representations, strategic groundwork, and plotting out the required working of a complete system. They are required to test machinery and look at the working of each component.
  3. HTML Designer: These designers are involved in the creation of software for various social media platforms, big online stores, and banks as well. In banks the designs they put into effect help in increasing the number along with the efficiency of bank transactions that are managed and done online including those done electronically.
  4. Data Scientists: Involved in the procedure of analysis, they are responsible for utilizing data to find out vital information using inspecting and modeling processes.
  5. Computational Linguistics: The job of a computer linguist holds candidates responsible for helping computers in understanding spoken languages and to constantly improve currently existing systems.
  6. National Language Processing Scientist: The people in this position are needed to do the designing and development of machines and also applications that can learn patterns and translate various words imputed by a speaker to various other languages.

The demand for professionals in the area that is machine learning is growing every day. Various other career paths in this area include Data Analyst, Cloud Architect, Intelligence Developer for a Business and also Data Architecture.

Best Courses and Facilities for Training within India

The top 3 machine language courses available in India are listed below. Students,

professionals in analytics and also data scientists pick the finest programs to increase their skills and improve themselves.

  1. PGP in Machine Learning & Artificial Intelligence offered by IIIT-B
  2. Offered by IIT in Hyderabad is Fundamentals of ML
  3. PGP in Artificial Intelligence along with Machine Learning

Colleges Offering Machine Learning Courses

The various colleges offering machine learning courses in India are listed as follows:

  1. Indian Institute of Technology, Hyderabad
  2. DY Patil International University
  3. University of Petroleum and Energy Studies
  4. Jain University, Bangalore
  5. Sharda University
  6. Indraprastha Institute of Information Technology
  7. Vellore Institute of Technology
  8. SRM Institute of Science and Technology
  9. Dehradun Institute of Technology University
  10. SVVV (Shri Vaishnav Vidyapeeth Vishwavidyalaya)

Job Opportunities in The Field of Artificial Intelligence in This Pandemic Time!

Posted on by Imarticus Learning
Reading Time: 3 minutes

To become an Artificial Intelligence (AI) professional, you need to have practical problem-solving skills, logic, communication, and analytical skills. AI is made to create computer programs that can achieve goals and solve a problem better than humans. With lesser mistakes and emotions to hinder the work, AI gives better and jan efficient output.

The scope in AI is vast. You can get into robotics, gameplay, language detection, machine learning, computer vision, speech recognition, and many more.

Some of the factors that characterize a great career in AI are as follows:

  • Robotics
  • Use of sophisticated computer software
  • Automation

Math, technology, engineering, and logic are some of the specific fields that individuals have to specialize in if they are considering a job in this field.

Along with this, learning science including physics and computer studies is beneficial.  Considering the computational approach to AI, knowing the technical, as well as physiological knowledge of the system, is immensely helpful. Knowledge of primary machine language is a must. There are many other courses that you can do to get into the world of AI like, Machine learning.

Data Science Online CourseMany institutes like IIT provide machine learning courses, there are other institutes that provide these courses online and then there are certification courses that you can take up in private institutions.

Some of the career opportunities in AI

  • Robotic Scientist

Robots are gradually taking over the industrial worlds. There is lesser workforce and more robots. To help create such robots that can solve problems as a human would, we need engineers or programmers. For a career in Artificial Intelligence field, a master’s in robotics engineering and having a license from the state can be of help.

  • Software Engineer

In every phone that is there in the market, there is an option for face recognition or finger print recognition. Many companies, including big businesses, security companies, casinos, etc. have face recognition and fingerprint recognition to understand the people who use their services. Hence being a software engineer is one of the opportunities here.

  • Game Programmer

To keep the players challenged and highly anticipated, every gaming company requires candidates that are well known with the basics of AI and can design games that can keep the players engaged and interested.

  • Search Engine Manager

Many big companies, like Google, pay a massive amount to candidates with an AI degree to manage their massive search engines. Many may search for various things on Google, but Google search is able to predict the search even when there are spelling mistakes or grammatical errors. This is done with the help of knowledge and the study of artificial intelligence.

  • Government Sector

There are jobs not just in the Private sector, but there is an intense need for candidates with a degree in AI in the government sector too. The pay is high, and along with that, the amenities provided are even better.

Conclusion

The scope of artificial intelligence is vast. Having a master’s degree or a doctorate is the best if you are looking for a long term job in the field of AI.

The demand for people with knowledge of AI is strong. Companies like Google, Apple, etc. are always on the lookout for candidates who can take the world of AI to another level. The choices are plenty, and the income from working in such a field is high.

‘Eve’, a robot created by the scientist at the University of Manchester, Cambridge, discovered that a common ingredient found in toothpaste is capable of curing malaria. This event, itself, can show how much this field has grown, and the job possibilities are endless.

How Statistics Relate to Machine Learning?

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Introduction

Machine learning and statistics have always been closely related to each other. This led to an argument about whether it was different from machine learning or formed a part of machine learning. Several Machine learning courses specify statistics as one of the perquisites for machine learning.

Hence, we need to develop an understanding of the fact if statistics relate to machine learning and if it does, how?

Individuals working in the field of machine learning concentrate on the task of model building and the result interpretation from the model that was constructed while the statisticians perform the same task but under the cover of a mathematician concentrating more on the mathematical theory involved in the machine learning task concentrating more on the explanation of the predictions made by the machine learning model. So, we can say that in spite of the differences between statistics and machine learning, we need to learn statistics in machine learning.

Statistics and machine learning

Both statistics and machine learning are related to data. Although they work with the data in their way, some requirements are needed by both and hence they form a close relationship with each other. Given below is a step by step analysis as to how statistics relate to machine learning.

Data preprocessing requires statistics

To proceed with the machine learning task, cleaning of data is a mandatory step. This process involves tasks such as identifying missing values, normalization of the values, identifying the outliers, etc. These operations call for statistical concepts such as distributions, mean, median, mode etc.

Model construction and statistics

After the data has been cleaned, the next step is to build a model with that data. A hypothesis test might be needed for model construction which calls for good statistical concepts.

Statistics in evaluation

Model evaluation requires tasks such as validation techniques to be performed so that the accuracy and model performance increases. These validation techniques are easily understood by the statisticians but a bit difficult for the machine learners to interpret as it involves mathematical concepts.

Presenting the model

After the successful construction and evaluation of the model, the model is presented to the general public. The interpretation of results requires a good understanding of concepts such as confidence interval, quantification, an average of the predicted results based on outputs produced and so on.

Other than the above-mentioned steps some additional concepts must be adhered to while working with machine learning. Some of these concepts are listed below:

  • Gaussian distribution – It is often represented by a bell-shaped curve. The bell-shaped curve plays a very important role while normalising the data as a normalised data is supposed to lie at the point where the bell-shaped curve is divided into two equal parts.
  • Correlation– It can be either positive, negative or neutral. A positive correlation indicates that the values change in the same manner(positive causes positive and negative leads to negative). A negative correlation indicates values change oppositely while neural suggests no relationship. This concept is of great importance to the analysts while identifying the tendencies in the data.
  • Hypothesis- An assumption might be done for the elementary predictive analysis in machine learning that requires a good understanding of the hypothesis.
  • Probability – Probability plays an important role in predicting the possible class values in classification tasks and hence forms an important part in machine learning.

Conclusion

Statistics is of huge importance to machine learning, especially in the analysis field. It is one of the key concepts for data visualization and pattern recognition. It is widely used in regression and classification and helps in establishing a relationship between data points. Hence, statistics and machine learning go hand in hand.

How AI and Big Data Can Be Used to Fight Against Coronavirus?

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COVID-19, a deadly virus that originated from Wuhan, China, has been declared as a pandemic by the WHO lately. The whole world is in quarantine to stop the spread of the on-going pandemic to further extend. The world has united to fight against the common cause. The results are most anticipated from the AI and Big Data to sustain through this so uncalled time.

Artificial Intelligence training is already helping many countries to fight against coronavirus and executives from Amazon, Google, Microsoft, and Apple met officials at Downing Street recently to discuss their role in the Coronavirus crisis. It is no secret that “Data” is the new gold; it is no less than a miracle that even on such a large scale shutdown of the economy the countries are doing well in providing necessities to the citizens. It is done by proper modeling and tracking of data.

What is modelling and tracking data?

Machine learning (ML) an advanced version of AI, has come to play a significant role in fighting CONVID-19. Five years ago, many were asking whether these models could be used to optimize corporate performance but now is the time when these models are helping daily to fight against coronavirus. Tracking the data using parameters and altering the matrix could come in handy in maintaining the resources and handling the outbreak in a more optimized way possible.

How to use the available resources to fight against the coronavirus?  

Countries like South Korea have an advanced digital platform for big data mining and they are already running government-run big data platform that stores citizen information and monitors foreign nationals and integrates all hospitals, government organizations, final institutions, and all other services too.

AI and Big Data have surely revolutionized the approach to fight the outbreak. Tracking and forecasting the path of infection and detect the most infected area to send instant help to limit the spread.

The difference is the quality of the data

Pumping huge amounts of data into AI and machine -learning systems is no guarantee of success and it makes it difficult to ensure that people focus on relevant information and not get mislead by hysteria. A recent update by Facebook stated the concern about the public reaction on the outbreak. They told us that they are monitoring people’s response to this outbreak and detecting the most affected areas across the world. This has come to be of great help in monitoring the outbreak on a global platform.

Using fresh data in these circumstances is of high priority as early detection of the virus can save other people from getting infected. Many countries have also introduced a quick reaction team and total isolation chambers to limit the contamination. Many drive-through labs are operational where you can get your results while sitting in the car and get treatment instantly if infected.  AI and Big Data-based start-ups are busy in making thermometers which can detect CONVID-19 at early stages.

Finding the cure using AI and Big Data Analysis

Exscienta, a British start-up became the first company to test AI-designed drug molecules on humankind. There are some limitations in finding the cure as it takes a long time to study the pattern and create algorithms

Conclusion

AI and Big Data have surely revolutionized the campaign again coronavirus in all aspects possible be it keeping the people comfortable and safe in quarantine or let it be the fight against coronavirus on the front ground and it’s no wonder why AI and Big Data analytics is booming globally and many companies are shifting their focus towards this upcoming mega technology.

Is Statistics Required for Machine Learning?

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Reading Time: 2 minutes

What is Statistics?

Statistics is a branch of mathematics that is used for comparing and analyzing various data points and the numbers associated with them. It also includes the study of numbers and drawing out insights from those numbers. Some of the statistical measures include average, median, mode, variance, standard deviation, correlation, regression, etc. Some of these help in analyzing single sets of data while others are used in comparing two or more sets of data and then making a comparative analysis in the form of trends and patterns. Often these tools are also brought into play when it comes to predicting future numbers.

What is Machine Learning?

Machine Learning is the application of artificial intelligence where the systems are programmed to perform a specific set of tasks. The computers are programmed to function automatically depending on the various scenarios and come up with the required results. It enables the analysis of huge data for drawing out various business insights.

Also, it makes the sorting and analysis of data quick and easy as the automation is brought into play with the help of machine learning. It is a really powerful tool in this data-driven world of today. It collects data from various sources as given by the algorithm, prepares it for analysis and then evaluates this data for bringing out insights and also throws light on various performance indicators in the form of patterns and trends.

Statistics and Machine Learning

Both Statistics and Machine Learning deal with the analysis of data therefore one could guess that the two areas are interrelated. Various statistical methods are used to transform raw data and bring out various results. Many believe that knowing Statistics is a prerequisite for understanding Machine Learning. Statistics is important as the data sets have to be created which can be easily made if one has prior knowledge of Statistics. Also, with the help of statistics, the observations are transformed and put to good use.

Machine Learning has a deep relation with Statistics and the elements of statistics such as the collection of data, classification, and sorting of data, analysis of data, etc. Predictive modeling can be done by someone who at least has a basic understanding of Statistics. Machine learning is also known as “Applied Statistics” as it practically uses various statistical theories and principles to drive growth and various results.

Data analysis is important for machine learning and statistics is an art of handling data. It is the primary skill that drives machine learning algorithms. Statistics plays a very important role when it comes to machine learning. One needs to know about the various parameters on which the data shall be analyzed to bring out desired results.

Methods such as Correlation and Regression are often used to compare various sets of data and these tools are built into algorithms with the help of machine learning so that these numbers of comparison can be automatically calculated and a comparative study can be made based on these numbers. Learning Statistics before getting into machine learning is the best way to go about it. Various Machine Learning training will also give you an idea about statistics and how it is applied to Machine Learning.

Conclusion
Machine Learning and Statistics are two parts of the same coin. Machine Learning makes use of statistics for sanitizing data and on the other hand, Statistics is given a practical shape and is made applicable with the help of machine learning. Therefore, it becomes easy to conclude that one must have at least a basic understanding of statistics to understand the aspects of Machine Learning.

What Are The Major Fields Of Robotics?

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Before we jump into the major fields of robotics, we should have a clear idea about the term robotics. It is a diverse field related to construction, engineering, and operation of robots in many commercial industries and consumer fields. Robotics involves the study of a physically constructed technology system and its performance or role in any interface or new technology.

The fields of robotics

There are five major fields of robotics namely: 

    • Operator interface – This refers to the Human-Robot Interface that explains how the human and the robot communicate. The robot works based on the commands conducted by the human. The best example of this is a child playing a video game. The joystick here acts as the interface between the human and the machine.
    • Locomotion based robots – In this case, robots perform tasks based on locomotion. Here, you will at times find human-like robots using legs for moving. Some flying robots and drones use the propellers for movement. Some may even use wheels depending on the environment they are i.e. air, water or land.
    • Component-based robots – In this case, it is the component within the robot that makes it do the specified job. Based on the situation, these human-like robots may use mechanized arms and fingers, claws or pushers to conduct the job. This is especially required in industries that are into heavy lifting and moving of things.
    • Ways of delivering a message to the robot – Commands need to be given to the robots with the help of different means. Today there are over one thousand programming languages and each robot interprets the given instructions in their own way. Some robots are even modified so that they are able to adapt to their changing environment.
    • Based on how robots to sense and to perceive – This denotes how the machines identify things in and around their environment and react. For example, when a robot comes in contact with an obstacle, what direction should it take. This component is fed into the robot that helps them make the right decision.

Role of machine learning in robotics

Robot learning is a field of research that connects both machine learning and robotics. Machine learning training helps robots deal with dynamic interaction and adapt them to situations where they can avoid obstacles and maintain productivity. Machine learning is often used by computer vision algorithms when it comes to robotics and these applications are normally not referred to as “robot learning”.

The future of robotics

This rapid advancement in the robotics sector in India will ultimately affect the job market and become a major cause of unemployment. Due to the huge possibility of robotics in warehousing, the Indian warehouse automation market is anticipated to grow at a CAGR of 10-12% in the period 2015-2020.

The future of the professionals working in fields like automation is also uncertain, with the new technological development. It will also lead to layoffs and downsizing of the employee strength. Some areas will not be affected immediately, but in the near future, they will be impacted in some way or the other. Robotics is also expected to play a key role in the “Make in India” initiative and attract global manufacturers to invest in the country. Hence, we can see that the future of robotics has both positive and negative impacts.

Conclusion

In order to build a fully functional robot, all these major fields of robotics should be carefully incorporated to ensure maximum effectiveness. Proper incorporation guarantees that the robot would work at an optimum level without any glitches.