How to Get a Job in Machine Learning Technology

You have completed your engineering degree and are now looking for a break from the extremely slow labor market. Unlike ten years ago, when engineering studies inevitably led to comfortable employment, the focus on the job market has shifted from traditional jobs to technology-driven roles. As more and more companies use artificial intelligence technologies and change virtually every industry, there is a demand for engineers and data scientists who can work with a variety of learning platforms and languages. Automation has increased significantly.

The artificial intelligence and machine learning market is expected to reach $ 8.8 billion in 2022. Unfortunately, companies find it difficult to find people with relevant experience. This has led to a huge deficit in the number of qualified machine learning (ML) developers. In this article, we look at some factors that can help you become a sought-after machine learning expert.

How to become a Professional machine learning engineer

Learn the require Languages

If you need a career in machine learning, it is important to have a good command of programming languages ​​such as C ++, R, Python, Java and SQL. Among these, Python and R are the most popular programming languages ​​for machine learning and are often a prerequisite for most machine learning courses.

Solid Knowledge of Data Modeling

You don't have to be a data scientist to become a machine learning expert. However, you should learn about data modeling and evaluation to identify and analyze unstructured data models. Machine learning engineers need data modeling to find data models, predict the properties of invisible instances, and determine the level of precision / error.

Solid knowledge in Statistics Skills

A good understanding of statistics and probability is the cornerstone of machine learning. Statistical concepts such as mean, standard deviations and Gaussian distributions are just as necessary as the probability theory for algorithms such as naive bays, Gaussian mixed models and hidden Markov models.

Learn ML Algorithms

Machine learning professionals must have a solid understanding of the theory of algorithms and how they work. This includes knowledge of topics such as partial differential equations, convex optimization language, quadratic programming and more.

Other technical skills that a machine learning expert must master are UNIX tools and advanced signal processing techniques.

While mastering technical skills is extremely important, it is also important to have good general skills. Machine learning professionals must have good expertise, excellent communication and problem-solving skills. Most importantly, a machine learning engineer has to keep up with the rapidly changing technology. As new technologies and paradigms explode on the scene, you need to stay up to date and improve your knowledge regularly. This could be achieved by subscribing to online courses, subscribing to the latest technology blogs, and regularly following research articles.

According to Analytics India Magazine, around 78,000 jobs in data science and machine learning were vacant in India in 2017. If you master the key skills of machine learning, you will find great opportunities in this area. Given the rapid growth of AI and machine learning, no matter what sector you work in, new-age technologies will soon affect your work if not. not finished yet. For this reason, it is important to improve the skills to follow these revolutionary trends in order to remain competitive in the world managed by AI.

Read More: 7 tips to get success in machine learning

Machine Learning Job Roles

There are various roles available under machine learning technology. Let’s discuss about those roles:

Machine Learning Engineer

Machine learning requires the knowledge of programming and computer science background. As a machine learning engineer you must be able to play with algorithms, data sets and coding part.

Data Scientist Role

Data scientists have a high degree of mathematical and IT know-how. With regard to ML, they both rely on data records at the same time. The art of data science combines theory and statistics.

These two work together to behave in a certain way towards large amounts of data. Using algorithmic processes, they extract information from data.

Artificial Intelligence Role

AI is known as computer intelligence and is the ability of machines to be intelligent. This intelligence uses arguments that are similar to human intelligence. Everything is based on theory.

But the goal is to make the machines work, learn and think like people.

Software Developer (ML)

Software engineering is similar to ML engineering in terms of basics and programming.

The exception concerns the deepening of computer science in software development through the design of systems. These engineers design, build, teach, and experiment with ML models.

Conclusion

I hope you have understood that how can you get job in machine learning. And to get job in machine learning what skills you must know. So follow this article and prepare for machine learning engineer. You will definitely get a good job in this technology.

NearLearn is the best institute that provides the best machine learning training in Bangalore. It provides other courses also like artificial intelligence, data science, blockchain, deep learning, and full-stack development etc.

How to become successful React Native Developer?

React Native, a framework for building cross-platform applications, recently made headlines for the right reasons. It is supported by a renowned team from Facebook and the entire JavaScript community. The framework aimed to achieve record heights with the slogan "learn once, write everywhere". After Facebook created React Native Open Source, there was a lot of discussion about whether cross-platform applications could be built using JavaScript. Most developer communities were unsure whether building applications for two types of platforms using JavaScript was a good solution. However, React Native surprised everyone with its exceptional performance, which is comparable to native applications (iOS / Android). However, since it was a relatively new framework, developers had many performance issues when building complex applications.

We'll look at six key tips to help mobile developers become better React Native developers.

If you are planning to become a react native developer and want to make future in react native development field then follow these 6 steps that will help you to become a react native developer. Here I am going to share a guide to make your react native developer dream.

 

Correct choice of Navigation Library is important from the start

React Native has a long history of pain and discomfort related to navigation. At the beginning of version 0.5, many navigation libraries were published and outdated, but only a few managed to maintain the effectiveness and usability of a native application. However, there can be many situations in which developers may find that the navigation library used in their application is not helpful for a better user experience. One possible example of such a use case is Airbnb, which has found that React Navigation - the recommended navigation library for React Native - does not work with its Brownfield application. For this reason, Airbnb developers have created their own navigation library, which is the second most used navigation library after React Navigation.

Read More: Why Corporate Training is Important for the Business

Focus on JavaScript

You must learn first react before interacting with react native. Note that without having knowledge of JavaScript, it is very difficult to learn react. So you first step should be to learn JavaScript. Then you have to go for react native. So first step focus on learning JavaScript then go to react native.

Learn Basic Fundamentals of ES6+

After you master the basics of JavaScript, you should start learning the latest version of the ECMAScript standard. While you may not need to use ES ^ + in React Native, this is an innovative and easy way to use JavaScript. You can work optimally while creating responsive native apps. ES6 + (ESNext) is equipped with a number of functions and syntax that make your coding experience much more convenient and easier.

Focus on React

After you become familiar with the JavaScript and ES6 + ecosystem, you can continue learning React. First, discover React Js, which offers the same interface as React Native. It is advisable to learn it from one or two tutorials to master the basics from different angles.

Focus on React Native

Once you understand the basics of using React, you should start using React Native now. You have access to a large number of free or paid online sources to learn how to respond to Native. Do your research and find the ones that will enable you to gain a good understanding of React Native.

Create Basic React Native Apps

On your way to becoming a responsive native developer, you will face a number of challenges. The transition between learning the code and actually building on React Native can be difficult and exciting. You need to find the right resources to learn how to develop on Reaper Native.

State Management

State management is a very important aspect in any serious application. If you are familiar with the development of mobile applications, you know that every component of an application has its own status. When building small applications, managing reports is not that difficult if you can manage reports using accessories. On the other hand, a real-time mobile application requires that your status be fully accessible throughout the application. Redux and MobX are two popular status management libraries for React Native.

After learning how to develop a simple application in React Native, you need to prepare for some complex application scenarios. As your applications grow and become more complex, you need to choose a reliable architecture that leaves room for scalability and helps maintain problems in the future. Redux will help you here.

Conclusion

I believe that you have understood the 6 steps of becoming a react native developer. If you follow these steps then definitely you can also develop react native apps and become a successful react native developer.

NearLearn is the best institute that provides React native online training in India. It provides other courses as well like machine learning, artificial intelligence, data science, blockchain and full-stack development.

How Ai Helps fight corona virus outbreak?

The COVID-19 epidemic (coronavirus) has affected most countries in the world. The number of deaths and deathbed patients increases from year to year. Since its first fall in China, the coronavirus has found its way into countries that are generally considered to be concerned about their health.

And to stop this epidemic, there is currently no vaccine or antidote that guarantees 100% results. However, modern technologies have covered us with various aspects that offer excellent services and help to combat this situation and improve over time. Yes, data science technologies destroy misunderstandings and offer excellent services for the search for coping mechanisms.

How Is AI Currently Helping Helm a Solution to Corona Outbreak?

Artificial intelligence helps to find the countermeasures that can stop the coronavirus epidemic. By analyzing the data available, it becomes easier to predict many factors and aspects that can help address this global situation.

1.      Detection

Coronavirus is not that easy to spot. However, a company called Infer vision has released a corona virus detection mechanism that can further identify and prevent the virus epidemic by analyzing patient conditions. This AI-based program helps to identify and prevent the corona epidemic.

Healthcare providers can rely on this software solution to predict corona infection, and it helps manage countermeasures because it can detect and isolate symptoms to effectively stop the epidemic.

2.      Robot for Sterilize Everything

During the coronavirus epidemic, hygiene is the most important aspect to counter the attack of the virus. Therefore, it is possible to assign robots to sterilize everything that comes in contact with people, everything that is used during the day. The sterilization process includes food, clothing, utensils, medical devices and much more.

Because robots are protected from the worry of virus infection, the work can be done more efficiently. In addition, robots can work without taking a break or anything else that an average person needs. You can therefore rely on these systems. These types of robots are already used to prevent infections in this way.

3.      Drone Helps in Delivering Medical Supplies

Artificial intelligence and machine learning may not be directly involved in the delivery of drones, but they make an important contribution to the automation of these processes and to an excellent delivery experience. And now that contact or human contact has become dangerous due to the risk of corona infection, drones can provide efficient delivery services.

Because medical vendors can receive orders on their special channels, they can use efficient drone delivery systems to provide delivery services. For example, if a medical provider has installed and registered a Gojek clone app, health services can order the consumables they need to get the drug without having to contact people in between. It helps prevent hygiene and supports prevention requirements.

4.      Track Outbreaks of Future

The likelihood of a corona outbreak increases with each passing day. But no measure can help prevent this epidemic. However, what we can do is predict the opportunities and deliver great services based on time and place. And AI is leading the way in effectively predicting and tracking these epidemics.

But how? By analyzing social media platforms, the latest news, updates and official government statements and much more, you get all the resources that give you a signal in advance. It becomes easier to predict epidemics in order to deploy the coping mechanism in time. In times when the spread of the COVID-19 virus spreads over us, it is important to take our safety and precautions into account.

5.      Drugs Delivery and Development

It will be much easier to develop and deliver drugs with smarter systems that help achieve the results you need. With the help of multiple supercomputers and the ability to adapt quickly to changes, AI systems can help achieve great results.

Regardless of whether you want to make a copy of DNA to test for possible vulnerabilities or to speed up the drug development process, these systems are just as effective. With all the data available, it becomes easier to perform each operation as planned and achieve excellent results.

For example, Insilico Medicine was able to identify possible molecules that could change the effects of the virus on our body and deliver excellent results. This helps to speed up the development of the possible remedy and shows how an average person can take a little longer than usual.

The possible cures for this dangerous virus have yet to be found. Data science technologies, particularly AI, can create a better world, where every process is monitored and supported by intelligent systems to achieve results over time.

Read More: Fighting Corona virus using AI, Data Science and Machine Learning

Summing up

Fighting this global crisis that we are facing has become crucial. While artificial intelligence feels obliged to provide support in all possible forms, the world is convinced of positive results.

By following the guidelines of doctors and the WHO (World Health Organization), we can also support this technology and its processes. The best way to cure and stop this disease is to follow the measures mentioned and live a healthy life.

Because scientists have always found a way to address these global threats, we need to understand and be patient. Data science technologies help us build a better world and define coping mechanisms to combat the global threat posed by the spread of COVID-19.

Conclusion

I hope you have understood how Ai technology is helping in fight corona virus outbreak.

NearLearn is the best institute in Bangalore that provides Online Artificial Intelligence Course. It provides online and classroom training as well. It provides other courses also like machine learning, data science, blockchain, full-stack development etc.

What is the Average Python Developer Salary

In today's fast-paced world, Python offers better salary and growth opportunities than other programming languages. Given the various factors that affect it, here we have tried to explain the content of the Python developers in this blog.

Given the popularity of Python, it may be time to familiarize yourself with the average salary of Python developers based on their experience, location, skills, etc. This blog about "Python Developer Salary" will help you understand the main aspects to decipher on which salaries are based. You will also learn whether or not to learn this lively and dynamic language.

What Does Python Developer work?

Python developers are usually responsible for writing the server-side (web) application logic. This includes developing core components, linking applications to third-party web services, and helping front-end developers integrate their work with Python applications.

Although web development and data analysis are still the main applications of Python, language is a big step in the area of ​​machine learning. This comes from several salary reports that show that a Python developer deserves a lot more in the field of data science.

Geographic Based Python developer Salary

Although the average base salary of a Python programmer is high, this is not the only reason for its popularity. There are also many other factors that contribute to its popularity.

Technology giants around the world love it. NASA, Amazon, Google, Facebook, YouTube, etc. are just a few of the big names in the technology world that use Python for several reasons and are still looking for Python engineers.

New York: The average Python developer salary in New York, US is $ 132,598 / year.

California: In California, Python developers have an average annual salary of $ 138,466.

San Francisco: Python developers in San Francisco have an average annual salary of $ 143,476.

Virginia: The average salary for a Python developer in Virginia is $ 108,649 per year.

Average salary of python developer in India is 489,514

Read More: Why corporate Training is Important for Business

Salary in Python Programming Compare to other Programming Language

According to Stack Overflow, Python was the hottest technology in 2018. According to the latest Stack Overflow annual report, Python 2019 ranks third in the list of most popular computer skills. At the same time, Python is one of the most popular more popular technical skills, demand exceeds its supply. Therefore, Python can open many doors for you.

Let us now consider the content of a Python developer compared to other languages.

In the US, Ruby on Rails developer salaries are $ 122,149 a year.

The average Java Developer salary is $ 103,460 per year.

Perl developers have an annual salary of $ 121,428.

C ++ developers have an average annual salary of $ 114,148.

The average annual salary for JavaScript developers is $ 113,730.

.NET developers have an average annual salary of $ 93,714.

PHP developers average an average of $ 83,925 a year.

Python developers have an average annual salary of $ 118,124.\

Source: indeed

Python developer salary Based on Experience

There are currently around 25 million software developers worldwide. According to SlashData, nearly 8.2 million developers use Python, and now that number has surpassed the Java developer population (7.6 million).

Here's a Python programmer's salary based on experience.

Entry-level salary for Python developers: The average starting salary for Python developers ranges from $ 59,888 a year for first-time software developers to $ 111,605 a year for full-stack developers.

Python Intermediate Programmer Salary: The average annual salary for Python Intermediate Level developers is $ 117,940.

Senior Python Developer Salaries: Average senior Python developer salaries range from $ 132,789 a year for full-stack Python developers to $ 145,923 a year for advanced software developers.

 

Why Should You Learn Python Programming?

After you fully understand a Python developer's salary, you should know what you can do with Python and how you can make a career there.

Here are some short overviews:

It is widely used by companies because it is powerful and simple

Its simplicity and clarity make it ideal for beginners

Due to high demand, it offers excellent career opportunities, particularly in the United States and India

It has a number of frameworks to make website development as easy as possible

There is a large community that continues to contribute to its development

It is considered the best for artificial intelligence (AI) and machine learning (ML).

Python has already replaced Java as the second preferred language for GitHub

With Raspberry Pi you can create our own Python craft!

Conclusion

Now you have understood that the average salary of python developer. If you are also planning to make career in python programming language then start and learn python programming now.

NearLearn is the best institute that provide best online python course in India. It provides other online courses also like artificial intelligence, machine learning, ReactJs, react native, blockchain, data science and full-stack development etc.

What is the Block chain Developer Salary in India?

The blockchain era is here and now. Companies from different industries are familiar with the concept of the decentralized general ledger. There is a worldwide wave of blockchain adoption by companies and businesses to solve basic business problems. In fact, the advent of block chain technology is happening so quickly that Gartner predicts that the commercial value of block chain technology will exceed $ 3 trillion by 2030.

According to a 2018 PwC survey of 600 executives from 15 In different regions, almost 84% of respondents said they used blockchain in one way or another. India is also catching up with the upward trend in blockchain adoption. Given the growing interest of state and private companies in blockchain, the labor market in this area is currently booming.

The demand for Blockchain Developers in India

As an emerging technology that has only recently gained a foothold in the past few years, blockchain talent is hard to find. Blockchain is one of the fastest-growing skills today. Jobs in this area are increasing by an astonishing 2000 to 6000% and salaries for blockchain developers by 50 to 100%. Higher than traditional developer jobs. Although there are many vacancies in blockchain, the talent pool in this area is limited. The demand for blockchain technologies, especially blockchain developers in India, is generated not only by the BFSI sector but also by healthcare, education, supply chain management and IT cloud, stock trading, real estate, and even government agencies.

The most sought-after blockchain capabilities are hyper ledger, Solidity, Ripple and Ethereal. However, since this area is relatively new, companies are often content with professionals with specific skills. For example, blockchain developers must have basic knowledge of mathematics and algorithms. You should be familiar with C, C ++, Java and Python as most blockchain projects are written in these languages.

In addition, blockchain developers need to know at least some of the tools required for blockchain development, such as Geth, Remix, Mist, Solium, Parity, BaaS, and Truffle, to name a few. They should also have experience working on open source projects. Usually, most companies hire blockchain developers with at least a bachelor's degree in math or computer science.

Overall, a blockchain developer must have solid technical training and always be curious to learn new technologies.

Read More: Why Corporate Training is important for Business

Average Salaries of Blockchain Developer in India

Due to the lack of talent and skills in this area, employers are always willing to pay blockchain professionals a high salary if they are worth it. In fact, a blockchain technician's salary is much higher than that of an average IT professional. If you have the right blockchain skills, you can double or even triple a software developer's salary in a year.

As more and more Indian companies and organizations join the blockchain train, the average annual salary of a blockchain developer in India has a wide range. Typically, a blockchain developer's salary in India is somewhere between Rs. 5,00,000-30,00,000 LPA. As can be seen, the higher your experience and skills, the higher your annual remuneration. The salary package also depends on whether a candidate has advanced certifications or not (job level, mid level, senior level).

In addition, blockchain job wages are very dynamic. For example, if a professional has three years of blockchain experience, the annual remuneration can reach Rs 45,000,000 or more. This is more than twice as much as a professional with five years of work experience (but no experience in blockchain technology).

High-level salaries for technological positions (without blockchain expertise) ranged from 1.5 to 2.5 billion rupees in 2018. As the need for security in various sectors, particularly in the United States, has increased significantly BFSI sector, companies are ready to pay more than Rs. 4 crores for high-level security experts and blockchain technicians.

There is a significant gap in the demand and supply of blockchain professionals in India. Of the 2 million software developers in India, only 5,000 have blockchain knowledge. Public sector banks currently dominate the game and, with around 4,000 specialists in this area (2018), represent the highest demand for blockchain developers compared to 2,300 experts in 2017. This corresponds to an increase of 75% in demand from blockchain specialists. According to TeamLease research, there are around 2,000 blockchain experts in the NBFCs and 2,400 in public sector companies.

Conclusion

However, since blockchain skills are primarily developed and promoted internally by employers, we hope that there will be more talented blockchain professionals in the near future.

NearLearn provides the best Blockchain online Training in India. It provides both classroom and online training to students. It provides other courses also like machine learning, artificial intelligence, data science, Reactjs, React-native, full-stack development, etc.

10 Jobs Artificial Intelligence will replace by 2030

With artificial intelligence technologies used worldwide, theorists have pointed out that this would also kill many of the work currently being done by humans.

The emerging technology will eventually replace most jobs that involve "repetitive" and "manual" tasks. In this article, we'll look at the other job profiles that also have the same dilemma.

Huard Smith, who heads Forrester Consulting's financial services practice, said recently that AI's impact on various professions will be profound over the next 11 years. He says that cabin and location related jobs are severely affected by the advancement of new technologies. A common example is the replacement of human workers with robots.

1.      Bookkeeping clerk jobs

With the industrial revolution 4.0, it is very likely that this profession will be completely replaced by new technologies in the next 10 years. For example, Microsoft Office offers a double-entry accounting application called Easy Bookkeeping that allows small businesses to record daily business transactions in newspapers and view accounts in book form. A poll mentioned that there is a 82% chance of automation in the next two decades.

2.      Location Based jobs

Many things have changed due to the development of artificial intelligence. In the current scenario, grocery stores are switching to intelligent ATMs for bill payments instead of cashiers. For example, in the Amazon Grocery Smart Store, Amazon Go, there are no payment counters, and customers don't have to wait to buy something.

 

3.      Market Research Analyst jobs

Experts believe that the job of market research analysts and marketers to be replaced by automation and AI is 61%. In a report from AmazonAWS, some market researchers plan to adjust their role in AI, while 12% of market researchers plan to change their careers outside of market research to protect their jobs from AI.

4.      Retail Workers

Popular retail giants like Walmart have developed new technologies to minimize the time retail employees spend on the most routine and repetitive tasks like cleaning floors or checking inventory on a shelf. In a press release, the company mentioned robots such as Auto-C, Auto-S, Fast Unloader, and Pick-Up Tower, which can be used to clean floors independently, scan shelves in real time, and sort inventory to introduce products. Shelves faster than ever and online orders. However, these companies have not yet fully reported the hardest hit jobs.

5.      Development

Most organizations use artificial intelligence faster. Mass layoffs in IT have already started. Technology giants such as Cognizant, Infosys and Capgemini have announced layoffs at medium and high levels for several reasons. One of them is the emergence of technologies like AI and automation. This helps companies, among other things, to work cost-effectively.

Read More: What is Artificial Intelligence Course Fee in Bangalore

6.      Telemarketing jobs

You are probably already getting robot calls for various products and services, and telemarketing career growth is expected to decrease 3% by 2024. This is mainly due to the success requirements: unlike other sales telemarketers, you don't need a high level of social or emotional intelligence to be successful. Think about it - are you likely buying from a telemarketer? Conversion rates for direct phone sales are generally below 10%, which makes this role a mature option for automation.

7.      Advertising Salespeople

When advertising moves from print and television to the web and social media landscapes, people simply don't have to manage those sales for marketers who want to buy from them. More and more social media platforms are enabling people to buy storage space via free application program interfaces (APIs) and self-service ad markets to suppress the seller and enable users to make money. faster and easier - and this is reflected in a 3% drop in industry forecasts.

8.      Compensation and benefits managers Work

This is surprising as employment growth should increase by 7% by 2024. However, this is not because the demand is immune to automation. As companies grow - especially in multinational markets - a human and paper system can create more obstacles, delays and costs. Automated performance systems can save time and effort to bring benefits to a large number of employees, and companies like Ultipro and Workday are already widespread.

9.      Receptionist Jobs

Pam predicted it in The Office, but if you're not a fan, automated planning and phone systems can replace much of the role of traditional receptionists - especially in modern technology companies that don't do office-wide phone systems or multinational companies.

10. Computer support jobs

The domain is expected to grow by 12% by 2024. With so much Internet content with instructions, step-by-step instructions, and hacks, it's no surprise that companies are turning to bots and automation to answer future questions about employee and customer support.

Conclusion

So I have mentioned some jobs which can be affected by artificial intelligence in 2030. What do you think AI is increasing the jobs or jobs are affected by artificial intelligence? Give your suggestions in the below comment section.

NearLearn is the best online machine learning institute in India. It provides various courses like data science, artificial intelligence, block chain, full-stack development etc.

5 Common Myths about Machine Learning

Machine learning has been the main media coverage lately, and several articles and emotional stories have been published every second. Machine learning is proving to be the most useful, and there is no doubt that we have begun to penetrate commercial work models to make many notable advances such as language translation, speech recognition, recommendation systems, etc. . Indeed, artificial intelligence and machine learning beat our experts on certain complex problems. Ultimately, this advance is, in one way or another, the main motivator for being excited and occupied by reading and researching machine learning.

As we study machine learning and its progress, we are often tempted to believe that there are countless ways to discover machine learning to solve all of our problems and apply it to any situation. But the sad truth is that any organization has not yet fully exploited the BC due to misunderstandings that have arisen around it and that resolve from the first step. Break through the prevailing myths and misunderstandings about machine learning to create more amazing things.

Myth#1 Machine learning will soon pave the way for superhuman intelligence

Well, from the daily headlines about advances in artificial intelligence, we often get the impression that computers will soon take control. Many popular AI films talk about how machines develop their ability to speak, see, and argue, and ultimately leave people in the dust. It is true that we have come a long way in digital advances, and the main reason for recent success is the rise of AI, machine learning, and deep learning, but we still have a long way to go. The machines are super-fast and can do tedious tasks at lightning speed, but they lack one of the most important things, common sense, and no one knows how to teach them.

Myth#2 Both Machine Learning and Data Mining are same

Thousands of articles are published every day about the difference between data mining and machine learning, but it is often confused that it is the same. Data mining is similar to the work of a miner who wins and wins coal, but doesn't know how to make a beautiful diamond ring out of it. Data mining involves digging data to identify unknown properties or patterns. Machine learning is later used to use paving data with specific properties or models to feed machines and obtain useful information. Although data mining and machine learning operate on similar principles, there is a thin line between the two that illustrates the differences.

Read More: Top 12 ways in which machine learning can help your business.

Myth#3 All Machines will start Learning like Human Beings

We see that these lively trends still speak of AI algorithms learning like humans, but the fact is they don't come close to chimpanzee learning. Compare the machine learning process to that of a child. A child shows curiosity and intuitively creates their learning strategy by watching other people walk around and setting their goals, while a machine needs advice and support at every learning stage. In addition, the machine does not have a sense organ to carry out an effective learning process. Therefore, at every step, it must be instructed on how to synthesize and integrate the inputs of several channels such as sound. View and text to understand things. Can you now see how difficult this job is?

Myth#4 Unbiased Result Produced by Machine Learning

As much as we wish, this is not the case. In order to achieve unbiased results, the data fed in internally must be undamaged or not one-sided. If you supply the system with one-sided source data, the results obtained are distorted. We cannot blame the machines for this defect, but it is a limitation for all machine learning experts working on the solution. You shouldn't blindly rely on the analysis, but also make sure that the results obtained are unbiased.

Myth#5 Machine learning Works Just Great Everywhere

Are you ready to spend hundreds and thousands of dollars on personalization if you have financial difficulties managing your business? If cheap human labor is available to do the same job for less than half the money, the machine learning solution won't win the situation here. While it is possible to apply machine learning to small businesses with fewer records, given the cost, only those who use big data services will make headway. It is therefore obvious that machine learning has its limits and we cannot blindly say that it can be used anywhere. However, some initiatives are being taken to overcome this dependency on large amounts of data and enormous costs. We can probably expect more startups to join machine learning in the future.

Conclusion:

I hope you have understood these myths about the machine learning.

Near Learn is the best institute that provides best online machine-learning training in India. It provides other courses also like artificial intelligence, data science, blockchain, full-stack development etc.

Top 5 ways Machine Learning will Impact in Your Everyday Life

Artificial intelligence (AI) and machine learning are now considered one of the greatest innovations since the chip. AI was a bizarre concept from science fiction, but now it's becoming a daily reality. Neural networks (which mimic the process of real neurons in the brain) pave the way for breakthroughs in machine learning, known as deep learning.

Machine learning can help us live happier, healthier, and more productive lives ... if we know how to use its power.

Some say that AI marks the beginning of another “industrial revolution”. While the previous industrial revolution used physical and mechanical forces, this new revolution will take advantage of mental and cognitive skills. One day computers will not only replace manual work, but also intellectual work. But how exactly will it happen? And is that happening already?

Here are 5 ways artificial intelligence and machine learning will impact your everyday life.

1.      Gaming intelligence

Some of you may remember 1997 when IBM's deep blue defeated Gary Kasparov in chess. But if you weren't old enough, you may remember when another computer program, AlphaGo from Google DeepMind, defeated Lee world champion Go in 2016.

Go is an ancient Chinese game that is much more difficult to master than computer chess. However, AlphaGo was trained specifically to play Go by not only analyzing the movements of the best players, but also learning to play the game better by practicing against yourself a million times.

2.      Self-Driving Cars and Automated Transportation

Did you fly recently? In this case you have already seen the automation of the transport to work. These modern commercial aircraft use the FMS (Flight Management System), a combination of GPS, motion sensors and computer systems to track their position during the flight. For example, a Boeing 777 pilot spends an average of seven minutes manually steering the aircraft, and many of these minutes are spent on takeoff and landing.

The jump in autonomous cars is more complicated. There are more cars on the road, obstacles that need to be avoided, and limits to consider in terms of traffic patterns and rules. Nevertheless, autonomous cars are already a reality. According to a study of 55 Google vehicles that have covered a total of more than 1.3 million kilometers, these AI-powered cars have even surpassed the safety of human-powered cars.

The question of navigation has long been solved. Google Maps already provides location data from your smartphone. Comparing the location of a device from one point to another can determine the speed at which the device is moving. Simply put, it can determine slow traffic in real time. This data can be combined with user-reported incidents to create a picture of traffic at a specific point in time. Maps can recommend the fastest route due to traffic jams, construction, or accidents between you and your destination.

But what about the ability to drive a car? Machine learning enables autonomous cars to adapt immediately to changing road conditions and at the same time learn new driving situations. Through the continuous analysis of a flow of visual data and sensors, on-board computers can make decisions in fractions of a second even faster than well-trained drivers.

It is not magic. It is based on the same basic principles of machine learning that are used in other industries. They have input functions (i.e. visual and real-time sensor data) and outputs (i.e. a decision from the universe of possible next "actions" for a car).

Of course, these autonomous cars already exist, but are they ready for prime time? Perhaps not yet, because vehicles currently have to have a driver for safety reasons. Despite exciting developments in this new area of ​​automated transport, the technology is not yet perfect. But give it a few months or years and you will probably want to own one of these cars.

 Read More: Top 15 machine learning with python interview question and answer for fresher in 2020

3.      Taking over dangerous jobs

One of the most dangerous tasks is bomb disposal. Today robots (or technically drones) take care of these risky professions. Most of these drones currently require a human to control them. As machine learning technology improves in the future, these tasks will be completely done by robots with AI. This technology alone has saved thousands of lives.

Another job outsourced to robots is welding. This type of work creates noise, intense heat, and toxic substances that are present in the fumes. Without automatic learning, these welding robots should be pre-programmed to weld at a specific point. Advances in image processing and deep learning, however, have allowed greater flexibility and accuracy.

4.      Banking Innovation

Think about how many people have a bank account. Now also consider the number of credit cards in circulation. How many hours would it take employees to view the thousands of transactions that take place every day? If you notice an anomaly, your bank account may be empty or your credit card may have reached its maximum.

Using location data and buying patterns, AI can also help banks and lenders identify fraudulent behavior as it happens. These anomaly detection models based on machine learning monitor transaction requests. You can spot trends in your transactions and alert users to suspicious activity.

They can even confirm with you that the purchase was yours before proceeding with the payment. It may seem annoying if you only eat out on vacation, but it could save you thousands of dollars a day.

5.      Enhancement in Healthcare

Hospitals could soon put their wellbeing in the hands of an AI, and that's good news. Hospitals that use machine learning to treat patients see fewer accidents and fewer hospital-related illnesses, such as sepsis. AI is also addressing some of the most difficult problems to solve in medicine, such as: B. the ability to better understand genetic diseases using predictive models.

Healthcare professionals previously had to manually review a variety of data before diagnosing or treating a patient. Today, high-performance computing GPUs have become key tools for deep learning and AI platforms. Deep learning models quickly deliver real-time information and, in conjunction with the explosion in computing power, help medical professionals diagnose patients faster and more accurately and develop new and innovative medicines and treatments. Reduce medical and diagnostic errors, predict and predict adverse events lower healthcare costs for providers and patients.

There are other area as well where machine learning will impact in your daily life. Here I have mentioned only top 5 ways. You can observe other areas as well in your daily life.

Conclusion

I hope you have understood that how machine learning is impacting in our daily life. There are other areas as well where machine learning is impacting in our daily life. You can comment area in the below section where you think that machine learning is impacting more.

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How to Get Data Science Job without Prior Experience?

The amount of data that is generated every day is enormous. For this reason, companies around the world convert data into information, thereby optimizing their strategies. The challenge, however, is the fact that every company needs a professional with relevant skills to extract information from the big data, it collects - a data scientist who is now getting a seat at the big table.

With the development of data and its increasing use in different types of companies, people have started to view data science as a super cool job. However, if we want to become a data scientist, we find that many professionals have dozens of MOOC courses and buzzwords on their resume or LinkedIn profile. And when a newcomer to data science sees these portfolios, they feel that data science is not their thing. However, this is not always the case - data science is about solving a real business problem and making the most of the overcrowded data. If you have the appropriate knowledge, you can start your career in data science without prior experience.

You just have to follow these steps

1.      Self-Study

This is primarily to be done if you are starting your journey into data science and have no experience yet. Ask yourself the following questions: Why should a company hire you? If they don't hire you, what could be the reason? What do you know about data science? What else do you need to know about the area? What additional skills do you need to stand out from the crowd?

In addition to the skills and knowledge that data science experts should have, learn about the latest industry trends - how the business works, what roles are currently in demand, what the latest programming languages, etc. Make a list of all the things you know, and you need to know and plan how to do it.

Read More: 7 best free online data science course in 2020

2.      Must-Have Skills

·         Mathematics

It is also considered one of the elixirs of life in data science. This is very important in the field of data science because there are many concepts that help a data scientist use algorithms. In addition, concepts such as statistics and probability theory are essential for the implementation of algorithms. So make sure you put a lot of effort into improving your math skills.

·         Programming Languages

There are many people who would suggest a variety of programming languages ​​to learn if you are aiming for a career in data science. However, don't overwhelm yourself with all the hype discussions. In data science, Python and R are the two most important programming languages. Concentrate on these two languages ​​at an early stage. If you later gain both trust and great trust, you can move on to the next one (Java could be one of them).

To learn how to program, you can take short courses or online courses at any time. Practice a lot too. The more you encode, the better you become an encoder.

•          Communication and Presentation Skills

Mastery of all technical aspects is one of them. However, to be a successful data scientist, you must have exceptional communication and presentation skills. You shouldn't just be a data scientist, but also a data storyteller. Why? Once you get the valuable information from the overcrowded data, your next task is to present it. If you don't have storytelling skills, how can others understand what the information is capable of and the value it would bring?

•          Real-Time Practical Knowledge

Learning and mastery skills are certainly mandatory, but to get the most out of your learning you need to practice - practice with real-time problems and add value to your data science learning. The more you solve these problems, the more experience, and self-confidence you gain and shorten the path to your dream job. There are many hackathons on the internet - you can pick one at any time, participate and see where you stand in this increasingly competitive area of ​​data science.

·         Advice from Leaders

It is always good practice to seek advice from someone who already knows the area. And you can make the most of platforms like LinkedIn to connect with some of the industry leaders.

Another great way to make contacts is to attend data science conferences, where you can not only attend lectures and masterclasses, but also meet many industry representatives to help you take the lead. On track when you start your journey into data science.

·         Accept the Reality

It's no surprise that Data Science is currently one of the highest-paid and most reputable jobs in the industry. And no company would pay someone a respectable paycheck and give it a high-level title until it demonstrates that it is able to solve some of the complex business problems. To accept the fact that at the start of your career you may not even get the title of a data scientist (in some exceptional cases). However, if you are determined and learn more and more about the field, the chances are that you will reach a higher position with a considerably high paycheck.

Make sure you don't hesitate to ask another data specialist for help if you need it. Knowledge and skills are the keys to success.

These are some factors that you have to focus on if you want to make a career in data science. A one can also become the data scientist by implementing these factors with having no prior experience.

Conclusion

If you want to get a job in data science then you have to follow the above mentioned factors and you will definitely get a good job in data science with having no prior experience also. So start today and follow these steps and land your first job as a data scientist.

Near learn provides the best online data science training in Bangalore. It provides other courses as well as artificial intelligence, machine learning, Deep Learning, Blockchain, ReactJs, React Native, Golang, and full-stack development, etc. 

How is Data Science Changing the World?

In this article you will learn what role a data scientist plays. There is a mysterious veil in data science. While the buzzword of data science has been around for a while, very few people know the real purpose of being a data scientist.

So let's examine the goal of data science.

Data science goal

The main goal of data science is to find patterns in the data. It uses various statistical techniques to analyze and learn from the data. A data scientist must thoroughly examine data from data extraction, wrestling, and preprocessing. Then it is responsible for making predictions from the data. The goal of a data scientist is to draw conclusions from the data. Thanks to these conclusions, he can help companies make smarter business decisions. We'll divide this blog into several sections to better understand the role of a data scientist.

Why Data Matters in Data Science

Data is new stream. We live in the era of the fourth industrial revolution. It's the era of artificial intelligence and big data. There is a massive data explosion that has led to new technologies and smarter products. About 2.5 exabytes of data are created daily. Data requirements have increased significantly in the past ten years. Many companies have focused on data. New sectors has been created by data in IT industry. However,

1.       Why do we need data?

2.       Why do industries need data?

3.       What makes data valuable?

The answer to these questions lies in the way companies have tried to transform their products.

Data science is a very new terminology. Before data science we had statisticians. These statisticians have experience in qualitative data analysis, and companies have used it to analyze their overall performance and sales. With the advent of an IT process, cloud storage and analysis tools, the IT area has merged with the statistics. This created data science.

Early analysis of data based on surveys and finding solutions to public problems. For example, interviewing a number of children in a district would lead to a decision to develop the school in that area. The decision-making process was simplified with the help of computers. As a result, computers can solve more complex analytical problems. As the data began to multiply, companies began to see their value. Its importance is reflected in the many products that are designed to improve the customer experience. Industry has been looking for experts who can harness the potential of data. Data helps them to take the right business decisions and maximize their profits. In addition, the company was able to examine and respond to customer behavior based on their buying habits. The data has helped companies expand their sales model and create a better product for their customers.

Data refer to products, electricity to household appliances. We need data to design the right products for users. This motivates the product and makes it usable. A data scientist is like a sculptor. He chisels out the data to create something meaningful. While this can be a tedious task, a data scientist must have the right expertise to deliver the results.

Why data science is important?

Data creates magic. Industries need data to make prudent decisions. Data Science converts raw data into meaningful information. Industries therefore need data science. A data scientist is an assistant who knows how to create magic with data. A competent data scientist knows how to extract meaningful information from the data he encounters. It helps the company in the right direction. Society needs solid data-driven decisions, of which he is an expert. The data scientist is an expert in various underlying areas of statistics and IT. He uses his analytical skills to solve business problems.

Data Scientist is good at solving problems and is responsible for finding patterns in the data. The aim is to recognize redundant samples and to learn from them. Data science requires a variety of tools to extract information from data. A data scientist is responsible for collecting, storing and managing the structured and unstructured form of data.

Although the role of data science focuses on data analysis and management, it depends on the area in which the company specializes. This assumes that the data scientist has knowledge of the field in this particular industry.

Target Data-Centric Industries

As mentioned above, companies need data. They need it for their data-driven decision models and to create better customer experiences. In this section, we will explore the specific areas that these companies focus on to make smart data-driven decisions.

I. Data Science Helps for Better Marketing

Companies take helps from data to analyze their marketing strategies and create better ads. Companies often spend astronomical sums to market their products. Sometimes this cannot lead to the expected results. By studying and analyzing customer feedback, companies can create better ads. To do this, companies carefully analyze the behavior of online customers. By tracking customer trends, the company can also get better market information. That's why companies need data scientists to help them make informed decisions about marketing campaigns and advertising.

ii. Data Science Helps in Customer Acquisition

Data scientists help the company attract customers by analyzing their needs. This allows companies to customize the products to best meet the needs of their potential customers. Data is the key for companies to understand their customers. The purpose of a data scientist is therefore to give companies the ability to recognize customers and help them meet their customers' needs.

iii. Data Science Helps in Innovation

Companies create better innovations with a wealth of data. Data scientists contribute to product innovation by analyzing and creating information in traditional designs. They analyze customer reviews and help companies create a product that fits perfectly with reviews and comments. With the help of customer feedback data, companies make decisions and take appropriate measures in the right direction.

iv. Data science Helps in Enrich Life

Customer data is important to improve their lives. The healthcare industry uses the data provided to support their customers in their daily lives. Data scientists in these industries want to analyze personal data and medical history and develop products that address customers' problems.

The data-driven company examples above show that each company uses data differently. The use of the data depends on the requirements of the company. Therefore, the goal of data scientists depends on the interests of the company.

 

Other skills-set for data scientist

In this blog about the purpose of data science, we will now see what other skills a data scientist needs. In this section, we will examine how data scientists go beyond analyzing and collecting information from data. One goal of data scientists is not only to use statistical techniques to draw conclusions, but also to share their results with the company. A data scientist not only needs to master the calculation of numbers, but also to be able to translate mathematical jargon to make the right business decisions.

Example: Imagine a data scientist who analyzes the company's monthly turnover. It uses various statistical tools to analyze the data and draw conclusions. In the end, he gets results that he has to share with the company. The data scientist needs to know how to communicate the results in a very precise and simple way. Sales managers may not understand technical results and processes. Therefore, a data scientist must be able to tell a story. Thanks to the data narration, he can easily transfer his knowledge to the management team. This, therefore, extends the goal of a data scientist.

Data Scientist's goal is not just limited to statistical data processing, but also to managing and communicating data to help companies make better decisions.

So it was all for the purpose of data science. I hope you enjoyed our article.

Conclusion

At the end of the article - the goal of data science - we conclude that data scientists are the backbone of data-intensive companies. The goal of data scientists is to extract, preprocess and analyze data. Thanks to this, companies can make better decisions. Different companies have their own requirements and use the data accordingly. Ultimately, the goal of a Data Scientist is to make companies grow better. With the decisions and information provided, companies can define appropriate strategies and adapt to improved customer experience. Want to learn these skills then go to Best online data science training in Bangalore and gain the right skills for your future. Other skills which is in demand are machine learning, artificial intelligence, react native, blockchain , etc.

However, if you have any questions about the goal of data science, post them freely with comments. We will definitely come back to you.