For many retail businesses, artificial intelligence, namely machine learning, isn’t something that’s coming to the industry in the next decade. Some of the biggest companies operating in the industry – including Amazon, Walmart, and many others – already have AI systems in place to process customer data to help them sell more.
For example, last year, Jeff Bezos, the CEO of Amazon, wrote a letter to the company’s stakeholders that explained how they used AI to create a competitive advantage. Naturally, most of the attention went to Alexa, but we also found out that the company spent quite a lot of time on using machine learning to analyze purchasing patterns and determine fraudulent purchases. Moreover, Bezos also shared that the company utilized customer browsing and buying data to provide them with personalized promotion and product recommendations.
That sounds about right: Amazon is fantastic at analyzing purchasing patterns because every time I visit the site, I get interesting, and, most importantly, relevant product recommendations. However, product recommendations are just a tip of the iceberg because machine learning can do so much more.
So, with the machine learning revolution in progress in retail, how exactly it will continue to impact the industry? Should smaller companies also join giants like Amazon in the pursuit of business insights provided by machine learning?
We’ll try to answer these questions here.
The Amazing Algorithm
I’ve recently come across this Economist article that I thought provided a glimpse into the future of retail business. Otto, a German retailer, was struggling with a problem of additional shipping costs and delays. The company sold products from other brands, so they had no warehouse to store them before shipping. To make a long story short, delays, as well as returns, were a usual thing for them.
According to the article, some of Otto’s customers even returned products they bought from the company after they “spotted the product in a shop for one euro less.” Needless to say, the company needed a quick and effective solution to predict future buying behavior, which was done by humans.
So, Otto’s management invested in the creation of a machine leaning-powered system which, although initially designed for particle-physics experiments, did a fantastic job at predicting what the customers bought a week before they ordered. The algorithm was able to make predictions of what will be sold within a month with 90 percent accuracy (!)
As a result, the brand was able to improve the effectiveness of its existing processes profoundly.
So I guess that the main takeaway here is that small businesses should also explore machine learning and its benefits. As the story of Otto shows, Amazon and other retail giants aren’t the only ones already benefitting from the technology.
So what are other impacting ways to use machine learning in retail?
Machine Learning in Retail: Use Cases
1. Dynamic Pricing
Machine learning algorithms process a lot of data collected from customers, but their functionality is not limited to personalized product recommendations and predicting what products will be bought. As we already know from the story of Otto above, many customers are very price-sensitive, e.g., they’re looking for the best prices and are willing to return a product if they find a lower price for it.
Machine learning can help retailers to check and continuously monitor the prices of competitors to automatically match them or even offer a lower price, so the customers get the best possible deal.
“You’ve probably seen deals that offer “The best price guarantee” or something like that – this is often used by companies in the hotel industry,” says Jamie Chapman, a data analyst from Trust My Paper. “Chances are that’s the work of machine learning algorithms.”
2. Fashion Trend Analysis
Without a doubt, machine learning will be a disruptive force when business models based on it will emerge. For fashion retailers, collecting social media data such as likes and comments will be tremendously important because it will help them to identify their interests and the hottest trends.
Of course, machine learning makes this task much more comfortable by processing tons of data within minutes. The results of this analysis can help to make relevant offers for specific customers as well as identify what’s selling.
No wonder there’s evidence that many of the well-known fashion brands are starting to use artificial intelligence; for example, Inditex, the world’s largest clothing retailer and an owner of the Zara brand, has recently formed a partnership with technology companies to be able to utilize the benefits of machine learning and keep up with the competition.
3. Inventory Management
As we already know, demand prediction is something that machine learning can help with, and this is also the first step in the process of inventory management. Once the level of demand has been determined, a brick-and-mortar retail store can resupply to ensure that the needed number of products is available to meet it.
Predicting too little or too much imposes additional preventable expenses for retailers, but, thankfully, they have lots of data, which is exactly what machine learning models and algorithms needed to generate a sound prediction.
4. Check-out Free Shopping
Another use for brick-and-mortar stores is quickly gaining interest, especially after the opening of Amazon Go, the checkout-free grocery store. Apparently, AI and machine learning can help to cut waiting time in retailer checkout lines by analyzing data provided by smart shelf sensors and cameras, which is undoubtedly something that a lot of people will appreciate.
According to Retail Gazette, in addition to Amazon, such international retail companies as JD.com and Alibaba also have their concepts of unmanned convenience stores so the revolution might be coming sooner than we think.
Is Machine Learning the Future of Retail?
Machine learning can change the way we shop as well as dramatically enhance the existing approach to customer data analysis, inventory management, and price setting. It’s inevitable that giant companies like Amazon and smaller ones like Otto can benefit from the technology in a big way, so the pace of adoption is likely to accelerate in the next years, as more and more companies study the benefits of machine learning.
Well, the IT industry is something that one can’t easily predict. Even if a niche of IT is booming, the next moment it can go down any time. The best thing to count upon is data. Even if there’s a complete change in the industry, still, data is something that everyone needs. Yes, with the introduction of GDPR that field is also under scepticism but don’t worry, some of the other data is needed to be taken care of.
Statistics say that the average time an employee works in one particular company has gone down to 4.2 years. Gone are the days when the employees used to work for 40 years and then enjoy their retirement. It’s said that by 2020, 65% of the skill you possess would be of no use. That in itself shows the kind of improvement you need to make on your skills.
Is AI a Threat?
You can take the example of Artificial Intelligence. AI is expected to take over the jobs of a number of executives especially in the field of customer support. There has been an influx of the chatbots in the industry and the companies are using them well. These bots have an instant answer and wouldn’t take much time before coming up with an answer. If you’d like to read about AI, read here.
The introduction of AI has put a number of jobs in trouble. Employees are worried about their jobs and are not certain till when they’re going to last. There are a few jobs that might face hard time standing in front of these technological advances. Let’s look at a few of these. Also learn how our day to day technologies are dependent on AI.
What are the job roles in trouble?
Customer support replaced by the ChatBots:
The customer support is a field that has quite a great employment rate, especially in India. In order to cope up with the high hourly wage rates, the western country organizations rely upon Indian and other eastern countries for these services. The customer care executives are paid low here but still a payment is a payment. In order to avoid this, chatbots with higher intellectual levels are being incorporated in this field.
These chatbots are intelligent, quick to reply and they are scarily empathetic. If you’ve come across Google’s Duplex, then you’ll understand what we are talking about. About 90% of the people talking to the bot couldn’t recognize it was a bot talking to them. Hence, there’s a large scope for AI to take over the bots.
Automatic programming replacing the coders:
The automatic programming is said to be the next future thing in the IT industry. There are multiple speculations as to the possibility of this. But, GitHub aims to make the coding more automated. You can read about the same here. Though there are high chances that these automatic coders might hit the industry anytime, still it’s definitely going to take a good amount of time before they take over the manual programmers. So be prepared for this upfront and make sure you don’t invest all your eggs in one basket.
Automated testers to replace the manual testers:
This has already started to roll. If you’re an IT enthusiast or if your friend is working in the testing field, you’ll know that there’s an automatic testing already. Though this automatic testing is not entirely automated yet, still there’s a high possibility of it turning into completely automated soon.
So keep yourself from sticking in this field and explore other options. Yes, testing is easier when compared to others but it’s going to lead you nowhere if you get stuck in this.
Data Analysts replaced by Analyzers:
Data Analysts are the ones who analyze the customer or client data. They are supposed to have a brief understanding of the upcoming trends. With the inception of AI, devices are being developed that can observe this automatically. The key performance indicators are analyzed and better results are put forth by these analyzers.
Layoffs in IT jobs:
IT jobs are never really reliable. There can be a sudden change of trend and the sudden change of the clients. If one major client decides to drop out of the deal, thousands of jobs fall in jeopardy. Hence, you can’t really rely on the job. Keep updating yourself in a new field. Show your expertise in different directions so the company knows your need. This way your worth is not overlooked. You can learn further about these statistics here.
Different roles in IT jobs:
IT which is the abbreviation for Information Technology is one of the most fluctuating and highly employing jobs. One needs to get a great idea of what they are stepping into, before starting their careers. The statement is no better suits for any other industry than this IT industry. So without further ado, let’s jump into the different role descriptions so you’ll know what to choose.
If you’re a graduate and have a computer science degree, there’s a high possibility you’ll land into an associate software engineer job. This would pay you around 3.5 LPA and this might change according to the industry standards.
A data analyst would be paid a starting salary of 4.5LPA and the hikes would be pretty awesome. There’s a good chance you’ll be paid higher when yous tart switching.
The developers are usually paid the same amount as an entry level in MNCs (3.5LPA), though the scope of growth is pretty high and the chances of layoffs are quite low.
This is another job that might keep you interested while you’re at it, but once in case you’re out, you’ll know nowhere to go. The salary is the same as stated for the developer and the growth in this role is bleak.
You either need a good expertise in the field or have the respective degree to land yourself in the job. The salary for an entry level would be around 5.5LPA and the hikes would depend upon the performance.
One of the most important roles and the pay is according to its worth. The job is tough and head blasting, but the incentives and pay are as good. The initial payment for the entry level would be around 6.5LPA.
Which information technology job has the highest salary?
As you’d all know that it’s the CEO who would be earning the most. But the salaries of CEO might change from time to time and it doesn’t completely come under the IT sector. So let’s look at the highest paid salary for an IT operating executive.
Well, it’s the Software Architect who earns the highest out of all the remaining technical executives. In India, in a well reputed MNC, a software architect would be earning about 21 LPA. That’s just for the start. If the respective person lands himself with some great experience could also earn about 3Lakhs per month, which totals to 36LPA
Which IT jobs are in demand?
The next big thing in the IT industry. The future of the world is going to work on data. In the world where the concept of astrology is believed without any proof, data is the one that people should invest their dollar in. You can see and understand the various trends basing upon this. A data scientist gathers all the information and uses it in the right areas. If the data scientist belongs to an e-commerce industry, he/she would try to figure out your likes and show the appropriate products.
Machine learning is a way for machines to learn and understand the different scenarios through Artificial Intelligence. On the verge of making the future of the mankind easier, multiple measures are taken. Out of all these measures, the one that everyone’s counting upon is the Artificial Intelligence. The machines are designed in such a way that just like the humans, they start learning from different occasions. All the things that happen around them are registered in their storage and the future performance would be based upon those incidents. There’d also be a few protocols installed right before their operation to avoid any unwanted situations.
In the generation where there’s a fear of losing data, cloud storage came for the rescue. The cloud storage is essential in protecting the data and keeping it away from all the dangers of losing. Though there have been a number of vulnerabilities in the access to these data, still experts are working on to make these as secure as possible. Learn how cloud computing is essential for the IT industry.
Another huge industry that hasn’t yet received the recognition it should have. Big data is the process of collecting and storing various pieces of data. These data are later utilized in making the appropriate choices. Depending upon the data collected, the further categorization and taking appropriate steps are done. Hence this is one of the other industry that you can actually count upon.
First of all, Blockchain technology is something that all of us have heard of. Also, the kind of waves bitcoin managed to create in the world of digital transactions is unmatched. Blockchain was able to make some really transparent and secure transactions. These transactions for their transparency on the digital level can’t be manipulated either. Not just that, blockchain is recently being incorporated in the advertisement platform as well.
Which IT job is right for me?
Every IT job has its own need for expertise. Different role demands for a different specialization. Hence, it’s you who needs to decide for yourself with respect to your skill. Here’s a link to the personality test that would help you determine your skills. Though these skills are not completely to be relied upon, still, you need to understand your own set of expertise. Here’s the government organized skill development program you can incorporate.
Courses available for the IT jobs:
Here’s a list of different software courses in order to land yourself into your dream role.
JAVA/J2EE & its Frameworks (Struts, Spring, Hibernate)
Big Data Analytics
DBA (Oracle, DB2, MySql, SQL Server)
System Administration (Red Hat, Solaris, UNIX, VMware)
Mobile SDKs (Android, iPhone, Windows Phone)
Animation & Graphics
SQT (Software Quality Testing)
Other language courses(PHP, Ruby/Perl/Python)
The career growth can’t be determined all at once. But if you’d like to sustain in this world of constant upgrading, keep learning and innovating. Also, the one easy step to keep yourself from the effects of losses, show how worthy you can be in other fields as well. Don’t just stick to one field that you’re assigned for. There are so many flavours of icecreams you need to try to be able to justify your position in the company.
The on-site opportunities in this field are quite high, provided, you show your worth. Yes, there’s a scepticism regarding the politics and stuff. But if you’d like to overcome it, show you’re better than the others, don’t just know it, prove it. Usually all the MNCs deal with the international brands as their clients. As long as you’re working for an international client you have some good chances of being called up if you’re good enough. So sit tight and just kleep doing better. You might as well land yourself in a great position.
Trends and the Future of IT industry:
As discussed earlier, the future is all about data and its analytics. Furthermore, The data is running all the major businesses these days. Knowing your clients or customers is the prime motive of any business professional. This not only guides you in the right direction towards satisfying the needs of your customer but also would help you see a great rise in revenue. Understanding the data and analyzing it through analytics is the future. If you want to stay in the race, keep a good eye on the trends and update yourself accordingly.
Though there isn’t a strong prediction that could anticipate how the IT industry is going to churn out in the near future, hence, all you can do is bet on it. Also, no industry is outdated if you strive on keeping yourself up to the changing trends. Change with time. Keep pushing yourself and never find your comfort zone. If you need a comfort zone, go ahead and find yourself a government job. Let me warn you, It’s certainly going to be boring when compared to the IT industry!
You think Google is always right? Have you ever noticed, people when asked a question and if they don’t know the answer, they say I’ll Google it! Are we really sure that Google pops out the right answers for you? Are you certain that the result displayed can’t be just a “version” or a “perception” of that particular author? Can you claim with certainty that the author is a professional in that particular field? Most of them are sure about it. That’s the sad truth about our perception of Google.
There’s also another phase to it. I’ve seen people who, when asked a question, Google it and show some blog article as a proof. They show off some content that was posted by some random author, who we don’t even know about. How can we be sure that what they’ve written is right? Can you just blindly go with a statement to be right, when it claims something? Is every result that pops up on your search engine result is supposed to be true? Let’s find out what the Google executive himself has to say.
So what does Google have to say?
A Google executive admitted that what you always see on Google is not true. No one can say with a certainty that the answers displayed are the actual facts. He exclaimed one thing and I think that’d be enough to enlighten all those souls that think that the Google is the ultimate dictionary of truth. “We’re not a truth engine”. Yep, that’s what he said and he’s certainly right about it. Google isn’t an artificial intelligence filled bot or neither it’s your answering bot. It only provides you with different sources to search for your answers. The answers might vary from page to page.
There are multiple instances where Google shows some of the most hilariously fallible results that might crack you up for the moment. But it would also make you think about all those times you trusted Google for your result and the saddest part is, you actually believed it without a sigh of a doubt.
The only problem with people is that they refuse to put their judgement on the line. Some people consider themselves to be inferior to all the information that Google provides and go with a delusion that Google is smart and always right. Although it’s not Google that answers you. Google just searches the relevant options for you and what you see on Google are just different answers from different people.
So the one, take away from this is that you can’t just blindly trust the search engine for your answers. Search engines do the job of searching for you. After the search part is done, it’s you who needs to analyze it and refer with something else and then finally pass a judgement. So, the next time you search for an answer on Google, analyze it rather than clinging to it.
Artificial Intelligence has come a long way from being just a science fiction dream to a reality which we see today. The world is evolving at a rapid pace and with it the technologies are being upgraded and are getting better too. Today we have more power in our pocket than we had in our homes in the 1990s. Not long ago, holograms and smartphones were just science concepts but now the smartphones can check our health as the technology is evolving. Some of the application areas of artificial intelligence are health, education, entertainment, services, security and many other domains but these fields are specifically the most to benefit from this technology.
People are beginning to explore the benefits of AI and how it can ease their life. Artificial Intelligence and Machine Learning go hand-in-hand so that machines can learn about critical programs to gain knowledge and respond to demands to perform similar human-like tasks. Artificial Intelligence enables a machine to self-learn from experiences and human intervention which finally provides us with human-like capabilities for interaction and problem-solving potential. From chess-playing computers to self-driving cars, AI is present in many areas which we not might have thought of. AI makes use of Deep Learning and Natural Language Processing (NLP) to accomplish tasks like these which drives automation and intelligent processes.
Various Technologies will Lead in Coming Days
Recently the most progressed fields are Augmented Reality, Virtual Reality, Voice Assistance and Artificial Intelligence, and people are beginning to wonder what the future might be like with this kind of technology. To explain these technologies in 2 lines I would like to start with Augmented Reality, which combines virtual objects with the real world which gives an interactive experience by computer-generated perceptual information. Another one is the Virtual Reality which is a computer-generated simulation of a 3D image which can be interacted in a real or physical way through an electronic device which has sensors. Last but not the least is the Voice assistance which has been with us for many years now but the enhancements in AI has provided more room for improvement in voice-assisted technology.
There are many examples of AR which are being used by individuals and industries like;
IKEA uses the application Place, which they have recently released so that customers can
check how different types of furniture look in their home which simplifies purchase decisions for the customers. Niantic’s Pokemon Go, an Augmented Reality based smartphone game, enables the player to catch Pokemon on real-time locations. Automaker Company Ford uses Microsoft’s HOLO LENS to design cars and experiment with new designs. Other automakers, Audi and Cadillac use Virtual Reality to enhance the customer experience so that the buyers can see the car model and the features. So in short what I mean to say is that nowadays industry giants have started implementing technologies like AR and VR in their fields and are experiencing a much better way to run their business.
Examples for Voice Search technology are Apple’s Siri, Samsung’s S Voice, Microsoft’s
Cortona and Google Assistant, Amazon Alexa. A lot of development has been made in the field of AI in the last few years which has motivated developers and companies to create more products and services around it.
These are some of the points which suggest why Artificial Intelligence is important:
1. Repetitive Learning though data
AI carries out continuous computerized tasks which are high volume and automated. Through Deep Learning, AI seeks to get in-depth information about a problem or a query raised by an individual. Human enquiry is still important to ask the right question so that the learning is in the right direction.
2. Adding Intelligence
Products which are already in use are improved through the integration of AI capabilities. Different types of components like automation, learning bots and conversational platform can be merged together along with huge amounts of data to improve the product or the service through AI.
3. Adapting to newer algorithms
AI continuously learns newer algorithms through structured data available to it where a new skill is acquired in order to provide newer experiences. An algorithm is important to solve a certain case or a problem. When new models are introduced, AI automatically learns through training and added data.
4. Deeper Insights
More and more analysis of data leads to a deeper understanding of the database available to AI and through neural networks many hidden layers can be uncovered. With the highest compute power, various models can be trained to go deep into data to get more relevant out of it.
5. Incredible Accuracy
The most appropriate example to define this point will be Google search and Google photos which gets better as we continuously use them. Through deep neural networks, AI achieves higher accuracy to dive deep into data and select patterns of the users which can be helpful in providing similar results.
6. Proper utilization of data
The algorithms are self-learning and the data is used appropriately and excessively by AI which helps bring out the best results out of the big data. Since the data is important now than ever before, it creates a competitive advantage because many newer techniques or solutions and be uncovered through proper analysis.
Artificial Intelligence can benefit the lives of each and every individual because it has the potential to offer a technology which can be implemented in day-to-day life and would make their lives easier. There are multiple areas which can benefit from AI and this will result in the reduction of human efforts, costs, and labor. Industries and Enterprises are already using AI in their business processes and have started seeing the difference through high efficiency, lower operating costs, and faster decision making.
Artificial intelligence or AI also known as machine intelligence or MI and this is the reverse of natural intelligence which includes humans and other living beings, as everything fully depends and demonstrated by machine. Which, basically means the area which gives full emphasis on creating the intelligent machines that work very similar to human beings. Some artificial intelligence activities involved in the computer are as below:
CLASSIFICATION OF ARTIFICIAL INTELLIGENCE (AI)
Artificial intelligence or AI can be classified in various ways but important are the two ways. Artificial intelligence first classification is either weak AI or strong AI also known as narrow AI which is designed and trained for only a particular task. Personal assistants which are virtual like the Apple iPhone’s Siri are the form of weak AI.
The strong AI is just like the human brain and its cognitive capabilities whenever there is an unfamiliar task given it is capable enough to find the solution.
The work that is associated with artificial intelligence is very technical and needs specialization. However, there are some problems you get while working on computer coding for some important traits which includes:
There are different forms of learning included in artificial intelligence and the simplest form is the trial and error one. For example- a simple program to solve the problems in computer chess game it tries the moves in a random manner until the mate is found. The program usually stores the moves or the position so, that when the next time the computer encounters the position it can declare the result accordingly. The memorizing of the simple items and procedures is called as rote learning which is very easy to carry out on the computer. And the most challenging one is the generalization. Which basically means applying past experience in the new incidents.
This means drawing or summing up the appropriate inference according to the situation. And inferences are basically of two types that are inductive or deductive. For example, if we consider “George who went to the restaurant or to the airport. He is not in the airport so must be in the restaurant” similarly, “similar type of accident was caused previously due to the failure of the instrument, so this accident was caused due to the failure of the instrument”. So, the most important difference between inductive and deductive is that. In deductive the premise truth is being guaranteed by the truth of conclusion whereas in case of inductive the truth of premise is supported by conclusion truth without giving absolute assurance. In science, there is a lot of use of inductive reasoning the models are developed after the collection of tentative data to describe and predict the future behavior. While going to the deductive reasoning is common for mathematical or logical where the elaborate structures which are undeniable are built up with small sets of axioms and rules.
It has considerably gained a lot of success and draws an inference from computer programming, especially the deductive reasoning. Generally, the true reasoning always doesn’t contain the drawing inference, it includes something more than like drawing inference which related and relevant to the solution of the particular task or work. This is one of the difficult tasks that is done in the Artificial Intelligence.
To reach a particular goal or target the systematic search done for it including a certain range of possible action is called as problem-solving which is an important aspect of artificial intelligence. There are two types of problem-solving methods general purpose and special purpose. For every specific tailored problem and gets exploited for any specific feature of the situation and the problems get embedded in it this is called a special purpose method. Whereas in case of general purpose it involves a wide variety of problems. This includes means-end analysis or the step-by-step and reduction of the difference present in the current state and final goal is called the general purpose technique. There are lists of means allows the program to select its action- this majorly includes the words which are for a simple robot that are PICK UP, PUT DOWN, MOVE FORWARD, MOVE BACK, MOVELEFT, MOVERIGHT until the desired goal is reached.
A lot of different problems have been solved by using this artificial intelligence. For examples, sequential moves in a board game, composing different mathematical proofs and manipulating virtual objects in the computer-generated world.
The environment is scanned by different sensory organs, real or artificial and the scene gets into different objects for different spatial objects this is known as perception. The analysis is always complicated and can’t deny with the fact that every object appears different from the different angle of view depends on the direction and illumination intensity in the scene and how the object gets contrast from the surrounding scene.
Nowadays artificial intelligence has become very much advanced and optical sensors can be enabled to identify the individuals, on the open road the motorcycles can drive at a moderate speed and robots can roam easily collecting the soda cans from building to buildings. The first system that implemented the perception and action was named as FREDDY, this robot has a television eye which was moving and pincer hand but the robot was stationary. It was constructed during the year the 1966-73 in Scotland. It was able to identify a variety of objects very easily and could assemble simple artifacts like toys and random heap components.
A system containing signs with a conventional meaning was called as Language. The language is just not confined to the words we write or speak. We have mini-language the traffic signals which delivers the message of having the hazard ahead. These are very distinct languages that the convention of the linguistic language posses and linguistic here means very different from the original one. A productive and successful language can result in unlimited and variety of sentences.
It would be very easy to write a program in human understandable language so that the responses, comments, queries can be easily understood. Although the machine doesn’t understand the language there are certain principles like the commands which are stored allows them to understand or distinguish between human understandable and non-understandable languages. Then evolves a genuine understanding where the computer even getting the commands fails to acknowledge the language. This thing still doesn’t have a permanent solution. And it depends upon a lot of factors, one’s behavior and history etc. can be trained and to take place in linguistic communication and by making it interactive.
Machine learning ML is also an important type of artificial intelligence AI and predicting the outcomes becomes very easy as it allows the software application to become more accurate without any explicit program done. It is a process which enables the data analysis to automate the analytic model building. It is basically made on the artificial intelligence idea which says that the system learns from the data, can easily identify the patterns and itself can make all the decisions without the involvement of human beings. So, the general work of the machine learning is to build such algorithms that predict the right output by using statistical analysis by taking the right input in an acceptable range.
Whereas machine language is a trade within the computer language which it is still different from traditional computer techniques. In traditional computing techniques, the algorithms used are explicitly programmed and certain instructions are given to the computer to calculate or solve a problem. Instead of machine learning, the algorithms trains the computers on the correct input of the data and get the output in a statistical order which falls within that specific range. This allows the machine language to build models from the sample data provided and which automates the process of taking decision-making by the data provided.
Everyone who is using this advanced machine language has been benefited from it. The technology like the face recognition which helps the users to tag, share and post their pictures on the social media platforms. Another technology that is the OCR or optical character recognition that makes a normal image of the movable type. Whatever T.V shows or movies we watch are based on the user-preferences which were done by the recommendation engine in machine learning of artificial intelligence.
As machine learning is a part of artificial intelligence so a course on machine learning is always a great idea as it is the most developing field nowadays. While working with the methodologies of machine learning you to keep a lot of consideration in mind and should know its importance and analyze it properly.
MACHINE LEARNING TECHNOLOGY EVOLUTION
As the technologies get improved and get advanced similarly the previous machine learning is not same as the new machine technologies. It was basically developed from the technology called pattern recognition and the based on the theory that a specific task can be performed without being programmed in the computer: the most important challenge for the researchers was to see if it the computer could learn effectively from the data already provided. In machine learning, as the models are always get exposed to new data, they have adapted themselves independently moreover machine learning is an iterative process. From every growing time, they adapt themselves easily they produce reliable, repeated decision and results. With every new machine learning something brilliant is coming up which is gaining the fresh momentum.
There are a lot of machine learning algorithm that has been invented till now, having the ability to apply mathematical calculation complex that is automatically added to the Bigdata which are faster and faster and over and over is the development done recently. Below are some of the examples which include the machine learning technique:
The self-driving Google cars include machine learning techniques.
Amazon Prime and Netflix online recommendation offers.
Checking what people say about you on twitter.
One of the most important aspects is the detection of fraud.
DIFFERENT MACHINE LEARNING METHODS
The most adopted method in machine learning method is the Supervised learning and the Unsupervised learning instead of this there are some other are methods also that are listed below:
Supervised learning: using labeled examples the algorithms are being trained where the input is given the desired output is already known. Considering an example there is an equipment which has data points labeled as F for failed and R for the run. A set of input would be received by the learning algorithm with the corresponding set of outputs, then it compares itself with the existing output and the real desired output this is how the algorithms learn about the errors. After knowing the errors it modifies the model accordingly. There are some other methods also like classification, regression, prediction, and gradient boosting instead the supervised learning is used as the values can be predicted using patterns on the label on the additional unlabeled data. The applications where the future events can be determined by the previous data are commonly learned from supervised learning. For example, it can easily determine when the transaction on the credit card is going to be fraudulent or which customer of the insurance is going to file the complaint.
Unsupervised learning: the data which doesn’t have any historical or previous label then unsupervised learning is used. The “right answers” are not told to systems. What are the things being shown must be figured out by the algorithm! To the desired structure within, we have to first explore the data. On the areas of transactional field unsupervised learning is applied. Considering an example: the customers having similar attributes can be identified with similar segments and all these customers can be given the similar type of marketing campaigns. Or in the other way, the customer segments can be differentiated from each other by finding the main attribute. The techniques like the self-organizing maps, nearest neighbor mapping, k-means clustering and singular value decomposition these all are the important and popular techniques. For segmenting the text topics, recommending items and for identifying data outliers for all these the algorithms are used.
Semi-Supervised learning: as the supervised learning is used similarly for that application only semi-supervised is used. The labeled and unlabeled data both are used for the training similarly the way labeled data were taken in small data and unlabeled data in large quantity as unlabeled data is less in price and requires very fewer efforts to acquire it. The methods of classification, regression, and prediction in which this type of learnings are used. A purely and fully labeled training process is not allowed as the cost of labeling gets very high in this type of situations the semi-supervised learning is used. Identification of a person’s face through the webcam is one of the examples.
Reinforcement learning: this basically includes robotics, gaming, and navigations. Which action will get the best reward is being identified by the trial and error method by the algorithm this whole method is called as reinforcement learning. The agent or the decision maker, then come to the environment that place or those everything the agent interacts with, finally the actions or the steps taken by the agent- these are the important and primary three components of reinforcement learning. To increase the expected reward and that too in a stipulated period of time the agent has to choose the appropriate action which the agents main objective. Having a good policy is an important aspect through which the agent can reach his goal much easily and fastly. In reinforcement learning knowing the best policy is the key aspect.
SECTORS WHICH ARE BENEFITED WITH MACHINE LEARNING
FINANCIAL SECTOR: the financial sector which included banks, investment companies, and other businesses uses this machine learning techniques for main two key purposes that are: first is the fraud prevention, second is to identify the important insights present in data. From this, the investors can easily know when to trade and can identify the investment opportunities from the insights. Another technology data mining can also be used for identifying clients with high-risk profiles and warning the clients about the fraud by using cyber surveillance which gives pinpoint information.
GOVERNMENT SECTORS: machine learning has a lot of importance in public safety and utilities in Government sectors as the data can be mined easily for insights as they have a lot of or multi sources for the data. Analyzing the sensor data for example which increases and identifies the efficiency and saves the money. Detecting fraud and minimizing the theft are the main things for which machine learning is used.
MEDICAL SECTORS: in medical sectors, machine learning is having a lot of hype all thanks to the new trending wearable devices and sensors that are used by the doctors to treat their patients. Using this technique the doctors can easily check or diagnose the data to analyze the trends or red flags which improves the treatment.
SALES AND MARKETING SECTORS: you can easily analyze your previous buying history when the website recommends you the similar items based on your last buying which automatically helps in promoting other items. It helps a lot to analyze the given data, capture it and use the same to get a customized shopping experience.
OIL AND GAS SECTORS: machine learning does very important work in the oil and gas sectors like discovering new energy sources, analyzing the minerals present in the ground, predicting the sensor failures in the refinery. Making the oil distribution streamlining more efficient and cost-effective. There are a lot of uses of machine learning in this sector which still are still expanding day by day.
TRANSPORTATION SECTOR: the key aspect in the transportation industry is to identify the pattern and trends by analyzing the data, which helps the transportation sector a lot by predicting the dangers coming and making the relevant routes and increase the profit percentage. The analysis of data and the modeling aspects are very important in the delivery companies, public transportation and in different transportation companies.