Why Machine Learning Services are getting Maximum Attention?

Machine learning is one the trending topic these days. Right now, it is a catchword in the field of technology. It defines major representations that how computer can learn in future. Basically Machine learning algorithms are trained with the help of “training set” data.  By using this machine learning algorithm, it gives answer to the questions. For example, in training dataset you will have given pictures of dog to the computer. Some pictures will say, “This is a dog” or some will say, “This is not a dog”. Then you can show a number of new pictures and it would start searching which pictures were of dogs. Every picture which is identify as a correctly or incorrectly gets added to the training set. In this way program efficiently gets “smarter” and better by completing its task over time.

A couple of years back; it was the time of social, mobile, cloud and analytics etc. Although these technologies are still important as they have very good place in digital strategies.  Nowadays big hype is Artificial Intelligence, Internet of Things, Big Data, Machine learning services etc.  It is observed from the various surveys that Artificial Intelligence is the future of Growth.  There are some Artificial Intelligence Consulting companies available which provide number of services in this field. Machine learning is a part of Artificial Intelligence.

Let’s discuss Machine Learning Services which are attention seeker. These services are as follow:

  1. Fraud Detection: In numerous fields, machine learning is much better to spot fraud detection cases. Fraud management has been very aching for the commercial and banking sectors. Due to plethora of payment channel such as credit/debits cards, kiosks, smartphones etc., this menace is increasing day by day. In the similar way, criminals have found out loopholes in these channels. So, it is becoming difficult for the businessman to confirm transactions. However, Data Scientists have been successful in solving this problem with the help of machine learning. For instance, PayPal is using machine learning to fight with these raiders. This company has tools which compare all the transactions and easily distinguish between legitimate and non-legitimate transactions between sellers and customers.
  1. Recommendations: If you are regular user of Netflix and Amazon, then you must be familiar with use of this term. Intelligent algorithms of machine learning are monitoring  your all the activities and compare it with the other number of users and as a result show you which thing you would like to buy. These product recommendation systems are getting smarter every time. Suppose you want to buy light shade jeans of a particular brand, during your search it will also recommend you light shades jeans of some other brands. It will definitely make your shopping better with number of choices.
  1. Natural Language Processing (NLP): NLP is an emerging trend which is almost used in every field. Natural Language with Machine learning algorithms can stand for agents of customer service and in fast way route customers to the information they need. Mostly it is used to translate incomprehensible legalese in contracts into basic language and help prosecutors to handle large volumes of evidence to prepare for a case.
  1. Healthcare: Machine Learning algorithms can practice more information and find out more patterns than the humans can do. Machine learning can recognize risk factors in a better way for sickness in a large population area. A company build Disease Prediction System name “www.AIvaid.com” which based on machine learning algorithm that is capable to diagnose the human health conditions and predict the disease report. There are plenty of symptoms and diseases dataset has been added and working it wonderful. Along with that, Personalized medicine is also one of the effective treatments which relay on the health data of the individual combined with predictive analytics is a latest research and diligently correlated to better disease assessment. For this purpose, supervised learning is used which permits physicians to list out  from more limited sets of diagnoses based on the  symptoms and genetic information.
  1. Smart Cars: IBM recently surveyed top executives and it is concluded that we would see smart cars on road by 2025. It will learn about its owner as well as its environment and integrate it with Internet of Things.  Thus it will automatically adjust its internal setting such as audio, temperature, seat position automatically based on the driver and itself fix problems, drive itself and also give advice about traffic and conditions of the road.

If you want to start your career with Machine Learning, then you can start online and offline course with Dummybyte, here are plenty of course available you can opt with Python.

In the nutshell, we can say that Machine Learning is a buzzword in the world of technology. Machine learning services are wooing more customers due to its smart learning techniques. Self-learning algorithms are now routinely embedded in mobile and online services. Researchers are getting massive gains in processing power and the data streaming from digital devices and connected sensors to improve AI performance. For many organizations, providing machine learning services can be challenging. When machines and human solve problems together and learn from each other, AI full prospective can be achieved.

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Artificial Intelligence

Trends Shaping Machine Learning in 2017

Technologies in the field of data science are progressing at an exponential rate. The introduction of Machine Learning has revolutionized the world of data science by enabling computers to classify and comprehend large data sets. Another important innovation which has changed the paradigm of the world of the tech world is Artificial Intelligence (AI). The two technological concepts, Machine Learning and Artificial Intelligence, are often used as interchangeable terms. However, it is important to understand that both technologies supplement each other and are essentially different in terms of their core functions.

It is often predicted by technology enthusiasts and social scientists that human beings in the workforce will soon be replaced by self-learning robots. It is yet to be seen whether there lies any truth in these predictions or not but for 2017, the following trends have been prominent in the development of Machine Learning.

Giant companies will develop Machine Learning based Artificial Intelligence systems

In 2016, we saw many prominent developments in the domain of Machine Learning, and numerous artificial intelligence applications found a way to our phone screens and caught our attention. In the previous year, companies just touched the tip of the iceberg and in 2017, we will continue to see more developments in the field of machine learning. Big names such as Amazon, Google, Facebook and IBM are already fighting a development war. Google and Amazon launched successful apps, which include Amazon Echo and Google Home, at the beginning of the year, and we have yet to see what these tech giants have in stores for their customers.

Algorithm Economy will be on the Rise

Businesses greatly value data to take the appropriate actions, whether it is to understand the consumer demand or comprehend a company’s financial standing. However, it is not the data alone they should value because without an appropriate algorithm, that data is worth nothing. Peter Sondergaard, Senior Vice President of Gartner Research, says that, “Data is inherently dumb and the real value lies in the algorithms which deduce meaningful results from a cluster of meaningless data”.

Algorithm Economy has taken center stage for the past couple of years, and the trend is expected to follow as we expect to see further developments in machine learning tools. The use of algorithm economy will distinguish small players from the market dominators in 2017. Small businesses that have just entered the transitional phase of embedding machine learning processes in their business models will be using canned algorithms in tools such as BI, CRM and predictive analysis. On the contrary, large enterprises will use proprietary ML algorithms.

Expect more Interaction between Machine and Humans

Google Home and Amazon Echo received an exceedingly positive response from the audience which made it evident that consumers perceive human-machine interaction positively. Innovative technologies embedded with machine learning processes prove to be helpful under various circumstances; for example, helping people with eyesight issues to navigate. But will they completely replace human-human interaction? Maybe 25 years down the road, but we do not see that happening anytime soon. Machine learning has made it increasingly possible for machines to learn new skills, such as to sort, analyze and comprehend. But nevertheless, there are certain limitations to it. Automated cars have frequently been tested, and even with modified algorithms and advanced technologies, the chance of an error is still present. This example alone is enough to convince that machines will not completely replace humans, at least not anytime soon.

Conclusion

Machine Learning and Artificial Intelligence is a promising field with much potential for growth. We have seen some recent developments in the sector which, not long ago, people believed were not possible. Therefore, we cannot give a definite verdict regarding the industry’s potential for growth. However for now, intelligent machines are only capable of handling the repetitive tasks and can follow a predetermined pattern. It lacks the skill to figure out things which are out of the ordinary, and we still require human intervention for keeping the chaos at bay in such situations.

Ronald van Loon is Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He will contribute his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.

If you would like to read Ronald van Loon future posts then please click ‘Follow‘ and feel free to also connect on LinkedIn and Twitter to learn more about the possibilities of Big Data .

This article was originally posted on SimpliLearn

Source: Trends Shaping Machine Learning in 2017 | Ronald van Loon | Pulse | LinkedIn

Securing Competitive Advantage with Machine Learning | The HR Tech Weekly®

Securing Competitive Advantage with Machine Learning

How to Secure Your Competitive Advantage with Machine Learning | The HR Tech Weekly®

Business dynamics are evolving with every passing second. There is no doubt that the competition in today’s business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages.

Digitalization and the introduction of machine learning into day-to-day business processes have created a prominent structural shift in the last decade. The algorithms have continuously improved and developed.

Every idea that has completely transformed our lives was initially met with criticism. Acceptance is always followed by skepticism, and only when the idea becomes reality does the mainstream truly accept it. At first, data integration, data visualization and data analytics were no different.

Incorporating data structures into business processes to reach a valuable conclusion is not a new practice. The methods, however, have continuously improved. Initially, such data was only available to the government, where they used it to make defense strategies. Ever heard of Enigma?

In the modern day, continuous development and improvement in data structures, along with the introduction of open source cloud-based platforms, has made it possible for everyone to access data. The commercialization of data has minimized public criticism and skepticism.

Companies now realize that data is knowledge and knowledge is power. Data is probably the most important asset a company owns. Businesses go to great lengths to obtain more information, improve the processes of data analytics and protect that data from potential theft. This is because nearly anything about a business can be revealed by crunching the right data.

It is impossible to reap the maximum benefit from data integration without incorporating the right kind of data structure. The foundation of a data-driven organization is laid on four pillars. It becomes increasingly difficult for any organization to thrive if it lacks any of the following features.

Here are the four key elements of a comprehensive data management system:

  • Hybrid data management
  • Unified governance
  • Data science and machine learning
  • Data analytics and visualization

Hybrid data management refers to the accessibility and repeated usage of the data. The primary step for incorporating a data-driven structure in your organization is to ensure that the data is available. Then you proceed by bringing all the departments within the business on board. The primary data structure unifies all the individual departments in a company and streamlines the flow of information between those departments.

If there is a communication gap between the departments, it will hinder the flow of information. Mismanagement of communication will result in chaos and havoc instead of increasing the efficiency of business operations.

Initially, strict rules and regulations governed data and restricted people from accessing it. The new form of data governance makes data accessible, but it also ensures security and protection. You can learn more about the new European Union General Data Protection Regulation (GDPR) law and unified data governance over here in Rob Thomas’ GDPR session.

The other two aspects of data management are concerned with data engineering. A spreadsheet full of numbers is of no use if it cannot be tailored to deduce some useful insights about business operations. This requires analytical skills to filter out irrelevant information. There are various visualization technologies that make it possible and easier for people to handle and comprehend data.

Want to learn more about the topic? Watch replay of the live session with Hilary Mason, Dez Blanchfield, Rob Thomas, Kate Silverton, Seth Dobrin and Marc Altshuller.

Follow me on Twitter and LinkedIn for more interesting updates about machine learning and data integration.


Source: Securing Competitive Advantage with Machine Learning | Ronald van Loon | Pulse | LinkedIn

How to Build a Data Science Team

Businesses today need to do more than merely acknowledge big data. They need to embrace data and analytics and make them an integral part of their company. Of course, this will require building a quality team of data scientists to handle the data and analytics for the company. Choosing the right members for the team can be difficult, mainly because the field is so new and many companies are still trying to learn exactly what a good data scientist should offer. Putting together an entire team has the potential to be more difficult. The following information should help to make the process easier.

The Right People

What roles need to be filled for a data science team? You will need to have data scientists who can work on large datasets and who understand the theory behind the science. They should also be capable of developing predictive models. Data engineers and data software developers are important, too. They need to understand architecture, infrastructure, and distributed programming.

Some of the other roles to fill in a data science team include the data solutions architect, data platform administrator, full-stack developer, and designer. Those companies that have teams focusing on building data products will also likely want to have a product manager on the team. If you have a team that has a lot of skill but that is low on real world experience, you may also want to have a project manager on the team. They can help to keep the team on the right track.

The Right Processes

When it comes to the processes, the key thing to remember with data science is agility. The team needs the ability to access and watch data in real time. It is important to do more than just measure the data. The team needs to take the data and understand how it can affect different areas of the company and help those areas implement positive changes. They should not be handcuffed to a slow and tedious process, as this will limit effectiveness. Ideally, the team will have a good working relationship with heads of other departments, so they work together in agile multi-disciplinary teams to make the best use of the data gathered.

The Platform

When building a data science team, it is also important to consider the platform your company is using for the process. A range of options are available including Hadoop and Spark. Hadoop is the market leader when it comes to big data technology, and it is an essential skill for all professionals who get into the field. When it comes to real-time processing, Spark is becoming increasingly important. It is a good idea to have all the big data team members skilled with Spark, too.

If you have people on the team that do not have these skills and that do not know how to use the various platforms, it is important they learn. Certification courses can be a great option for teaching the additional skills needed, and to get everyone on the team on the same page.

Some of the other platforms to consider include the Google Cloud Platform, and business analytics using Excel. Understanding the fundamentals of these systems can provide a good overall foundation for the team members.

Take Your Time

When you are creating a data science team for the company, you do not want to rush and choose the wrong people and platforms or not have quality processes in place. Take your time to create a team that will provide your company with the quality and professionalism it needs.

About the Author:

Ronald van Loon has joined as an Advisory Board Member for its Big Data training category. Named by Onalytica as one of the top three most influential personalities of Big Data in 2016, Ronald will contribute his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.


Source: How to Build a Data Science Team | Ronald van Loon | Pulse | LinkedIn