The most powerful algorithms extensively used in the field of deep learning are neural networks. Here we focus on the artificial neural networks which are nothing but the computing system based on the structure of human brain. It is a type of machine learning which is used for detecting patterns in unstructured data. A lot of business applications today depend on neural networks to solve various complicated problems. However companies use neural networks in various ways. For instance, as explained by LinkedIn’s Deepak Agarwal, neural networks are used in LinkedIn along with text classifiers to detect spams. Neural networks are widely used in activities like marketing, finance, operations management, insurance etc. Thus the capabilities of artificial neural networks are inexhaustible. Read more at: https://www.cmswire.com/digital-experience/what-is-a-neural-network-and-how-are-businesses-using-it/

The power of machine learning is growing exponentially. Almost no industry domain is remaining untouched with the wonders and powers of machine learning.  Machine learning is just an application of artificial intelligence whose algorithms helps to analyze the historic experience without being explicitly programmed to predict the future affairs. Before jumping into the world of machine learning, it’s important to know which languages are being used to analyze the data and predict the future. Here are those 5 languages which are being using for machine learning: 

1. Python

2. R Programming

3. LISP

4. Prolog

5. javaScript

why and how are these languages being used for machine learning? For detailed information,

https://www.analyticsinsight.net/top-5-machine-learning-programming-languages-you-should-master/

Of course we have achieved 3D printing, but somehow we are still not able to produce a metal object which is capable of replacing the real world articles. Now implementing machine learning with 3D printing we have the capability to have real world objects replaced by objects produced by 3D printers. In the world of manufacturing researchers are planning to produce self correcting and repairing machines. There can be multiple approaches to have self-correcting machines. What are they? 

For more information, visit:

https://www.analyticsinsight.net/the-confluence-of-machine-learning-and-3d-printing-will-revolutionize-manufacturing/

Microsoft is all set to infuse AI onto Raspberry Pi, a tiny device. They are working on systems that can run machine learning algorithms on microcontrollers as small as a speck of red pepper flake. If not totally tiny, there are devices such as sensors in the current scenario that can collect data and send it to machine learning models running in the cloud. However, the disadvantage of this is that the processing requires a lot of power in data crunching along with occupying a lot of storage space. This is where the team at Microsoft is playing a big role. The only hitch is to get neural network in as small as a breadcrumb sized micro controller. The entire research process is in line with Microsoft’s growing indulgence in the area of AI and machine learning. Read more at:  http://analyticsindiamag.com/making-tiny-bits-smart-infusing-ai-onto-raspberry-pi/

Canada biggest bank entering towards an era of machine learning, and they are using machine learning techniques for their system. So, RBC (Royal Bank of Canada) is hiring more engineers than business people, where the programmers developed for uses technique that is machine learning. Moreover, the bank is developing a technique which would help them to work and load data efficiently. The important factor is the customers who deal their prices with more information to look at their risk. It is a vogue that the machine learning and artificial intelligence will boost the financial sector with big reforms. Read more at: http://www.cnbc.com/2017/03/29/rbc-capital-markets-machine-learning-trading.html 

Machine learning and AI are flooded with data and are most of the sectors in the world are using such technique. Machine learning is a technique without any human intervention and is very fast and efficient. It enables humans with new and extraordinary idea to understand things more prominently and helps in support marketing to understand market and customer more deeply and pull out the data more efficiently by all the means of the use of Machine Learning. To keep efficiency and make the decision more accurate in the field of customer oriented place, there is pool of data available with Machine learning solutions. Read more at: https://www.ama.org/publications/eNewsletters/MarketingInsightsNewsletter/Pages/machine-learning-artificial-intelligence.aspx?utm_content=bufferba2a8&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer 

A huge amount of digital data is getting piled up every day and to deal with that the technology recruiters are valuing the skills in data visualization, data science, machine learning and data analysis the most. These skills in data analysis help the companies to give more insight about the data and help to predict a better future. With the courses on data science people are now showing immense interests in machine learning and data visualization tools. Professionals are willing to upskill to keep pace with the automation. Read more at: http://economictimes.indiatimes.com/jobs/techies-reskill-to-log-on-to-big-data-deluge/articleshow/58103804.cms

Competition today is much fiercer than competition in yesteryears. In such a world getting a comparative advantage of machine learning would be beneficial. Though in their initial phase data analytics and machine learning faced criticism, the development of data structures and more accessibility helped to minimize criticism. Data is the source of information and the mining right kind of data would lead to significant results. There are four elements of data management, namely hybrid data management, data governance, data science and data analytics. Linking all the departments of a company is necessary to ensure free flow of information and accessibility of data. Read more at: https://readwrite.com/2017/06/21/competitive-advantage-machine-learning-dl1/

Prosumer- the proactive energy consumers who design and customize their products using smart devices to manage consumption, add renewables to the mix and look for personalized service from his or her utility. The energy consumer gets connected to utilities in terms of both demand and supply and this makes them more vulnerable to ransomware attacks. The energy security benefits are designed to provide security, adaptability and personalization to the consumers with the help of Augmented Intelligence (AI) which help the organization to communicate with consumers and the press. Enterprises can succeed by focusing on consumer personalization, security and the technologies. This can be done with the help of automation, predictive analytics and machine learning. Read more at:https://securityintelligence.com/personalizing-energy-security-with-robust-analytics/

With all the talk about predictive machine learning and deep learning applications, one can lose sight of the data engineering, some might call it data art that is needed to prepare the data to work on. Many questions go into the planning for deep learning applications like should the processing be disturbed; how much noise obscures the signal arriving from internet of things devices such as cell phones. In the case of mobile phone sensors, data preparation for deep learning applications can present unique problems, data preparation can involve considerable preprocessing. Insurance and other industries are entering the golden age of sensor data, but the data needs preprocessing because the data initially is very noisy. Given the Data, the algorithms will figure out the right transformations of the data. Read more at: http://searchdatamanagement.techtarget.com/news/450419925/Data-prep-for-deep-learning-applications-means-careful-planning

Business analytics help in taking decisions. Business intelligence involves analysis of prior analysis and is used to support tactical decision making. Machine learning involves processing historical data to make future predictions. There is scope to add applications to deal with consumers. Unless analysis is done in production application the business will not realize efficiencies. Such analysis can speed up daily activities. The prescriptive analysis makes best use of resources by establishing how processes should be executed. Artificial intelligence supports automation of processes and decision. Machine learning is under AI, but more advanced. The main issue is that the analysis and intelligence should be integrated into the working environment. Methods, culture and discipline will always be the key challenges. Read more at: https://www.gooddata.com/blog/key-strategies-for-profitable-business-analytics