Contextual Integration refers in identifying meaningful relationships between different information types. This gives a multi-dimensional view of the data rather than a single access point. The best approach is to analyze these volumes of data from different perspectives. The traditional way is to follow a fragmented approach. The web teams, marketing and sales team will look at the different statistics offered by data. This lengthens the time to take decisions and also introduces inaccuracy. The need is to look at data from many angles to create a multi- dimensional profile of the customer. Then predictive analytics can assess and lead to intelligent messaging. Machine Learning is also helping to improve these predictive analytics algorithms by checking it on the real time data. Read more about it in the article written by Dominik Dahlem (Senior Data Scientist at Boxever) at: http://data-informed.com/contextual-integration-secret-weapon-predictive-analytics/

Failures in the functioning of equipment are inevitable in any kind of industry. The repair and recovery time often leads to big financial losses each year. But we have machine learning and predictive analytics as a solution. The machine learning models are trained to learn the ideal functioning of the machinery. Then this functioning is compared with how the machines are working at present. So if even a minor change occurs somewhere, it doesn't go unnoticed. Then, with the help of predictive analytics the loss that can take place in the near future is predicted. This adds to the huge advantage of the firms. Learn more about this in the article written by Mike Reed (manager of analytical services for Avantis PRiSM software) at: http://www.intelligentutility.com/article/16/02/saving-money-and-man-hours-machine-learning

Machine Learning is getting easier and accessible because of the computing power becoming affordable. Moreover, big enterprises are making their algorithm open source. This is because data is the food. More data an algorithm gets, the better it becomes. But from step 1, making algorithms, feeding data in humans play a significant role. Sometimes there are outliers which the algorithms cannot interpret. Here human intervention is necessary. They manually check such pieces. But when these are fed into algorithms, they make them robust by identifying outliers. Thus, human intervention is both necessary for accuracy and training. Read more at: http://insidebigdata.com/2016/01/11/human-in-the-loop-is-the-future-of-machine-learning/

Most businesses need a combination of multiple marketing techniques to exhaust the full potential and make it more productive. Though internet usage is increasing each day, but offline marketing channels still involve lots of customers. Best marketing strategies merge both offline and online marketing. This might seem a difficult task, but machine learning models make it easier. These lessons from the data field which channel, in what way, and at what time will be most effective for the particular set of customers. This is called intelligent messaging. Thus leading to maximize success of marketing. A significant increase in the success rate has been observed in the marketing strategies using these techniques. Learn more about it in the article written by Brendan O'Kane (managing director and chief executive officer of OtherLevels) at: http://www.itproportal.com/2016/01/04/intelligent-messaging-big-data-machine-learning-a-powerful-combination-for-multi-channel-marks/

The evolution of Machine Learning (ML) is affected by the approach of the tech giants towards it. Open Source Platforms and the data sources also have an important impact on the ML models. Tech giants have realized the importance of ML, and this is becoming the new normal for them. They are now focusing on providing ML models as a Service. These are built for the common usage, not just for the data scientists. Most of the softwares being used for ML are open sources, thus affecting the market of other softwares making sources. Tools like Apache Spark are going to dominate the market. Read more about it on: http://www.infoworld.com/article/3017251/data-science/what-machine-learning-will-gain-in-2016.html

Machine Learning (ML) will have a big impact on economics as it helps in taking properly calculated decisions. The ML methods can be modified to make them fit for using in econometrics. These reduce the risk to failure. Data is the key to success for this. Based on the previous records, a model can be trained. Economists are being increasingly interested in technology. ML can help in clarifying the real situation from what is being assumed. Many technical firms are hiring economists. The mix of proper proportion of technology with the knowledge of economists will definitely lead to great results. Read more at: http://www.forbes.com/sites/quora/2016/01/27/what-will-the-impact-of-machine-learning-be-on-economics/#5bccf597ad5e

Ever wondered if machine learning can be used for Data Driven Marketing. The Intelligent Agent (IA) developed on the concept of machine learning takes a big leap in data driven marketing.  IA technology can learn about the user behavior and suggest most suitable actions. It can identify the most relevant data from the whole lot and can even find out the reasons for the importance or relevance of that data. Artificial Intelligence is revolutionalising the field of marketing. Intelligent Agent would reduce the manual processing and will produce better results. Read more about it in the article written by John Bates(Senior Product Manager for Data Science & Predictive Marketing Solutions) at: http://blogs.adobe.com/digitalmarketing/analytics/data-driven-marketing-ready-for-machine-learning/

We've been thinking about the Internet of Things all wrong. According to various predictions by various companies, there were various statements specifying volume and amount of money, number of connections. These are just numbers, Numbers, more numbers. If we believe in the predictions, there is no way that current analytical solutions can manage that level of information. In the immediate future artificial intelligence capabilities are required. Which means all companies who have an analytics platform play will have to invest in A.I. research, acquire and finally emerge with solutions based on methods beyond machine learning. Or risk being left behind. If this sounds vaguely familiar, it's because right now all efforts are pointing towards machine learning and algorithms as the goal for analytics. To read more visit on: http://www.forbes.com/sites/theopriestley/2015/12/08/a-series-of-unfortunate-tech-predictions-artificial-intelligence-and-iot-are-inseparable/#39f25ec8523a1253d985523a

Cybersecurity is a very important and is becoming one of the biggest worries for companies. According to various surveys, companies are investing a lot of money in cyber security and training their employees in it. It is estimated that till 2020 investment in the cybersecurity will be around $170 billion. In today’s world as the data is rising, so the risks on it are also increasing. The pattern classification systems that machine-learning algorithm rely on themselves exhibit vulnerabilities that can be exploited by hackers. As we know machine learning algorithms train themselves with the training data set so it may be manipulated according to the needs as hacker wants. For example, Search-engine-optimization algorithm was trained and manipulate website content to boost results in the search ranking or senders of junk emails try to fool spam-filtering algorithm. Even the results of the public election can also be affected by 20% or more as it is found that the order in which candidates appear in search results can have significant impact on perception. To read more about Cybersecurity risks, follow the article by Dr. Kira Radinsky (CTO and Co-founder of SalesPredict) at: http://blog.kiraradinsky.com/author/kiraradinsky/

When we talk about Artificial intelligence, we always come across the question,' will there ever be possible that machine replaces humans and preforming better than us?' The answer is partially 'yes', as at least in many things machines are performing as unchallenged champions of creativity and intelligence. Areas where artificial intelligence already performing better than humans are.

• Search the web quicker. Machine learning AI helps in web engine optimization through understanding the meaning of words and phrases, and can therefore guess what should be in the page ranking in never seen before searches.
• Work in deadly environments. Robots can survive in conditions where humans can’t like deep space, the oceans penthouse, or inside a radioactive reactor.
• Get a PhD quickly. Few critics of AI argue that machines could never be creative, or curious, or discover anything of significance, but team at Tufts have proved the naysayers wrong.
• Deliver a correct medical diagnosis.
• Translate in many languages.

Read more at: http://www.huffingtonpost.com/george-zarkadakis/5-things-ai-can-do-better_b_8906570.html?utm_hp_ref=technology&ir=Technology

Why to go far? If we see in our recent past artificial intelligence and machine learning were very exciting and dream topics among engineers and developers. But now machine learning has emerged as the ideal branch of big data and working as oxygen to concepts like artificial intelligence. Year 2015, was a year of massive market shifts. Machine learning (ML) and its super set artificial intelligence (AI) where computer receives new information and learn without supervision have played very important and revolutionary role for the shift. Still Machine Learning has much more in the store. Year 2016 is going be a big year for machine learning. Usually, computers have been used to enhance the ability to carry out tasks. Users see this with features like auto completion and spell check. In the upcoming year these leaps are likely to be made on three fronts: natural language processing, personalization, and security.  To know more about machine learning follow the article written by Motti Nisani(author) at: - http://www.geektime.com/2015/12/27/2015s-big-leap-into-machine-learning/

If we talk about the recent past, machine learning was only used by a few who understood the algorithms and had access to very huge amount of data on which to employ it. But with the evolution of big data technology becoming a commodity and algorithms easier to use, machine learning has moved out of the hands of the few to the hands of the citizen developers and regular users. Four key steps taken in 2015 for the development of the machine learning are:-

# learning became easier to use.

# Everyone and their brother released a machine learning library or toolkit. 

#Big data to feed machine learning also became cheaper and easier.

#The label “machine learning” was applied to way too many items.

Read more at: http://www.infoworld.com/article/3017250/application-development/4-great-leaps-machine-learning-made-in-2015.html

As the field of Data Science is growing, the confusion regarding the skills needed to be a data scientist is also increasing. Most of us think data science skills range from computer science and statistics, to machine learning and strong communication. But, the top data science skills list includes data analysis at the top, followed by others like R, Python and machine learning. As per recruiter lists, R, Python, SQL, SAS and Hadoop are appreciated. To know more about data science skills, follow the article written by Daniel Levine (Content Marketer for RJMetrics) at: http://www.smartdatacollective.com/daniellevine/366486/top-20-data-science-skills

Big data analytics require skilled data scientists who are paid unreasonably high amount of money, because of their ability to ask right question and create the most effective algorithm in order to extract meaningful information from tons of data. But, not anymore. Researchers at MIT teamed had developed a machine of automated analytics that explores patters and designs in data structures. Read more at:- http://blogs.csc.com/2015/10/16/can-automated-analytics-reduce-need-for-data-scientists/

One area of analytics is machine learning. In earlier days, large companies could buy workstations and specialist software and employ highly educated people to look at their CRM data. The big change that makes machine learning with CRM data necessary today is the change in how customer interactions take place. Read more at: : http://it.toolbox.com/blogs/insidecrm/let-the-machines-do-the-marketing-machine-learning-with-crm-data-70746

Earlier we have seen that CRM required manual input to fill in notes from sales activities and collect information about prospect behavior. But, nowadays, this is done with the help of technology and machine learning is one such technology. Machine learning is helping sales teams to fill in the notes. But, first we must know what machine learning is. It refers to a computerized data analysis where algorithms learn from new information and quickly decide what the next best action is. Web search results, credit scoring and email spam filtering are powered by machine learning algorithms nowadays. Such technologies are extending to CRM also. To know more, follow: http://it.toolbox.com/blogs/insidecrm/machine-learning-your-way-to-better-crm-69781

Over a decade ago, High frequency trading (HFT) used to be part of very few financial firms, but now it is an integral part of every major financial firm and is key to drive the success of these firms. Many industry experts in the field are of an opinion that big data has started entering into the financial sector at a minute level for now and it will follow the same trend like the HFT in expanding into ever major company. It will be the major decisive factor in taking many calls in near future. On the technical level, many experts feel Machine Learning (ML) will take a dominating role in areas where Statistical techniques are now used for finance and risk management. ML with its ever increasing algorithms/techniques is an ideal replacement to humans in trading scenario, though it has its own caveats. It is seen that ML and Big data is going to lead a new revolution into the field of Finance. To read more: 

http://www.automatedtrader.net/headlines/153852/gdtrm15-machine-learning-is-the-new-c%2B%2B

Natural Language Processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human languages. NLP executes various tasks by understanding the language. It converts it into a machine representable format to tell the computer what needs to be done. An important aspect of NLP is Natural Language Generation (NLG). It takes the input, processes it and thereby helps to generate natural language back to the user. NLP uses machine learning in order to determine language from bunch of data. It is crucial to demonstrate milieu and subject of each word. NLP has its applications in various fields. It is also used to categorize text into different sections and also to translate the language.

For further details on Natural Language Processing, please follow the link: https://channels.theinnovationenterprise.com/articles/a-beginners-guide-to-nlp

Machine learning has showed tremendous potential to transform companies from inside out. Everyday new algorithms are coming up that are being used to encounter data and tackle new problems. On the other hand, a closer look at machine learning reveals it to be nothing more than a branch of statistics for a world of big data. Business executives with a thorough understanding of machine learning have the ability to reach efficient business outcomes. In this age of data, firms have to work with large scale data. Both advanced software and hardware is needed to manage, analyze and store it. Herein lies the applicability of machine learning. To know more, please follow: http://www.dataversity.net/what-business-execs-need-to-know-about-machine-learning/

Just as sourcing ingredients is essential to the quality of a great dish, the quality of labels determines the accuracy of a supervised or semi-supervised machine learning (ML) solution. What is a label?
Data labeling involves taking unclassified data and augmenting each piece of that data with some sort of meaningful ‘tag’, ‘label’, or ‘class’ that is somehow informative or desirable to know. Assigning a label is a judgment task to be performed by an analyst depending upon the kind of variables he/she wants to work with and is an essential part of the ML process.
How to ask Questions?
All tasks involving labels involves, at some point, asking a question to a human being for collecting the data. The questions should be relevant, clear and precise in addition to being plain and understandable and one for which answering does not involve much effort for the individual.
How to debug tasks?
The collected data may sometimes show discrepancies due to:
• Data: Certain factors which can cause bias in the workers have to be eliminated.
• Workers: We have to detect the expected errors arising due to human involvements, say, an error arising due to spammers rather than people making genuine mistakes.
• Tasks: If the problem still persists then there might be a problem with our initial assumptions. Time to rethink from the start.
How to assess Work Quality?
Specific domain based algorithms exist for every point during the project that determine the ongoing quality of work.
Labels are essential and cutting on effort in this regards is most likely to lead you to erroneous results.

For more information visit:
http://blogs.technet.com/b/machinelearning/archive/2015/06/23/label-quality-for-machine-learning.aspx