4 latest Microsoft innovations that clicked and actually amazed people

4 latest Microsoft innovations that clicked and actually amazed people

Microsoft has rocked the world, still rocking and going to ROCK as well in the future…Here, Microsoft showcased again the best of 4 new innovations that really wins people heart.

 1. The Surface Tablet

When Apple launched an iPad back in 2010, the tablet market took a new height. People are fond to complement between a laptop and a smartphone, and many inventions have jumped up because of the device admiration. These are really awesome for the business, and they are truly super for play; however, the ability was somehow absent to perform with a comparative power as a computer or laptop. This was the story, until Microsoft released the much awaited Surface tablet. This is a great gadget which in general almost all users were thinking and expecting a device like this to be in market. It is small, with a touch screen, as well as a snap-on keyboard.

Windows gamble big on Windows 8 to work flawlessly on both the desktop and Surface ecosystems. The idea has resulted in a warm response with regard to desktop interface and windows 8 has demonstrated to be much satisfactory experience on the tablet as well. It may take some time to be user accustomed, but just like the Xbox, Microsoft is in it for the long haul on this one. The Surface stands to advance as more of people come into this market.

 2. Microsoft Sync

Recently, Microsoft and Ford have partnered to bring Sync. In many new cars, car users can use apps on the phone via voice activation which means one need to worry about picking up the phone if it rings. In addition to this, you can also use apps like Pandora (in US, NZ & AUS) or even start turn-by-turn directions without ever touching your phone or taking your eyes off the road. This is a big help to many drivers which stops driver distractions and can have better smooth driving experience.

 3. Xbox 360

In spite of Microsoft’s confusing communication regarding the Xbox One’s DRM policies, though now it is reversed, the Xbox 360 is still one of the highest selling media devices in the market. As of April, Forbes reported that Microsoft has a subscriber base of 46 million out of the total of 77 million total Xbox 360 units sold. That’s a pretty decent market share considering the competitiveness of the industry. Xbox 360 still remains a very profitable and highly regarded product in the consumer electronics industry.

4. Microsoft Office

Guy’s, how we can forget about Microsoft which has touched billions of heart and no talk on Microsoft can be complete without latest innovative enhancements of Microsoft Office. This is an elephant product of the company and nearly every computer in every home and office uses this software. It has become so popular that Microsoft has extended its availability on Apple’s OS and iOS as well. Ahaha, a well-deserved kudos for this. So, Office is ready to reach and rock everywhere now….

The new sleek look of Office 13 has got lot of customer attraction and users are simply loving it even more.

By saying this, Microsoft is always customer centric and lead the Industry and keep winning hearts with its technology and super amazing user experience.  

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Amoeba Technologies Core Team | www.amoebatechno.com


What is Machine Learning…Even Kids will get to know post reading this post

What is Machine Learning…

Learn in a hilarious way, what is Machine Learning…we are sure each and every one of you of any age group will get to know about Machine Learning, post reading this article….So, Happy Reading!!!

As we all know about Parrot fortune telling and you know what parrot does. Parrot sits before the fortune teller and the latter opens the cage and lets the parrot out. He instructs the parrot to pick a card for the patron. The parrot walks over to the cards, picks one from the stack or the spread with its beak and gives it to the astrologer. It then walks back inside its cage. The astrologer opens the card and based on the image tells the fortune of the patron.

Now, you might be thinking why we are telling all this and how it is related to machine learning, hahaha, the writer is crazy….Hold on your horses guys, you are about to understand the point…

So, similar is the working of Machine Learning where an organization let’s say Financial organization deals with multiple products and does the selling to their customers. Now, they want to take the next level of Business decisions and strategies to grow their business, so in short, organization in nutshell wants to know the future success story or how well they can align their business for better prospects. So, the data scientist or technologist take the customer business transaction data and feed it to the Machine Learning Software (playing the role of parrot) where internally in simple language is a package of different algorithms built out of statistical formulas and in turn output the predictive results on the basis to different past data trends and patterns fed.

Therefore, by this, Machine Learning tells and showcase the hidden patterns and share the predictions & could be the possible future by which organization leaders can align their Business Strategies well for the future growth of the business and better customer satisfaction.

Now, we are sure, it really does make sense by explaining it with Parrot fortune telling example.

To give you an example of Machine Learning software’s, to name a few in the market are SPSS, SAS and the latest GUI based Microsoft Solution on Cloud: Azure ML where you need not to be a data scientist or deep knowledge of the statistical algorithms, already the Task are predefined for the same, you just drag drop on the development pane, feed the data and it will give you the output result without any writing custom code, really amazing guys, we mean it.  

How it helps, to give you an example, let’s say in Medical Diagnosis: Given the symptoms exhibited in a patient and a database of anonymized patient records, predict whether the patient is likely to have an illness. A model of this decision problem could be used by a program to provide decision support to medical professionals. This is just one small example, the list is very long.

Hope this article helps you to understand what Machine Learning is…

Let us know in case of any questions.

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Difference between SMP and MPP, get to know here...

Difference between SMP and MPP

Hello guys, these days there is a lot of Buzz round MPP, so thought to share what it stands for and what is the difference between SMP and MPP.

Hope this article helps you understand the basic fundamental…..

SMP: Firstly, SMP stands for Symmetric Multi-Processing.

It is the processing of programs by multiple processors that share a common operating system and memory. A Single copy of the Operating System is in charge for all the Processors Running in an SMP. SMP can also be called as tightly coupled system or "Tightly Coupled Multi-Processing".

SMP is better than MMP systems when Online Transaction Processing is done, in which many users can access the same database to do a search with a relatively simple set of common transactions. One main advantage of SMP is its ability to dynamically balance the workload among computers (as a result serve more users at a faster rate) and this works effectively and is a go to option to manage terabyte of data, however, to manage multifold terabyte of data, MPP is the best to go option.

Example of SMP: Microsoft Fast Track Datawarehouse

MPP: MPP stands for Massively Parallel Processing. It is the processing of programs by multiple processors that work on different parts of the program and share different operating systems and memories. These different processors which run, communicate with each other through message interfaces.

An Interconnected arrangement of data paths allow messages to be sent between different processors which run for a single application or product. The setup for MPP is more complicated than SMP. An MPP system can also be called as a loosely coupled system.

An MPP is considered better than an SMP for applications that allow a number of databases to be searched in parallel with humongous amount of data.

Example of MPP: Microsoft Parallel Datawarehouse (PDW) / Microsoft Analytics Platform System

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Analytics with K Nearest Neighbor Classification

K Nearest Neighbor Classification

K Nearest Neighbor Classification is a pattern recognition algorithm. It is a non-parametric method used for classification and regression. In both cases, the input consists of the K closest examples.
we can consider each of the characteristics in our set as a different dimension in some space, and take the value an observation has for this characteristic to be its coordinate in that dimension, so getting a set of points in space. We can then consider the similarity of two points to be the distance between them in this space under some appropriate metric. The way in which the algorithm decides which of the points from the training set are similar enough to be considered when choosing the class to predict for a new observation is to pick the k closest data points to the new observation, and to take the most common class among these, thus called the k Nearest Neighbors algorithm.

The Algorithm
The algorithm can be summarized as:
1.    A positive integer k is specified, along with a new sample
2.    We select the k entries in our database which are closest to the new sample
3.    We find the most common classification of these entries
4.    This is the classification we give to the new sample.

The closeness can be identified by various distance measurements. Hence the distance method we choose effects the final results.


Let us Consider the following data concerning credit default. Age and Loan are two numerical variables (predictors) and Default is the target.

We can now use the training set to classify an unknown case (Age=48 and Loan=$142,000) using Euclidean distance. If K=1 then the nearest neighbor is the last case in the training set with Default=Y.
D = Sqrt[(48-33)^2 + (142000-150000)^2] = 8000.01  >> Default=Y

With K=3, there are two Default=Y and one Default=N out of three closest neighbors. The prediction for the unknown case is again Default=Y.

K-Nearest Neighbor algorithm in case of high number of dimensions and low number of training samples, "nearest" neighbor might be very far and in high dimensions "nearest" becomes meaningless. It is an easy to understand algorithm and handling of missing values is effective (restrict distance calculation to subspace).

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Have you heard about R Programming. Here we clarify....

R Programming

Just a letter and may be a way to future!!
R is a simple user friendly language used for interpret, interact and visualize data. It’s a software for statistical programming environment and a generic programming language concepts as they are implemented in a high level statistical language. It is especially designed for data analysis faster than users of legacy software, with flexibility in mix and match models for better results. Practical issues involved are to program, read data, access packages, and provide working examples.

Why go for it?
Data Analytics and Management has many challenges for any industry. The major challenges being volume and frequency of data, variation, identification of the most valuable pools of data. These problems can be simply cleared by using this language. We can find, download and use best community reviewed methods in statistics and predictive modeling from leading scientists in data science without any charge. Yes it is absolutely free!!

Steps involved in getting to know about R language is pretty simple and quick. All we need to do is write our code initially and then improvise the data using statistics. We can reinstate the data sets to figure out better analysis of the data. Data visualization can be done through various interactive plots and then, comes the final step of big data.

What’s so special about it?

R represents complex data showing beyond bar and line plots. Multidimensional data with multi-panel charts, 3-D surfaces and many more interesting info-graphs can be plotted. R programs are easily scripted and understandable for further passing on to produce research and deployable production.

Well, keeping things aside, R is used by data scientists worldwide to create social and media marketing to developing financial and climatic models that helps driving our economy and community.

We can sit back to observe the world changing with this software!!

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Know what Predictive Analysis is all about

Predictive Analysis

Can we know our future? Well, an interesting field to discuss. An enthusiastic reply would be may be or a yes. But clearly relying on such prediction for our daily needs? Can we think of the level of uncertainty and still proceed?

With an emphasis on the past data, upon rapid analyzing gives future predictive results which can be useful for business interests. Not just in for Business interests, we have been following these predictions in our daily activities without even thinking about it. And hence thereby we can influence the future simply from the data. Listening to data gives the best clues for our future.

“Will you get Diabetes as a heredity?”
“Will your company run in future with effective profits considering the inflation rates?”

“Stock prices to rise or fall”

Well the process is simply to get an insight into the data but cannot confirm anything for sure.

Where and how can we use it?
Predictive Analytics and data mining solutions for the enterprise are currently available from a number of companies including SAS (Predictive Analytics Suite), IBM (SPSS) and Microsoft (Dynamic CRM Analytics). Predictive analytics software can be deployed on premises for users or in cloud platform for team based initiatives.

How it works?
Predictive Analysis basically involves combining capabilities of predictive modeling, Big Data mining, Real-time Business Intelligence (BI), Data visualization and more to check emerging trends. These disciplines also involve rigorous data analysis, and are widely used in business for segmentation and decision making, but different purposes and the statistical techniques underlying them vary. We can detect and prevent threats to guide frontline decisions with future insights.

Can group of predictions make the future?
There have been many more interesting improvements in the core technology of predictive analytics. Persuasion modeling, which predicts influence - in order to do influence. The Obama campaign used it for 2012 presidential election; marketing uses it to persuade customers; and medicine uses it for selecting better patient treatments. Like the collective intelligence that spawns the wisdom of a crowd of people, we see the same effect with a crowd of predictive models. Each model alone may be totally primitive as of a few simple rules, so it might get prediction wrong a lot, as an individual person trying to predict also does. But having them come together as a group and there emerges a new level of predictive performance.

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Do you know what is Data Analytics and its purpose

Data Analytics

What is Data Analytics?
Data Analytics is an opportunity to find insights in types of data and content, to make the business more agile, and to answer questions that were previously considered beyond reach.

Why, we need to do it?
Every day, we create 2.5 Quintillion (10^18) bytes of data of which 90% of the data in the world today has been created in the last two years alone!!

Benefits of this includes creation of new products and services for customers. For example GE has made a huge investment in new services for its industrial products using data analytics. Apart from cost reduction using the data, faster and better decisions can be taken.

Getting data from various sources and in various formats, data analysis can be a challenge. The ability to import the data from these sources and analyze them to see the big picture is variably important. The data can be used to formulate Key Performance Indices (KPI’s).

We can identify relationships, patterns and analogies involving comprehensive analysis of data by using various techniques like regression, clustering to name a few.

Does this simply means proliferations of data is an evidence for an intrusive world? Or the data will play a useful economic model? With an understanding of how big the data is, harnessing it involves workforce to support business decision making and economic growth. To enable effective decision making it is important to have the potential value that big data can create economic benefits for organizations and sectors.

This article is just to bring awareness about the jargon of Data Analytics and we will take it up to the next of details and understanding in the coming set of articles. Happy Reading friends!!!

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