In Python, we can create three types of methods in a class:  instance or regular method, classmethod and staticmethod.  Instance methods are associated, as the name infers, with an instance or object of the class and take self as the first parameter.  Classmethods take a reference to the class, cls, as the first parameter of the class.  Staticmethods, for the most part, are convenience methods that could be declared as functions since they really do not have much to do with the class itself.  They were probably added at some time after the advent of Python in order to make the language more object oriented i.e. minimize the number of free floating functions. 

Refer the our article static, class and regular methods in Python for a detailed explanation on this subject.

Many of us who have iPhones download every interesting app we find on the App Store, especially when they’re free. They can range from a simple payment method app, to a game, to a measurement tool. But, as you may have noticed, our phones become cluttered with tons of pages that we have to swipe through to get to an app that we need on demand. However, with an update by Apple that came out not so long ago, you are able to group your applications into categories that are easily accessible, for all of you organization lovers.

To achieve this grouping method, take a hold of one of the applications you want to categorize. Take a game for example. What you want to do is press your finger on that particular application, and hold it there until all of the applications on the screen begin to jiggle. This is where the magic happens. Drag it over to another game application you want to have in the same category, and release. Your applications should now be held in a little container on your screen. However, a step ago, if you did not have another game application on the same screen, and since you can’t swipe, try putting the held game application on any application you choose, and simply remove that extra application from the list, after moving over another gaming application from a different page.

With the rise of the smart phone, many people who have long seen themselves as non-gamers have began to download and play to occupy themselves throughout the day. If you're a game developer who has a history of writing your code in C#, then perhaps this still emerging market is something you should consider taking advantage of. This, however, will require the familiarization with other programming languages.

One option for moving away from the C# language is to learn Java. Java is the programming used for apps on the android platform, billions of phones run on this programming language.

If you want to break into the android market, then learning Java is an absolute must.

There are both some pros and some cons to learning java. Firstly, if you already know C# or other languages and understand how they work, then java will be relatively easy to learn due to having similar, but quite simplified, syntax to C-based languages, the class library is large and standardized, but also very well written, and you might find that it will improve the performance and portability of your creations. Not to mention, learning java opens you up to the entirety of the android app and game market, a very large and still growing market that would otherwise stay closed off to you. That's too much ad and sale money to risk missing out on.

The few cons that come with learning the language is that, when coming from other languages, the syntax may take some getting used to. This is true for most languages. The other problem is that you must be careful with the specifics of how you write your code. While java can be written in a very streamlined fashion, it's also possible to write working, but bulky, code that will slow down your programs. Practice makes perfect, and the knowledge to avoid such pitfalls within the language.

If you wish to develop for the iOS on the other hand, knowledge of Objective C is required. The most compelling reason to learn Objective C is the market that it will open you up to. According to the website AndroidAuthority.com, in the article "Google play vs. Apple app store", users of iPhones and other iOS devices are much more likely to spend money on apps rather than downloading free ones.

Though learning Objective C might be a far jump from someone who currently writes in C#, it's certainly learn-able with a little bit of practice.

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Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.