When I hear the words ‘machine learning’, the first thought that pops up in mind is the unforgettable impression of all those famous books, movies and TV-shows describing different sides of the artificial intelligence (AI). We are here and this is our future, can you imagine? AI is finally on the verge of the full-fledged existing, and it’s progressing and evolving, it’s helping people in so many spheres like finances, law enforcements, medicine, etc.
For example, one of the recent crashes and my most favorite TV-show is ‘Person of Interest’, where an AI helps a government and a group of other caring people to calculate possibilities of the crimes and to predict a possible perpetrator or a victim. Just a concept itself fascinates me so much, that I decided to mash my excitement with the latest top mobile app development trend for 2017 – machine learning science.
‘’Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions through building a model from sample inputs.’’Wikipedia (c)
It’s a bit scary, right? Just to imagine that someday computers and robots will think and feel just like humans do. They could rise against us and overpower the humanity (hello there, my dear “Westworld’ and ‘Terminator’), they could take our jobs... Well, let me calm you down, this is still just our fantasies. Computers which can learn and adapt, in fact, more likely will be for the profit of humanity. On our behalf, it could save lives and resources...
So, for now, there is no drama, the people aren’t even reached the point where robots can mimic or learn natural behavior and humans’ emotion. And as for the Machine Learning concept, it’s more a list of algorithms for predictions based on the processed amount of information. Those results would ease our lives and save a lot of time and effort. It’s hardly an AI, but technologies are entwined, yes.
Digital devices are everywhere, and little do we know, that it’s machine learning (ML) that makes it possible to implement in our gadgets’ functions image recognition, predictions based on user’s preferences, analysis and classification of a huge cluster of data.
In modern mobile devices, the machine learning algorithms can process a massive amount of information and define the patterns for significant improvement of the way our gadgets serve us.
And it was Google who pioneered with the usage of machine learning algorithms after they launched computer and mobile software that uses neural networks for language translation and speech recognition. The tendency was picked up by Amazon company with their intelligent personal assistant Amazon Alexa and Apple with their Siri platform. Also, machine learning technology is used by YouTube and Uber, Snapchat and Microsoft Cortana.
So, as you see, the expansion of the hot technology is just booming, a demand for intellectual mobile apps is high as never. The future is waiting just for you, jump on board!
There are so many fields of usage of this wondrous science... We’re gonna take it niche by niche, stay put.
“Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.”Wikipedia (c)
According to the definition, the overall goal of the data mining is to extract information from a data set and transform it into an understandable structure for further use. It’s based on the data storage and management aspects, data pre-processing, model considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
The niche also allows analyzing big data, and ML here represents a set of tools and the learning algorithm to find all possible non-obvious connections in this data. The best example will be the recommendations in the mobile apps based on the history of user’s preferences or search requests, and “People you might know” function on social media.
This field is constantly growing with each day! Robots are already helping humans on jobs and housekeeping, entertain us and become some sort of companions for some people. And for these results, we should thank the implementation of machine learning technology which allows the engineers to combine mechanisms with cognitive mobile solutions. We manage those robots with our voice commands or special apps installed on our smartphones or iPhones. Little do we know, such applications also always have ML feature.
Here ML algorithms are designed to predict future trends, bad loans, and market crashes. The technology is used for analysis of client’s previous transactions and the estimation of his/her creditworthiness. Therefore, improving the defense and security systems for banks and enterprises. ML software allows to spot and stop a fraud.
Machine learning for eCommerce app gives us the ability to customize apps while using Big Data. Which leads to personalized recommendations, shopping lists analysis and trends forecasting.
So, here are the opportunities to make revenue grow by providing the customers with hot discounts on the favorite goods, which will ensure their loyalty to the app. Product search system, recommendation, promotions, and analytics for various users’ behavior.
This is just the top of the mountain. What else? Well...User Authentication, Image, audio and video recognition, Optical Character Recognition, Spam filters, Security algorithms, Navigation & Travel apps, Healthcare apps, Logistic and Supplying apps, Money management apps, Fitness apps.
And so more... Intrigued? We shall proceed then to a critical issue.
The main idea is that ML produces computer algorithms for the sake of programmers, who don’t need to write a code in this case. They only need to type the data into the algorithm which will do the rest, like automatically define patterns in the given data, find the way of solving the problem and achieve a most suitable result.
So, to create a machine learning mobile app you should have a basic knowledge and understanding of such things as an algorithm, computational statistics, mathematical optimization data analysis and so on. But we won’t make this a university lecture, instead, let’s explore the subject from the simple angle.
Knowledge is everything. Research the market, look for those apps like Uber, YouTube or Snapchat and study them to get better at understanding what you’re dealing with. It’s never bad to know your competitors as well. Also, prepare yourself for a long ride as a developer – if it’s your first project, go check our guide about 7 steps of mobile making.
Yeah, sound boring, uh? But data is a key to everything when it comes to ML science. You have to learn how to handle data clusters and sort the information to pick the right one for your algorithm. It’s crucial that a proper data set there or else your app risks suffering from wrong prediction capabilities.
Here is a nice and simple post on ML concept for any mobile app developer. In short, the more data you provide for the algorithm, the more accurate will be the results. Even for the greater good, we recommend finding some data scientist for this cause so he chooses the right method and parameters for the best outcome.
This stage will define the ultimate success of your project! There are various algorithms out there and it’s your top priority to select a right machine learning method that will suit your purposes. Keep in mind, that for the learning process to be as easy as possible, you have to choose a simpler model that will provide accurate predictions and results.
When you want to develop a mobile app with machine learning techniques, you should define the type of the app you want to create. You have two main options given the existing ML algorithms: multimedia content analysis or data mining direction.
The decision depends on the application’s purpose – what problems it would be solving and how large is the project’s budget. Each of these options will lead to different ML models, which is super influential regarding software solutions, therefore, your choice of the programming language for writing machine learning app code. For example, Python has a nice set of libraries for ML, as does Ruby along with available API services.
Actually, to learn more about the open-source APIs and SDKs we recommend this article called Intelligence in Mobile Applications, which gives a very useful overview of the coolest ML tools any developer will find useful. You’ll be introduced to services like Тensorflow, Wit.ai, Google Cloud Machine Learning APIs, IBM Watson, Amazon (with Polly & Lex). But even that is not all, you can also explore Microsoft Cognitive Services, AlchemyAPI etc.
Any ML algorithm will need a thorough and proper beta-testing. And then testing some more, in fact. It’s a serious matter you’re with, so, be prepared to stretch your limits and spend some money and time on the MVP for your app with already implemented ML model. We already established 4 main reasons why does any startupper need an MVP for his application. And how do you do it?
Well, make a list of the most valuable features and crucial functions that you want in your application, then prototype them entwined with ML method. To find out more visit the fossbytes.com, where you can get the access to ML tutorials, books, software and techniques along with links for video lessons. More so, there is a free course ‘Edx Course On Building Apps Using Machine Learning’ that will teach you how to create bots and to use algorithms and concepts of ML.
After you defined a business model, there is a need to concentrate on the future marketing campaign for the product to succeed, and then, there is the budget to calculate. We can’t tell about your related expenses, but as for the cost of the application itself... Let’s see?
You’ll have to hire the UI/UX designers, a team of developers, QA & PM engineers, a back-end developer... And do not forget that your choice of mobile platform (iOS or Android) will also influence the price. So, based on our approximate estimation, a custom machine learning mobile app will cost you probably around $70,000 – 300,000, depends on your project complexity.
Okay, there are some basics above, but the ML science is so bigger... Much to learn and to discover, some genius inventions are ahead of us. And if you choose the path of the machine learning implementation in the mobile development... there is a great adventure awaiting for you and some really tangible profit.
On the one hand, you’ll spend a lot of time and money, on the other – the expenses will be highly rewarded, believe us. Look up to giants like Google, Amazon, Facebook, IBM and Microsoft – they’re already investing hard in the ML industry to ensure our high-tech future.