A Comprehensive Guide on Machine Learning for Mobile Devices

Hey everyone its me Badari , today we are dealing with an interesting topic. I think everyone should be have anxiety about this one , okay lets go through some basics about it.

We’re living in a world of mobile applications. They’ve become such a part and parcel of our everyday lives that we rarely look into the numbers behind them. (These include the revenue they make, the actual market size of the business, and the quantitative figures that would fuel the growth of mobile applications.)

Mobile devices and services are now the hubs for people’s entertainment and business lives, as well as for communication. The smartphone has replaced the PC as the most important smart connected device. Mobile innovations, new business models, and mobile technologies are transforming every walk of human life.

Now, we come to machine learning. Why has machine learning been booming recently? Machine learning is not a new subject. It existed over 10–20 years ago, so why is it in focus now and why is everyone talking about it? The reason is simple: data explosion. Social networking and mobile devices have enabled the generation of user data like never before. Ten years ago, you didn’t have images uploaded to the cloud like you do today because mobile phone penetration then cannot be compared to what it is today. The 4G connection makes it possible even to live stream video data on-demand (VDO) now, so it means more data is running all around the world like never before. The next era is predicted to be the era of the internet of things (IOT), where there is going to be more data-sensor-based data.

All this data is valuable only when we can put it to proper use, derive insights that bring value to us, and bring about unseen data patterns that provide new business opportunities. So, for this to happen, machine learning is the right tool to unlock the stored value in these piles and piles of data that are being accumulated each day.

Machine learning in the mobile space is a key innovation area that must be properly understood by mobile developers as it is transforming the way users can visualize and utilize mobile applications. So, how can machine learning transform mobile applications and convert them into applications that are any user’s dream?

When is it appropriate to go for machine learning systems?

Is machine learning applicable to all scenarios? When exactly should we have the machine learn rather than directly programming the machine with instructions to carry out the task?

Machine learning systems are not knowledge-based systems. In knowledge-based systems, we can directly use the knowledge to codify all possible rules to infer a solution. We go for machine learning when such codification of instructions is not straightforward. Machine learning programs will be more applicable in the following scenarios:

• Very complex tasks that are difficult to program: There are regular tasks humans perform, such as speaking, driving, seeing and recognizing things, tasting, and classifying things by looking at them, which seem so simple to us. But, we do not know how our brains are wired or programmed or what rules need to be defined to perform all this seamlessly, for which we could create a program to replicate these actions. It is possible through machine learning to perform some of them, not to the extent that humans do, but machine learning has great potential here.
• Very complex tasks that deal with a huge volume of data: There are tasks that include analyzing huge volumes of data and finding hidden patterns, or coming up with new correlations in the data, that are not humanly possible. Machine learning is helpful for tasks for which we do not humanly know the steps to arrive at a solution and which are so complex in nature due to the various solution possibilities that it is not humanly possible to determine solutions.
• Adapting to changes in environment and data: A program hardcoded with a set of instructions cannot adapt itself to the changing environment and is not capable of scaling up to new environments. Both of these can be achieved using machine learning programs.

The model-building phase consists of many sub steps , as indicated earlier, such as the selection of an appropriate machine learning algorithm, training the model, testing it, evaluating the model to determine whether the objectives have been achieved, and, if not, entering into the retraining phase by either selecting the same algorithm with different datasets or selecting an entirely new algorithm till the objectives are reached.

Android supports a wide variety of machine learning tools and methods: The ML design guides, Google’s turnkey machine learning SDK ML Kit, TF Hub for finding pre-built cutting edge models, TF Lite Model Maker to train an existing model with your own data, and ML Kit custom models and Android Studio for integrating these models into your app.

As a subfield of artificial intelligence, machine learning keeps changing a growing number of industries. Using algorithms that are constantly learning from data, machine learning allows computers to find such insights as detecting credit card fraud, optimizing manufacturing processes, predicting customer purchase behavior and the personal interests of Web users. It raises the question about how computers might automatically learn from past experiences. Thus, the special data management system uses near-real-time analytics to determine normal behavior, single out anomalies, compare the samples to historical data, and summarize empirical regularities. Due to their high accuracy, these predictions can guide smart actions without human intervention. Machine learning app development has the power of making a mobile app more intelligent. It also means that the tasks are completed without any special programming.

Mobile ML impact on businesses

The number of smart or artificial intelligence and machine learning applications is constantly growing. Although there are still many apps which are written with a fixed algorithm and do not adjust by the data received, it will change in the nearest future. Users are looking for intuitive and easy ways to satisfy their needs. Fortunately, ML app development implies fetching predictions for apps without execution of custom prediction generation code. AI is not only opening up opportunities for businesses, it also allows them to respond to customers’ inquiries much more quickly, primarily via mobile devices. Market leaders are therefore currently incorporating ML into their products, since the advanced techniques for machine learning app development and ML algorithms, in turn, can adjust the apps to make them personalized.

Conclusion

Machine learning algorithms are a mysterious game changer. However, they do adjust mobile applications to create meaningful and personalized experiences. These apps can also give their users the needed functionality and content driving innovation across every industry. The user and the intelligent system interact with each other primarily to improve the system’s accuracy. Although the machine learning technology is still in its infancy, human-computer collaboration is a promising direction for machine learning systems to work more intelligently. This suggests that companies and developers who are still in doubt should put all doubts to rest, try using ML and see how they can benefit from it.