SCOPE OF MACHINE LEARNING IN MOBILE TECHNOLOGY

Machine learning is the application of the artificial intelligence which provided the systems to the ability of automatically learn and improve by the experience without the explicitly programmed.the development of computer programs is focused by the Machine Learning,which can access data and utilize learn for themselves.

The learning process by the observations and data,for example ,direct experience or instruction to look for the patterns in the data and make the better decisions for the future based upon the examples that we provide. The first aim is to allow the learning of computer automatically without any human interaction.

Machine Learning is the process in which the computer science uses statistical techniques to give the computer systems the ability to learn that is the performance of the specific task is improve.

The name machine learning was emerged in the year 1959 by Arthur Samuel.By the study of the the pattern recognition and computational learning in artificial intelligence ,it explores that the study for the construction of algorithms which can learn and make predictions on the data,that is the algorithms which is overcome  strictly static program instructions for making data driven by the decisions, and by the building model from sample input.Machine learning is the employment for the range of the computing task ,where the design and programming explicit the algorithm with the better performance is infeasible for example filtering mails etc.

Machine learning that is very closely to the computational statistics ,that is focused on the prediction making through the computers.It is the bond which bind up the mathematical optimization,which delivers method ,theory and application to the field.

MACHINE LEARNING APPLICATION AREAS

Machine learning Application that is we can say that it is very multidisciplinary field it can also find that the implementation at the intersection of technologies, science, and business.

APPLICATION OF MACHINE LEARNING IN ROBOTICS

In the vast field of robotics,engineering includes not in mechanism but in cognitive technologies also.We have the trust that the emergence of the era that robots help people on work and household, take care and entertain them.People have to mange these machine with the sound command and program tool actions with few steps on their mobile phone screen.

IMPLEMENTATION OF MACHINE LEARNING IN DATA MINING

The field of data mining serves that analyze the big data and that want to discover interesting, non-obvious relation with the set of data. That consists the data storage for  maintenance, and the actual analysis. Machine learning provides both the set of tools and the learning algorithm for discovering all possible contact. In this section we will talk about that how can machine learning help in mobile apps.

APPLICATION OF MACHINE LEARNING IN FINANCE

In the field of finance,machine learning algorithm are strongly used for the predicting future trends etc.For example in the bank ,if the anyone want to take the loan from bank so the banker wants to check their history of all transaction that their cibil should be clear .

SCOPE OF MACHINE LEARNING IN MOBILE APP

Machine learning is suitable for the action that we will take the closer look ,which make technology is better for your Machine learning mobile apps.

1.PERSONALIZATION

The possibilities is quit less, that the matching of mobile app’s functionalities with different groups of users.When we think about mobile app for transportation that is deal with both the clients and drivers or kids mobile apps where it is need to convince parents and children about the benefits provided by Mobile App by giving the information to each other.with the help of machine learning ,it is to analyze that the offer for the everybody ,what they really want.

2.PRODUCT SEARCH

Now in these days we find that there are so many e-commerce sites.when we want to  buy or search the product it is to automatically that some product is shown on the screen by the based on the previous purchases .On the other hand ,same is happening for the other business websites as well.many of the tools that make an app understanding the user nature with the all activities such as what they ranking,their favorite determination etc.

By given the corresponding most relevant result to the product search and showing most suggestions related to the search that is the most important for the e-commerce mobile apps,because small screen real estate make user more impatient and less attentive to scroll down the way to the last search. If yo don’t want to go away your visitors without finding what they are looking for,you have the one best thing that is machine learning.

3.PRODUCT RECOMMENDATION

By the filtering method recommendation are built such as site content analysis, purchase patterns, user behavior and the business logic and a brand implements.This will surely make the answers more relevant.

4.MACHINE LEARNING FOR HEALTHCARE APPS

Healthcare apps understand the large potential of machine learning for the sector and respective application. The algorithm has access the million of healthcare database for the correspondence to every different disease by the machine learning,it could suggest the best path for the treatment and medication.To understand this we put an example in front of you that IBM Watson have such a robust database of cancer patients can actually make the best diagnosis of the disease than the qualified medical professionals. same as the fitness tracking and costumer healthcare apps by the analyzing the data of million of people can offer valuable trends connecting the lifestyle and related diseases according to their recommendation.

5.FRAUD CONTROL AND SECURITY

Machine learning  is used to control the security arrangements and it also control on the fraud activity. Machine learning algorithm with the crucial application  can calculate the behavior of the user and all sorts of irregularities for  assess the most probable frauds and security vulnerabilities in the making.While a whopping $32 billion worth of the frauds occurring most of the year making an increasing number of financial transactions vulnerable and an application within the machine learning algorithm can also be detect such that the frauds and help to build a best defense system.

6.TREND FORECASTING

Every e-commerce company wants  to continuously changing trends and quick Chang in the product and services such that matching product etc. There is a huge difference in the past season and upcoming trends .Big data and machine learning can make it easy to use sales information from different sources such as social media, digital reports, blogs, etc to make predictions in real-time.

7.FAST AND PROTECTED AUTHENTICATION PROCESS

Applying machine learning to sum up with the different types of recognition that is including the newest one bio-metric and to pass user identity and authentication processes.It should be the best decision for the any kind of mobile apps including e-commerce.Machine learning is very huge used in mobile apps like zoom, that offer an simplest way of log into the other apps and website.It is to difficult that the people remember their password so why should not make it easier and faster.

STRUCTURE OF MACHINE LEARNING

various business depend on the machine learning to improve the operation. Machine learning will vary according to their requirement for the different field such as , a small data science team would have to collect, pre-process, and transform data, as well as train.We can understand the structure by the following stages that the data science team members who work on each stage, and the instruments they use.

 

  1. STRATEGY THAT IS MATCHING THE PROBLEM WITH THE SOLUTION

In the first stage the machine learning project realization, company representatives mostly outline strategic goals. They consider the solution of the problem to define the scope of work and plan the development. For example that e-commerce store sales are less than the expected.It is because the lack of the consumer behavior analysis may the reason.

 

  1. DATA SET PREPARATION AND PREPROCESSING

For the machine learning ,the foundation is the data.The next stage of machine learning project implementation is complex and involves data collection, selection, reprocessing, and transformation. All of these phases can be break into the several steps.

a)Data collection

b)Data visualization

c)Data selection

d)Data preprocessing

e)Data formatting

f)Data cleaning.

g)Data anonymization

h)Data sampling

i)Data transformation

j)Scaling

k)Decomposition

l)Aggregation

 

  1. DATA SET SPLITTING

Data set can be partitioned into three subsets — training, test, and validation sets.

a)Training set

In the training set to train a model and defining its optimal parameters -parameters it has to learn by the data.

b)Test set

It is useful for an evaluation of the trained model and its capability for generalization. It means to identify the old pattern and trained for new unseen data.

c)Validation set

It is for the purpose that a validation set is to tweak a model’s hyper parameters — higher-level structural arrangement that should not be directly learned from data

d)Data set-splitting

More get the data for the training more will be the better potential model will perform.

 

  1. MODELING

In this stage the data analyst trains numerous models for defining that one of them provides the most accurate predictions.

a)Model training

b)Supervised learning

c)Unsupervised learning

d)Model evaluation and testing

e)Cross-validation

f)Improving predictions with ensemble methods

g)Stacking

h)Bagging (bootstrap aggregating)

i)Boosting

 

  1. MODEL DEPLOYMENT

In the model deployment it cover the model into production use. If a data analyst choose a reliable model and specified its functional requirement,then he delegates that deployment for the data engineer.When the data comes to storing the small amount then a database administrator puts a model in the production.

 

 

  1. ADVANTAGE OF MACHINE LEARNING

1.machine learning is used in vast area of banking ,financial sector ,healthcare ,retails ,publishing etc.

2.The Google and Face book are widely used in machine learning that is to push the relevant advertisements and suggestion for relevant things or people.

3.Machine learning is used to finding and control data in multi variety in the dynamic environments.

4.Machine learning gives the permission to the time cycle reduction and efficient use of resources.

5.If anyone wants to provide the continuous quality that is large and complex process environments. There are number of tools present due to machine learning.

6.There are so many things that come to practical benefit of machine learning and they  involve the improvement of autonomous computers, software programs.It would have the processes that can lead to automation of the task.

DISADVANTAGE OF MACHINE  LEARNING

1.The main problem of machine learning is  Acquisition. On the basis of different algorithms data wants to be processed. Data have to be processed before providing the input to their respective algorithm.The significant impact on the result to be achieved.

2.Interpretation is one of the more term that we have ,it results is a greater challenge that need to determine the effectiveness of machine learning algorithm.

3.For all the above discussion we can say the use machine learning is to be limited.It should not be sure that the algorithm of the machine learning always work properly.In the most of the cases ,we have seen that the machine learning fails that would requires some understanding of the problem to apply the algorithm.

4.For learning the algorithm deeply ,machine learning needs the lot of practice on the algorithm.We have to say that it would be the cumbersome to work a large quantity of data.For image recognition purpose there are the lot of training data.

5.One of the limitation of the machine learning is that susceptibility to errors.”The actual problem with the inevitable fact ,” is  said by the Brynjolfsson and McAfee .When the error occurs it may be difficult to solve the problem because it will need going through the underlying complexities.

6.There are less possibilities to immediate predictions within the machine learning system.It would learn through the historical data. The and the longer that needs to expose to these data, the better it will perform.

7.Lack of variability is the main limitation of the machine learning.Machine learning deals with statistical truths,it also said by the Brynjolfsson and McAfee .It would be difficult that machine learning is nothing without historical data.Prediction is made by the system are suitable to all scenarios.

CONCLUSION

Machine learning is an innovative technology that can be the most useful for any of the mobile app.It can be the promise of the machine learning that mobile apps will be continue for growing path and no one can stop it .And the other thing is that the it becoming the trend for the biggest influence on the mobile apps of future.The market of machine learning grow continually and the scope of machine learning will be in the mobile apps as you are going to watch it among the mobile UX trends in upcoming years as well.

The quotes about machine learning which gives us the idea about machine learning that it would be advantage or disadvantage for humanity is that “Human think and worry that computers will get more smart and take over the new world, but the real problem is that they’re too stupid and they’ve already taken over the world.”

Then please let us know that AIS Mobile Apps is a software development company which has expertise resources in the field of machine learning and they have successfully created a very good bonding and relationship with large enterprise as well as small enterprise businesses.

 

 

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