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Solutions And Challenges Pertaining to Machine Learning
Like any other technology, there is always a series of challenges attached to machine learning as well. The basic working principle behind machine learning is the availability of enough resource data as a training sample. And as a benchmark of learning, the size of training sample data should be large enough so as to ensure a fundamental perfection in machine learning algorithm. Here are the methods that help to avoid risks of misinterpretation of visual cues or any other digital information by the machine or mobile application
Hard Sample Mining: When a subject consists of several objects similar to the main object, the machine is ought to confuse between those objects if the sample size provided for analysis as the example if not big enough. Differentiating between different objects with the help of multiple examples is how the machine learns to analyze which object is the central object.
Data addition imitation : In this method, some of the data is nullified keeping only the information about the central object. This is done so that the machine memory only contains the data regarding the main subject image and not about the surrounding objects.
Data Augmentation: When there is an image in question in which the machine or mobile application is required to identify a central image, there should be modifications made to the entire image keeping the subject unchanged, thereby enabling the app to register the main object in a variety of environments.
Benefits of Machine Learning:
Advanced Search: Machine learning app ideas let you optimize search options in your mobile applications. ML makes the search results more intuitive and contextual for its users. ML algorithms learn from the different queries put by customers and prioritize the results based on those queries. In fact, not only search algorithms, modern mobile applications allow you to gather all the user data including search histories and typical actions.
Predicting User Behavior: The biggest advantage of machine learning app development for marketers is that they get an understanding of users’ preferences and behavior pattern by inspection of different kind of data concerning the age, gender, location, search histories, app usage frequency, etc. This data is the key to improving the effectiveness of your application and marketing efforts.
Machine Learning and E-commerce:
Healthcare Apps:
The use of machine learning in e-commerce mobile apps can provide relevant information to users while they search for products. With its help, the app can recommend them the right products based on their interests, and even analyze the fashion trends and sales information and give predictions in real-time.
In the healthcare apps niche, machine learning can play the role of doctor/adviser. So it could analyze the symptoms and give the needed solutions.
Sports Forecasting Apps:
For the sports forecasting mobile apps, machine learning can be of great help. Machine learning model written right can predict the outcome of any sports game with an extreme accuracy.
These ML apps also can forecast the possibility of a headache and recommend ways to prevent one.
Finance Apps: Finance mobile apps with machine learning implementation can analyze the history of previous transactions and utilize the historical data to offer users unique deals that are going to be perfect for each specific user.
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