Technology has made the mobile app space more relatable. After developing over 3500 mobile apps, and constantly updating ourselves with Artificial Intelligence (AI), Augmented Reality (AR), Machine Learning Technology (ML) trends, we, along with our mobile app development consultant decided to give you a gist about how machine learning app works and machine learning app development areas.
Have you ever shopped online? So, while checking for a product you must have noticed when it recommends a product similar to what you are looking for? Or it also provides you with products bought by another person. How are they recommending you? Machine Learning is the answer to that.
The era of generic services is diminishing.
The technological advancements have taken the mobile apps world on whole new automation. Through machine learning cognitive technology mobile application development companies create algorithms and machines that understand humans, assist them in their tasks and even entertain them.
It gives a whole new experience to the users, making them more capable of leveraging features, accurate location-based recommendations or instantaneously detecting micro diseases. The technology makes a mobile app solution more user-friendly, improves the customer experience, maintains customer loyalty, brand awareness, and target audience filtration.
Basically, ML resembles a new era in software development, where electronic gadgets will not require special programming to complete the tasks. The gadgets can easily accumulate information and can analyze the same to draw appropriate conclusions and learn during the performance.
To eliminate all your confusion related to machine learning technology, our experienced mobile app developers, at Space-O Technologies, did a demo experiment so that you can get an idea about how it actually works.
Here in this example, we have experimented with “image recognition,” which is one of the most used machine learning tactics. We have performed this experiment by taking a glass with different amounts of water in it.
First of all, we collected photos of empty glass, half watered glass, full watered glass, and targeted into the machine learning algorithms. This helped the machine to search and analyze according to the nature, behavior, and pattern of the object placed in front of it.
- The first photo that we targeted through machine learning algorithms was to recognize an empty glass. Thus, the app did its analysis and search for the correct answer, we provided it with certain empty glass images prior to the experiment.
- The other photo that we targeted was a half water glass. The core of machine learning app is to assemble data and to manage it as per its analysis. It was able to recognize the image accurately because of the little bits and pieces of the glass given to it beforehand.
- The last one is a full glass recognition image.
Note: For correct recognition, there has to be 1 label which carries at least 100 images of a particular object.
Machine learning through mobile app solutions can benefit your business by a continual learning process. It can classify user based on their interests, collects user information and decides on the app’s look. It analyzes information from various sources like social media activity for credit ratings and recommendations onto the customer’s device.
We, at Space-O Technologies, are known for our market research before applying any technology to the mobile app. Here, we have found some viable facts for you to rely upon and know the scope of this advancement in the coming years.
Machine Learning Statistics
- Machine learning is known as a digital transformation which is assumed to be of $58 billion by the end of 2021 cumulative investment.
- The global ML industry growth is predicted to reach almost $9 billion in the latter part of 2022 at a CAGR of 42 percent.
- The neural network market is expected to worth over $23 billion by 2024.
Now, after studying the machine learning tutorials and statistics about the raging ML technology, let us take you to the actual process of machine learning.
How to Develop Machine Learning App? 5 Steps
To ace in the machine learning technology, the most important thing is to train the ML algorithms. According to the data given to the machine, it trains itself effectively over time. It is basically a 5 step process. Let’s have a look at the machine learning process with diagram.
Collection & filtration of the data
This step is all about data representation. The technology works accurately on the basis of quality and quantity of the data. After gathering all your data, make sure that you remove duplicates, correct errors like missing values.
The data has to be randomized so that it erases the effects of the particular order in which you collected or otherwise prepared the data. Perform exploratory analysis and split into training and evaluation sets.
Choose and train a model
There are different tasks that are fulfilled by the different algorithms of machine learning technology. A suitable algorithm has to be chosen and trained to answer a question or make a prediction correctly. Thus, the machine needs to learn values for a and b (x is the input and y is the output)
There are certain things to be taken care of while training a machine learning model:
Assume that you are opting to train the machine in object recognition. There are certain specifications to be followed when it comes to training a model.
- Take a proper video of the object. The video has to be of 1 to 2 minutes.
- Cover the object from all sides possible. A 360° capturing will help the machine to have in-depth knowledge about the nature of the object.
- Make sure that your object is captured in different resolutions, lightning, and background conditions to know its exact behaviouristic patterns.
So, these are some predefined conditions, that one has to follow while planning an object recognition training under machine learning technology.
The Machine Learning Workflow
Evaluate the model
For evaluating or testing the training of the machine, one has to represent unseen data. This unseen data is meant to be a representative of the real world but helps in tuning the model as an opposition to the tested data. In simple language, this metric allows you to see how the model might perform in the real model.
After evaluation, it’s possible that you might want to see if you can further improve your training in any way. This can be done by parameters tuning. Always assume a few parameters while training your model.
For instance, how many times can you run the training dataset during the training? Show the database to the model several times, rather than just once. This will lead your model functioning to higher accuracy.
For predictions, use the data, which you have not used until this time. Test your model with the unused data which gives an approximation of how the model will perform in the real world.
Using further data which are withheld until this time from the model are used to test the model and get an approximation of how the model will perform in the real world.
Now, after knowing the process, it is very important for you to learn the types of machine learning application integrations. Let’s take a closer look at the three kinds of MLS.
- Face recognition
- Image recognition
- Object recognition
3 Uses of Machine Learning
1. Face recognition
Face recognition takes place when it finds a face in the image and highlights the area. The Hair, a black and white mask is superimposed on different parts of the face. The algorithm adds the brightness of all pixels of the image that are under black-white parts of the mask and then calculates the difference between the values.
The recognized image is then compared with the system accumulated data, and having determined the face in the image, continues to track it to select the optimum angle and image quality. This process is performed by motion vector prediction algorithms or correlation algorithms.
Having chosen a suitable image, the system proceeds to face recognition and its comparison with the existing base. The most important measurements for the face recognition program is the distance between the eyes, the length of the nose, the height and weight of the cheekbones, the height of the forehead and other parameters.
After measuring all these with the database, if the parameters coincide, the person is identified. If it comes to machine learning app development, face recognition is the widely used technology as it helps in the photo and video editing apps, camera apps and other different types of apps.
2. Image recognition
As we discussed earlier, with the help of training data, we train the machine. In this case, we have a bunch of images which are tested and trained, removing duplicates (or new duplicates) between them. Then, the data is fed into the model to recognize the images.
The challenge in building image recognition is hardware processing power and cleansing of input data. It can be possible that most of the images might be high definitions. Such images value a lot more on the machine learning model. The calculations are not easy addition or multiplication, but complex derivatives involving floating-point weights and matrices, thus such machine learning applications take time in making from scratch. However, it is widely used when developing eCommerce apps, social networking apps and gaming apps.
3. Object recognition
To recognize objects from a given photograph, the machine locates the presence of the objects. When our clients inquire for “object recognition” they often mean “object detection.” Detection is a part of the recognition, and so the process goes by image classification and localizing it into the machine neurons.
For instance, if you are planning on a machine learning app development with object recognition then the input and output will be.
Input: Images with more than one object, such as a photograph
Output: One or more bounding boxes (e.g. defined by a point, width, and height), and a class label for each bounding box.
Thus, this was a quick tour on machine learning app-based integrations. We have over 200 app developers at Space-O Technologies who are highly qualified and knowledgeable in learning new mobile app trends. As people are getting used to such convenience, mobile apps have a long way to go. Now, let’s have a look at industries that are benefited the most by using this technology.
Machine Learning Application Areas
1. E-Commerce App Development: The machine learning app can help in product search. Our mobile app developers provide solutions like ranking, query understanding, expansion of product category.
For instance, to know the ranking of a product through machine learning, “click ratio” or “selling rate” are considered. This helps a machine learning app development in learning user behavior by his search history.
It can also help in analyzing or searching product recommendations and promotions, trend forecasting and analytics, fraud detection and prevention. These are all the fruitful outcomes to become a huge successful app like Amazon, eBay.
2. Healthcare App Development: Through face recognition machine learning, the skin neurons can easily detect a particular skin disease for the skin specialist. The doctor’s clinics can have individual patient’s face recognition to avoid any mishappening with the medications. Also, there can be security soundness with enhanced technological advancements.
3. Crime and Security App Development: Machine learning apps can help in reducing human trafficking. The child beggars can be detected which can, in turn, result in a lot of crime rates. Smuggling, robbing, child rackets, thus, it is the most helpful digital transformation.
4. Finance App Development: The finance industry can use it for future analysis and predictions. ML can help in the searching history of previous transactions, social media activities to determine the credit rating, and portfolio recommendations.
5. Photo And Video App Development: Through machine learning app, a user can easily fetch a particular individual’s photo, easily via face recognition. Thus, if a user has over 1000 photos in his application, he can easily find a particular photo at once.
Still have any query or confusion regarding machine learning app development, the difference between Artificial Intelligence and machine learning, ML algorithms from scratch, how this technology is helpful for your business? Just fill our contact us form. Our sales representatives will get back to you shortly and resolve your queries. The consultation is absolutely free of cost.
Author Bio: This blog is written with the help of Jigar Mistry, who has over 13 years of experience in the web and mobile app development industry. He has guided to develop over 200 mobile apps and has special expertise in different mobile app categories like Uber like apps, Health and Fitness apps, On-Demand apps and Machine Learning apps. So, we took his help to write this complete guide on machine learning technology and machine app development areas.
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