Skipping the implementation, we’ll see to the best use case, applications, purpose and scope of Machine Learning in Mobile App Development!
Machine Learning comprises computer programs that use algorithms to analyze data and make intelligent predictions based on the data without explicit programming.
To understand it better, let’s assume:
Problem Statement 1: Predicting the House Prices.
On basis of the size of the house (x), you want to predict the price of the house (y). So we make a function f(x) to map input variable with output variable y: f(x) -> y. That’s the definition of machine learning. Also, there can be multiple input variables like the size of the house, number of rooms within, the location of the house, the city, the country, number of fans within and likewise. These can be shown as X2, X3, X4. Likewise, we can make another function: f(X2, X3, X4) -> Price of the house.
Alternatively, machine learning can be a computer program that learns from experience E concerning some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
Problem Statement 2: Email Spam Detection System.
From the definition above,
T = 0/1 Detecting a spam
E = Experience of detection
P = How much performance increases upon detection
So, we need to make a function ‘f’ which maps the input variables with the output variables.
After understanding the problem statement, we need to train an algorithm. It is just a function (f(x)) that associates input variables with the output variables. The next step is to evaluate the emails and check if it is correct or not. If it works well, then launch the system, do error analysis, and again loop back to fine-tune your algorithm. This way ML is an iterative process.
When Do We Need Machine Learning?
Machine Learning involves improving customer experience based on their “experience”, the problem’s complexity, and the need for adaptivity. It automates and accentuates routine tasks. It can turn medical archives into medical knowledge, predict the weather, analyze genomic data, web search engines, electronic commerce, and astronomical data. ML detects meaningful patterns in large and complex data sets (big data) combining programs that learn with unlimited memory capacity and the ever-increasing processing speed of computers opens up new horizons.
Where is Machine Learning applicable?
- Self-Driving Cars
- Real Estate
- Predicting Stock Price
- Disease Prediction
- Cancer Detection
- Speech recognition programs
- Adapting automatically to changes like spam e-mails
- Spam detection programs
- Programs that decode handwritten text
- And much more…
ML Use Cases
A ride-hailing app or food-delivery app service can use data from previous rides and apply machine learning to accurately estimate the arrival time, and cost of the trip based on traffic, time of day, and weather conditions.
Camera apps can be used to reduce noise, correct HDR, exposure by taking multiple shots, analyzing them, create a cleaner, and better-looking resultant image.
ML can also be implemented to identify objects – in shopping apps a user can point to an item and the app will automatically find matching results online.
It is used in speech recognition models, biometric verification, video games, ML applications are endless. You just need a deliberate software development stage to select the right algorithms.
Types of Machine Learning Systems
Supervised – Features are directly map-able with the target variable. There is a relationship. We know what our output should look like. It is a kind of regression, continuous values. Examples: Stock price prediction, weather forecast, house price prediction, disease prediction, etc.
Based on the size of the house(X) – > we want to predict the price of the house (Y)
Based on various independent features (temperature conditions) -> we want to determine if we can play tennis or not.
This function lets us know whether the boy will play tennis or not.
Statistically, f(x1,x2,x3,x4) -> y or f(Outlook, Temperature, Humidity, windy) -> Play Tennis.
Here, all input features are independent of each other. But as a whole, we need all of them to determine the relationship with the target variable.
Unsupervised – Here we do not know what the output should look like, and there is no relationship between the input and output variable. We have to determine the patterns based on the data. For this, we need to implement certain algorithms.
Example: Selecting T-shirt Size is random, and we are not expecting a set possible outcome.
Re-inforced – It involves scenario-based learning that includes trajectory optimization, motion planning, dynamic pathing, and controller optimization. Likewise, if you know automatic parking policies, you’ll get the parking space, which includes lane changing, overtaking, speed limit, drivable zone, avoiding collision, maintaining a steady speed thereafter.
What is Machine Learning offering to the world?
Machine Learning (ML) is closely interrelated to mathematics and statistics. Other interdisciplinary fields include information theory, game theory, and optimization. It is a subfield of computer science that aims to program machines to single out tasks. It stems from Artificial Intelligence (AI) offering the ability to turn experience into expertise or to detect meaningful patterns in comprehensive sensory data as a foundation of human intelligence.
How does ML leverage modern mobile applications?
ML does not build automated imitation of intelligent behaviour like Traditional AI, but it performs tasks that fall way beyond human capabilities. For example, It allows scanning massive databases and detects patterns that are outside the scope of human perception.
Machine Learning in Android Applications
Android supports various ML tools and methods like:
- ML Kit – It is Google’s ready-to-use machine learning SDK
- TF Hub – It is used to pre-fetch cutting-edge models
- TF Lite Model maker – It is used to train an existing model with your data
- Android Studio – It is used to integrate these models into your app
Challenges with ML
Once the program has been written, the software has been installed, it remains unchanged. It secures a lot of scope for improvement and scaling up.
In Conclusion
Machine Learning Companies always have the option to build their servers for deploying and training a neural network. Saving on cloud storage also keeps infrastructure operational. Factor in some scalability, and ensure that the configurations of your server meet the desired performance output. Application development integrates many challenges, and these business decisions will trickle down to your user experience. Be sure, you’re considering the needs of your target audience.