Machine Learning in IOS Application Development What is machine learning? Machine Learning is a concept that allows a machine to learn from examples and experiences. Machine learning is a type of artificial intelligence where computers “learn”...
Machine Learning in IOS Application Development
What is machine learning?
Machine Learning is a concept that allows a machine to learn from examples and experiences. Machine learning is a type of artificial intelligence where computers “learn” without being explicitly programmed instead of writing a bulk of algorithms and code. The process starts when we provide specific data to generic algorithms and machines that extract the logic from this given data. Today, more and more businesses are proactively investing in machine learning development services to maintain a cutting edge market position. Let’s learn about how your business can deploy machine learning and artificial intelligence services to build intuitive and intelligent iOS applications.
Understanding Machine Learning
When you go for online shopping? So while checking for the clothes shoes or anything, did you noticed when it recommends similar kinds of products like shoe clothes or what we search? did you notice all these things how it happens? How are they doing this recommendation? This is machine learning.
Types of Machine learning.
Supervised Learning is like having a guider teacher, trainer which guides you step by step that what’s right and wrong and it is a book, it is a flower, it is a car. it’s a way of teaching the computer. We have a dataset that acts as a teacher and its role is to train the model or the machine as per the dataset. Once the model gets trained it can easily predict or take decisions when new data is given to it.
Unsupervised learning- The model learns through observation and finds structures in the data. some times It follows the concept of hit and trial method. The agent is rewarded or Penalized with a point for a correct or a wrong answer, Once the model is given a dataset, it automatically finds and recognizes patterns and relationships in the dataset by creating clusters in the model. It can’t be Abel to add the labels to the cluster. like it cannot say this a group of lychee or guava, but it will separate all the lychee from Guava.
Suppose we have images of lychee guava and strawberry in our model so based on the pattern and relationship it creates and divides the dataset into those clusters
and on the basis of the positive result gained the model trains itself. And again once trained it gets ready to predict the given data presented to it.
How we can implement the Machine learning in ios
Before going to implementation we will discuss the Requirements
Check You have macOS Mojave installed
For using some of the CreateML methods to work you have to firstly installed MacOs 10.14 or 10.14 (Mojave).
If you are still running the earlier version of macOS such as Mavericks, Yosemite, Ei Capitan, Sierra, High Sierra, etc you have to update your MackOS
Let’s come to the point.
To implementing the Machine Learning in our application we have to use the Core ML.
Core ML- it is the framework used to integrate the Machine learning Models in the iOS application.
It provides the Unified representation of all models
Our app will use the Core ML API and the data given by the user to make the Predictions and to train the modules.
The Best thing in Core ML is that you don’t Require Any extensive knowledge about neural Network and machine learning you can easily implement the Core ML without any so much prior knowledge of machine learning.
Model -> it is the result of applying machine learning Algorithms to a set of trained data.
We use the model to make predictions based on input data.
We can also build and train a model with Create ML Bundle with Xcode.
Models trained by Create ML are in the formate of Core ML.
With the help of Core ML, we can also implement
Create ML: Train custom machine learning models As per your data.
The process of creating the machine learning models in Swift playground follows the steps
Import Create ML
Import Core ML library in the Swift Playground
Use foundation Framework if using URL
The next step is to specify the data to train the model.
It can be images, texts, CSV files or Tables or maybe in swift
Store all the training data inside one directory.
Test data in another directory so we can evaluate the model easily for accuracy.
let trainDirectory = URL(fileURLWithPath: “~/Desktop/Fruits”)
let testDirectory = URL(fileURLWithPath: “~/Desktop/TestFruits”)
Create a Model
It depends on what type of data we need to train
Different methods for a different type of Data
For Image we use
let model = try MLImageClassifier(trainingData: .labeledDirectories(at: trainingDirectoty))
There are other Methods also we use Respective Methods
MLTextClassifier for TEXT
MLRegressor for Tabular Data
We have created the model now we have to evaluate it as below
let evaluation = model.evaluation(on: .labeledDirectories(at: testDirectory)
It will evaluate the model against the test data and provide relevant results
We always improve accuracy by re-evaluating the model and feeding the correct data.
Once the level of accuracy has been achieved then we can save the model.
try model.write(to: URL(fileURLWithPath: “~/Desktop/FruitClassifier.mlmodel)
This will save the model on Desktop You can Save it anyWhere
Finally, you can use this Model in your Project.
Core ML Support Vision Framework for analyzing images, Natural Language framework for processing Text Speech framework for converting audio to text, and SoundAnalysis for identifying sounds in audio.
Currently, iOS launches CoreML 3
Which adds a lot of new stuff in iOS machine learning
The main key feature is on-Device Training of Models on the iPhone And Ipad.
The second thing it can also run many advanced model architectures.
In this article, we’ll discuss some foundational concepts in machine learning (ML) that are particularly important for mobile developers (iOS Developers) interested in working with ML.
Mobile data sources, Machine Learning overview, and basic use cases
Mobile devices provide four different input sources that can be used for Machine Learning. These sources are:
Before we dig deeper, let’s understand some fundamental concepts that we need to know to better understand Machine Learning. The first and the foremost building block of Machine Learning is the model.
A machine learning model is a combination of an algorithm that’s taught by a computer to perform a specific task, and the data that’s used by the algorithm to train itself.
An ML model is a mathematical model that generates predictions by finding patterns in your data. — AWS
We call this a model because it “models” the domain for a given problem. For example, while trying to identify faces of our friends in a given image, the problem domain is digital images of humans. The respective model corresponding to this problem domain will contain everything to make sense of these images.
The first thing needed to create a model is the algorithm. Then, we use this algorithm to train the model by showing it a large number of images related to the problem that we want to solve.
But creating a model can be quite tricky and resource-intensive, so the first step is often o acquire a pre-built model. Many such models can be easily found online and can be converted to a Core ML model format, using tools such as the TensorFlow converter and Core ML Tools.
In our example, the model would need images of our friends and all the things that we want the model to learn from those images, such as their names. Once we have both the data and algorithm we can begin training the model.
The process of training an ML model involves providing an ML algorithm with training data to learn from. The term ML model refers to the model artefact that is created by the training process. — AWS
The training data must contain the correct answer, which is known as a target variable or target attribute. The learning algorithm finds patterns in the training data that map the input data attributes to the target (the answer that you want to predict), and it outputs an ML model that captures these patterns.
Once training is complete, a model contains the knowledge about the problem that was extracted by the algorithm from the images, and hence we can use it to find out answers to unknown questions. This is called inference.
Using trained models to draw conclusions or make predictions for a given input is called “inference.”
After training, if the model is able to predict names of our friends for a given input image, then we can say that the model generalizes to the task we’ve provided it, as expected.Types of Machine Learning
Machine Learning comes in many different flavors, depending on the algorithm and its objectives. You can divide machine learning algorithms into three main groups based on their purpose:
The image below depicts difference between the three:
In this following sections, we will discuss more about supervised learning.Supervised learning
Supervised learning is the most common learning type in machine learning practice. Human intervention is required for supervised learning, as the algorithm needs training data that’s labeled. This process requires the model to be fed with labeled examples—in our case, labeled images of our friends.
Data labeling is the process of attaching meaning to different types of digital data like text, audio files, images, videos, etc. It’s a time consuming process, as it involves human interaction for reliable results.
These labels tell the model what or who is in a given image.
Supervised learning always needs labeled data.Types of Supervised learning
Supervised learning is categorized into the following two types:
Classification is a specific sub-area of supervised learning and can be used to classify text, images/video, and sound. This makes classification suitable for different tasks across different problem statements. And it’s what we’ll be working with in our example.
Classification techniques predict discrete responses or categories, such as whether an email contains spam or not. The output of a classification model is a SPAM or NOT SPAM, for instance. Or, in the case of our previous example, the name of one of our friends.
The classes predicted by the model are the ones that it recognizes. You might have seen on your social media handles when Facebook starts predicting names of our friends while uploading a picture, or businesses analyzing sentiments of a Tweet—these are all examples of classification.
How do we create a good model?
The answer to this question depends on the data we have and what we aspire to predict with the help of the model. We might sometimes come across an existing, pre-trained model (or, put another way, pre-built) that suffices for our needs and does what we expect it to do. In such a scenario, all we need to do is convert the model to Core ML and use it inside our iOS app.
Core ML comes with a bunch of ready-to-use models that detect thousands of features and understand a thousand different classes of objects. Training such a model from scratch requires very large datasets and a huge amount of computation—both of which can be expensive in terms of time and money.
For those reasons (and given limited expertise), training our own models from scratch to do the same thing might not turn out to be a wise choice. Instead, we can take a pre-trained model and customize it on our own data— this process is called transfer learning.
Using an existing pre-trained model to extract relevant features from custom training data is called transfer learning.
Transfer learning saves a lot of time, effort, and resources, as it’s much faster when compared to training an entire model from scratch. We don’t need a huge dataset to use a pre-trained model, and in many cases, we can get by with a few thousand images instead of millions of images.
Apple provides two tools that perform transfer learning. These are:
In upcoming articles, we’ll learn how to create a binary image classifier and also discuss how ML works behind the scenes in iOS.
Thanks for reading, please share it if you found it useful.
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Supervised VS Unsupervised - 22:26
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Logistic Regression - 55:45
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