Machine Learning in iOS

Machine learning is an integral element of today’s technologically-driven world.

It has become a source of making programming scalable in a way that helps developers in the perfect production of apps and users to make the most of the high-tech systems.

But first, let’s take a look at what machine learning is.

What is Machine Learning?

Machine learning is the use or implementation of artificial intelligence in which the machine or computer learns the behavior of past activities and predicts future actions accordingly. Without following specific instructions, machine learning makes use of an algorithm that helps in analyzing the data and form the regular patterns of the data.

Basically, it is the application of artificial intelligence-based systems that allows automatic learning and enhances the system without having to be individually programmed for the purpose. The advancement and development of computer programs with which the data is easy to access and can be learned are possible via machine learning.

The learning cycle of machine learning depends on how well the data has been observed and consumed. The data may include a list of instructions, regular data patterns, and experiences that enable in contemplating and making a decision itself. The objective of machine learning is to ensure that the machine learns and performs an action without any external human assistance or adjustments.

Machine learning is as diverse as primary learning is. There are methods of machine learning based on their algorithms, such as

Supervised machine learning algorithms
Unsupervised machine learning algorithms
Semi-supervised machine learning algorithms
Reinforcement machine learning algorithms

1. Supervised Machine Learning Algorithms

These are the algorithms that learn from past experiences and implement them in developing new data with the help of labeled instances for future events. This learning algorithm makes predictions for future outputs simpler. The targets are then associated with the input for the effective results. The output is also capable of comparison to find the accuracy of the results and modify accordingly.

2. Unsupervised Machine Learning Algorithms

The unsupervised machine learning algorithm is used for non-labeled or non-classified data. It learns the raw structures of unlabeled data to deduce the results. It cannot comprehend the exact output however, this algorithm studies the data set and draws conclusions based on the data sets of hidden raw structures of the unlabeled data.

3. Semi-Supervised Machine Learning Algorithms

A semi-supervised machine learning algorithm is a mix of supervised machine learning algorithms and unsupervised machine learning algorithms. This is the type of hybrid category of the two as it uses both labeled and unlabeled data for the training.

The systems based on this algorithm can invariably enhance the precision of unlabeled data. Although, obtaining unlabeled data does not need external resources, but semi-supervised learning is preferred in cases where labeled data demands the use of solid resources for their training and learning.

4. Reinforcement Machine Learning Algorithms

The reinforcement machine learning algorithm is special as it provides a learning method where a connection is maintained between error or rewards and actions. The highlighted feature of this method is the trial-and-error search and delayed rewards.

This algorithm enables all machines and software agents to automatically figure out the correct behavior of the context to capitalize the overall performance.

Applications of Machine Learning

There are many mainstream and trending applications of machine learning in today’s world. Let us discuss a few.

1. Speech Recognition

When you are browsing on the search engine, it also gives you a choice to search by speaking up that comes under the banner of “Search by Voice”. This feature falls into the category of speech recognition. It establishes a process of converting your speech to text.

The latest trends such as Cortana, Google Assistant, Alexa, and Siri are marvelous examples of speech recognition technology that follows voice instructions and performs the desired functions.

2. Self-Driving Cars

You may have come across the images and the idea of self-driving cars which is as thrilling as it sounds. Machine learning greatly contributes to self-driving cars. It is the unsupervised method of machine learning algorithm that has made self-driving cars an exciting reality. The methods train cars and distinguish every object and person on the drive. Tesla is one the finest examples of self-driving cars that the world is aware of.

3. Image Recognition

The identification of people, places, objects, digital images, and more is a form of an image recognition system. When your phone unlocks itself once it recognizes your face is one of the daily life examples of image recognition. Moreover, when you post a picture on social media with a friend and it automatically generates tagging suggestions with names is how the primary step of the image recognition algorithm works and that is commonly known as face detection.

4. Traffic Detection

Machine learning has made it easier for you to know if your required route to a destination will face traffic. It predicts the recent traffic situation of the place you want to go to and shows you the correct and shortest route on Google Maps.

It also tells you the duration to reach the destination, slow-moving or heavy traffic with real-time locations, and the average time that has been consumed in the past days.

In a Nutshell

Machine learning has made our lives easier in many different ways we were not aware of. So far, we had no idea how much of our lives’ technological use depends on machine learning. With this blog, however, we hope that you have gained insight into this domain.