Machine Learning:

Machine learning is a process of teaching a computer algorithm or program to perform a given task. Talking technically, machine learning is the study of creating applications that bring iterative improvements. There are three major categories of machine learning including; 1. Supervised Learning which includes classification and regression, 2. Unsupervised Learning, which in the context of machine learning, is based on clustering, and 3. Reinforcement learning. All of these three mechanisms are used by software development companies to make the customer experience better with the use of AI and machine learning Algorithm.

Supervised Learning:

The most popular image of machine learning is supervised learning. Supervised learning is easily understandable and can be implemented easily too. In supervised learning, a developer feeds data in the form of examples with mentioned labels. The algorithm is then allowed to predict the label for the given an example followed by the feedback about the right or wrong answer. Once the algorithm learns to find the right association between the examples and labels, it can observe an unknown example and predict the right label for it. 

This type of learning is task-oriented and focused on singular tasks. It works by parsing additional examples until the algorithm starts performing the tasks correctly. 

 

Examples of supervised learning 

  • Spam classification:

Spam classification in e-mail web applications is an example of supervised learning. Associated labels to the emails category as (spam/not spam) help the algorithm learn to classify spam emails and filter them on the basis of its classification. 

  • Face recognition:

The supervised learning algorithm is used by software development companies to enable their app with face recognition functions. Picasa and Facebook have been lately using the face-recognition function for one of its features. The algorithm is made capable of finding faces, classifying them as certain names and suggesting a tag. This is an example of a supervised algorithm. 

  • Advertisement popularity:

Ads are placed on the basis of relativity and data collection. Ads placement on specific browsers is an example of supervised learning. The ad is placed on the basis of increased popularity and relatability while it still asks the user whether is an appropriate placement or not. 

  • Unsupervised Learning:

Apparent through the title, unsupervised learning is quite the opposite of supervised learning. It does not feature labels, and instead, the algorithms are fed to analyze the data through tools that understand the properties of data. The system learns to group, cluster, or organize the data just like a

human does. This human-like attribute is performed by an intelligent algorithm. The majority of data is in the world is not labeled, so what works her is unsupervised learning. Intelligent algorithms that gather terabytes of unlabeled data and make sense of those data to benefit businesses with unparalleled profit. Unsupervised learning is a source of increased productivity in several industries. For instance, if we have a large database of researches done on several topics that are published. Through an unsupervised learning algorithm, it can be grouped in a way that it makes you aware of the current advancement in a certain area of research. Now, if someone wants to work on a certain research area, the particular algorithm would add suggestions about related work. With such a helpful tool, it would be much easier to increase productivity. 

Since unsupervised learning is based on the clustering of data, it may be called data-driven. The results of unsupervised learning are gathered by the data. 

Recommendation system:

While using YouTube, it offers a suggestion for other videos. Those suggestions are based on the previously watched videos. This is an example of unsupervised learning where on the basis of similarity, the user is prompted with a suggestion. Moreover, software development companies use this algorithm to make experiences more personalized and localized depending on different locations and preferences. 

Reinforcement Learning:

The mechanism of reinforcement learning is different from classification, regression, and clustering. Supervised and unsupervised learning has a connection in terms of labels, while reinforcement learning works on a different mechanism. Reinforcement learning is a type of learning that timely learns from the mistakes. The algorithm in this learning process makes a lot of mistakes in the beginning. However, when provided with a reinforcement of positive and negative signals for a good and bad decision respectively, it learns by reinforcement. In this way, we can reinforce our algorithm to go for good decision and avoid bad ones. As the algorithm learns things the right way, it makes fewer mistakes and improves over time. This type of learning is rather behavior-driven and has influence from behavioral psychology and neuroscience. 

Video Games:

Video games are one of the most common examples of a reinforcement learning algorithm. Web development companies use this algorithm to teach their system to perform a certain task. AlphaZero and AlphaGo are Google’s reinforcement learning application, which learned to play the game Go. 

Final Words:

There are different categories of machine learning; however, all of them are used by software development companies to make their system more efficient and user-centric. Moreover, many times the classification is not clear among these algorithms. There are a number of tasks that initially worked as a learning algorithm and later transforms into another. Consequently, what matters here is the efficiency of the algorithm regardless of its type. But, it’s important to understand the types of machine learning algorithms to understand the functions in depth. It turns out machine learning is helping software development companies to design and develop intelligent apps that transform the world

 
 
 
 

Published by Zubair Hassan