Machine Learning Algorithms: A Detailed Primer
Machine Learning has taken over the world, and it has come out from the fancies of the science fiction world to business intelligence reality. It can be termed as a new age business tool that entails multiple elements of business operation.
The future of business intelligence is now dependent on machine learning. Machine learning is an important technology in the modern workplace.
It has crossover from science fiction to business intelligence reality, where it can be used for multiple purposes such as improving marketing strategies or analyzing customer trends with great accuracy.
74% of over 1,600 surveyed enterprise owners, decision-makers, and tech leaders consider ML a game-changer with the potential to transform their job and industry. It’s clear that this technology has arrived as an important part of our lives’ everyday work routines.
The market for artificial intelligence and machine learning is expected to grow by 33% over the next few years, reaching $202.57 billion in revenue by 2026 if current trends continue!
What would you say if I told you that your future could be determined by a machine? Well, it’s not too far-fetched because machines are learning to do more and better than human beings every day.
Moreover, the usage of machine learning (ML) applications has also become prominent among different industries such as Supply Chain, Healthcare, Information Technology, Banking, and more.
The Prominence Of Machine Learning In 2022
Both Artificial intelligence (AI) & Machine Learning (ML) are the most valuable and trending technologies for businesses today.
Data analysis has become a key part of understanding customer needs predicting behaviour patterns with accuracy rates far beyond human capabilities alone; all this can be done by leveraging AI or ML algorithms that help make informed decisions on where resources should go next.
The future of AI and machine learning is now in your hands. These algorithms are being embraced by Big Data analysts, who rely on them for their work to be more intelligent applications that can themselves analyze user behaviour patterns while offering desired content at needed times or places — all without human intervention.
In short, Machine learning apps are being used by businesses to make significant benefits and drive customer engagement through predictive analysis.
So, if you are an enterprise owner or a machine learning expert, it is thereby necessary for you to comprehend what various kinds of machine learning algorithms can bring to the table?
After a thorough conversation with our AI dedicated development team, we have formulated an exhaustive primer on the AI algorithms.
In this blog, I will try to bring out the essence of the types of machine learning (ML) algorithms and will try to describe the purpose and uses of distinct types of machine learning algorithms in detail:
Types of Machine Learning Algorithms
Before drilling into the expediences and execution of machine learning algorithms in brief, let’s try and understand what exactly Machine Learning is?
ML is a form of artificial intelligence that provides computing systems with the ability to automatically learn, improve and replicate from experiences without being explicitly programmed.
Some common applications for machine-learning include medical diagnosis (e.g., radiology), image processing such as object recognition or facial analysis, prediction where algorithms may forecast future events based on past data points.
The application of machine learning is being used in many different fields, and it has become a major industry. The algorithms that make up these functionalities can be found across various industries because they’re so versatile!
If you are seeking to execute machine learning capabilities in your enterprise application, you ought to hire machine learning developers who can leverage the Machine Learning algorithm to formulate business-oriented apps.
The Major machine learning algorithms
1. Supervised Machine Learning Algorithms
Supervised learning algorithms are those where data scientist or analyst has a clear idea about what kind of outcome is expected from the algorithm.
The algorithm is then trained using a labelled dataset, which contains a set of training data points along with the corresponding labels.
The aim here is to produce a model that can map input values (x) to the corresponding output values (y). After the model is trained, it can be utilized to predict the label of new data points.
Some common examples of supervised machine learning algorithms are:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines, etc.
Supervised Machine Algorithm includes two major processes
Regression: In this, the aim is to predict a continuous value output. For example — predicting the price of a house based on its size, age, location, etc.
Classification: In this process, the aim is to assign a class label to input data points. For example, classifying an email as spam or not spam.
Use Cases: Supervised Machine Learning Algorithms
The use cases of supervised machine learning algorithms are vast. Some of the most popular use cases are as follows:
Detecting fraudulent activity in bank transactions: This is a crucial task for any bank, as it can help them avoid huge financial losses.
Supervised machine learning algorithms can be operated to train a model on past transaction data (both fraudulent and non-fraudulent), which can then be used to predict whether a new transaction is likely to be fraud or not.
Predicting stock prices: By using historical stock data, a machine learning algorithm can be trained to predict future stock prices. This is extremely useful for investors who want to make informed decisions about where to invest their money.
2. Unsupervised Machine Learning Algorithm
An unsupervised Machine Learning Algorithm is one where the analyst or data scientist has no idea about what the expected outcome of the algorithm should be.
The algorithm is then trained using a dataset that doesn’t contain any labels. The aim here is to produce a model that can learn to group input values together based on some common underlying characteristic.
In contrast to supervised learning algorithms, unsupervised learning algorithms do not require labelled data. Instead, they try to learn the underlying structure of the data by looking for patterns in the data itself.
Source: Towards Data Science
This ML algorithm uses these types of techniques to describe data:
- Clustering: This is a method of data visualization that groups similar data points together.
- Anomaly detection: This is a method of identifying unusual data points that do not fit well with the rest of the data.
- Dimensionality Reduction: This is a method of reducing the number of features in a dataset while still retaining as much information as possible.
- Association Mining: This is the process of finding relationships between different items in a dataset.
Some common unsupervised machine learning algorithms are:
- K-means clustering
- Hierarchical clustering
- Apriori algorithm, etc.
Use Cases: Unsupervised Machine Learning Algorithms
The use cases for unsupervised machine learning algorithms are also vast. Some of the most popular use cases are as follows:
Finding patterns in customer data: By clustering customers based on their purchase histories, a retailer can identify groups of customers who have similar buying habits. This can be used to target these customers with specific marketing campaigns.
Predicting customer churn: Churn is the term used for customers who stop doing business with a company. By using unsupervised machine learning algorithms to cluster customers based on their past behaviour, companies can identify groups of customers who are at risk of churning.
This information can then be used to target these customers with specific retention campaigns.
3. Semi-Supervised Machine Learning Algorithm
As the name suggests, semi-supervised machine learning algorithms use a combination of labelled and unlabelled data.
The advantage of using semi-supervised learning algorithms is that they can exploit the extra information contained in the unlabelled data. This can help to improve the accuracy of the models that are learned.
Image Source: ResearchGate
Some common types of semi-supervised machine learning algorithms are:
- Co-training
- Tri-training
- Self-training
Use Cases: Semi-Supervised Machine Learning Algorithms
The use cases for semi-supervised machine learning algorithms are similar to those of supervised and unsupervised machine learning algorithms. Some of the most popular use cases of Semi-Supervised Machine Learning Algorithms are as follows:
Text classification: Semi-supervised machine learning algorithms can be used to classify text documents. This is because it is often easier to obtain a small amount of labelled data than it is to obtain a large amount of labelled data.
Image classification: Semi-supervised machine learning algorithms can also be used for image classification tasks. This is because it is often easier to obtain a small amount of labelled data than it is to obtain a large amount of labelled data.
If you want to create a machine learning application, then you should hire machine learning programmers.
4. Reinforcement Machine Learning Algorithms
Reinforcement learning algorithms are a type of machine learning algorithm that learns by trial and error. They are used to solving problems where the solution is not known in advance, and the algorithm has to figure out the best way to achieve the desired outcome.
One common application of reinforcement learning algorithms is game playing. In games such as Go, chess, and poker, the best move for a player is not always clear. A reinforcement learning algorithm can be employed to learn the best moves by playing against itself over many iterations.
Another common application of reinforcement learning algorithms is in robotics. Robotics tasks such as navigating through a maze or picking up an object can be difficult to program manually.
However, a reinforcement learning algorithm can be used to automatically learn how to perform these tasks.
Image Source: nateq news
Some common reinforcement learning algorithms are:
- Q-learning
- Monte-Carlo Tree Search (MCTS)
- Temporal Difference (TD)
- Asynchronous Actor-Critic Agents (A3C)
Use Cases: Reinforcement Learning Algorithms
The use cases for reinforcement learning algorithms are vast and varied. Some of the most popular applications are as follows:
Training robots: Reinforcement learning algorithms can be used to train robots how to perform tasks such as navigating through a maze or picking up an object.
Predicting stock prices: By using reinforcement learning algorithms, investors can learn how to trade stocks in order to maximize their profits.
Optimizing manufacturing processes: By using reinforcement learning algorithms, companies can learn how to optimize their manufacturing processes in order to maximize efficiency.
Ending Words
Machine learning algorithms are the key to unlocking data sets that would otherwise be useless. By merging different types of these powerful, machine-learning-based algorithms, you can find new insights in your unsearchable information and take action on it quicker than ever before!
So, whether you have a corporation that is focusing on projects like taxi apps or food delivery services or even any other app, your next and current business need would be profited from machine learning algorithms.
If you require professional expertise, then you can Hire machine learning consultants from the best Machine learning app development company to infuse machine learning algorithms that can create a business-oriented solution to increase your customer engagement and sales.
