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PyNet Labs- Network Automation Specialists

Different Types of Machine Learning

Author : PyNet Labs
Last Modified: December 30, 2024 
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Introduction

Machine learning is a way for computers to learn and improve by using the information they receive. ML algorithms help solve various business problems, such as Regression, Classification, Forecasting, Clustering, and Associations. It is primarily categorized into four types, i.e., supervised learning, unsupervised learning, and Reinforcement learning. All these machine learning types use different approaches.

When we talk about supervised learning, predictions are based on the data available with labels, whereas unsupervised machine learning involves analyzing data that have no labeling information; finally, reinforcement learning is limited to decision-making through trial and error. In this blog, we will discuss these categories, their approaches, and use cases for a better understanding of their contribution to industries and technologies. You should enroll in an AI and ML Course to get understanding of both these technologies.

Let us discuss the different types of Machine Learning along with their respective algorithms.

What are the Different Types of Machine Learning?

Based on the methods and way of learning, there are four types of machine learning, which are:

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Semi-Supervised Machine Learning
  • Reinforcement Learning
Machine Learning Types divided into four categories and their application

Let’s discuss each one by one.

1. Supervised Machine Learning

Supervised machine learning is one of the types of machine learning where the algorithms are trained by labelled data, and every training example contains the input data together with the right output. Through this process, the model becomes capable of mapping inputs to the correct output.

Raw Data entering Supervised Learning Model with Algorithms and prediction/output coming out of other end

Popular problems are classification, in which the goal is to label a set, and regression, where the task is to predict real numbers. It is popular across different industries for applications like credit card fraud, image identification, and sales prediction.

Categories of Supervised Machine Learning

There are mainly two types of Supervised Machine learning. These are:

  • Classification: Classification is a type of supervised machine learning where we teach a computer to sort data into specific groups or categories. To do this, we use a special type of data called ‘labelled data,’ where each piece of data is already tagged with the correct category it belongs to. The computer uses this labelled data to learn and make predictions about new, unseen data. The most often used algorithms for classification are decision trees, logistic regression, and support vector machines as well as neural networks. Examples of the use of classification include the ability of a system to filter out email spam.
  • Regression: Regression is another broad subheading of supervised machine learning algorithms that target predictive analytics. While, as in the classification method, the model does not involve nominal outcome variables, regression models are trained with labelled data to identify a dependency between input parameters and a non-discrete target variable. Popular algorithms are linear regression technique, polynomial regression technique, and support vector regression technique. Regression is applied quite often in many fields, for instance, it is used for stock price prediction, forecasting of sales and, most importantly, estimation of real estate prices, as the results are expected to be numerically accurate.

Common Algorithms

Some Common Algorithms of Supervised Machine Learning are:

  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Support Vector Machines (SVM)
  • Neural Networks

Applications of Supervised Learning

Some of the applications of supervised learning are:

  • Email Spam Detection: In supervised learning methods, emails are classified as spam or not spam to enhance the email filtering and users’ experience.
  • Image Recognition: They are used for pattern recognition, object identification, faces or scenes in images, with applications in biometrics/face recognition, self-driving cars, medical image analysis, etc.
  • Credit Scoring: Financial institutions use supervised learning to assess the creditworthiness of applicants by predicting default risk based on historical data and attributes.
  • Customer Churn Prediction: Supervised learning is adopted by business organizations to identify customers likely to leave their services so that actions to retain them can be taken.
  • Disease Diagnosis: Supervised learning helps diagnose diseases based on a patient’s symptoms and past medical records, detect early disease, and find the most appropriate treatment procedure.

2. Unsupervised Machine Learning

Unsupervised learning is one of the most used machine learning types. It is a process of constructing a model to recognize patterns from the data set without any input or output variable in mind. It lacks the labeled training instances as does the supervised learning, but this way, the model is free to discover the underlying structure of the data on its own.

Raw Data entering Unsupervised Learning Model with Algorithms & Unlabeled Data and prediction/output coming out of other end

Some usual methods are clustering, where similar data points are placed in one cluster and where data is made simple for analysis. Unsupervised learning is used very often in customer segmentation, in the analysis of market baskets, and in fraud detection.

Categories of Unsupervised Machine Learning

Unsupervised Machine learning is categorized into two types:

  • Clustering: Clustering is a type of unsupervised machine learning where data is partitioned into categories according to similarities within data points. In this case, the major objective is to find a set of structures that are embedded in the data as they are without any predetermined categorizations. Some of the most commonly used clustering algorithms are the K-means algorithm, hierarchical clustering algorithm, and DBSCAN. Thus, clustering is an industry-standard technology implemented in different fields like consumer segmentation, storing data in a structured manner, and image and music data in which complex information is divided into simple chunks for easy understanding.
  • Association Rules: Association rules is another category of machine learning that has unsupervised learning, which primarily aims at finding interactions in variables between large data sets. This approach seeks to establish relationships or a match, basically in the form of a rule such as “If A then B”. This is done using techniques like Apriori and FP-Growth. The goal is to understand how things are related, and it’s often used in retail to figure out which products are usually bought together. This helps stores arrange their products in a way that makes sense to customers and makes them happy.

Applications of Unsupervised Learning

Some of the applications of unsupervised learning are:

  • Customer Segmentation: Companies apply clustering algorithms to sort out the buyers including their buying habits, age, and preferences. With this, one can focus on targeted marketing strategies.
  • Anomaly Detection: It also focuses on finding unusual patterns in the data, which is important in fraud detection, secure networking, and monitoring the industrial system for any sign of failure.
  • Market Basket Analysis: Each type of association rule contains different principles because retailers use association rules to find out the frequent purchasing of products and assist in inventory control and sales promotions.
  • Recommendation Systems: Unsupervised learning through clustering and association enables the creation of individual user solutions for the platform and, ultimately, increases user interaction on platforms such as streaming or e-commerce platforms.

3. Semi-Supervised Learning

Semi-supervised learning can be defined as a type of machine learning that shares features with both supervised and unsupervised learning. In this method, the training is performed on a small amount of labelled data collected through the proposed label propagation method and a large number of unlabeled data. This is especially beneficial when labelling data is either expensive or requires a lot of time.

Input Data and Partial Labels are fed to Machine Learning Data having unlabeled Data and Prediction coming out

The components of the model improve the learning from the labelled examples and, in the same process, enhance its ability to generalize based on the information provided in the unlabeled data samples. Other areas that semi-supervised learning can be used include image classification, text categorization, and speech recognition, where it is difficult to get labelled data.

Categories Of Semi-Supervised Machine Learning

Semi-supervised learning can be categorized into three types. These are:

  • Self-Training: In self-training, the model first learns from a small amount of labelled data and then uses that learned information to predict the data with no label. It also presents an improvement over the previous models in the sense that the most confident predictions are added as ‘pseudo-labels’ to the training set, and the model is retrained iteratively. It continues until the model becomes a union or until the performance is enhanced.
  • Co-Training: Co-training means training different models on different features of the same data. The workflow of each of the models involves the use of labelled data to make predictions on the unlabeled data for the other model. This strategy improves the learning by making use of discriminant information, which is available from the feature sets used.
  • Graph-Based Methods: In graph-based semi-supervised learning, data instances are considered as nodes of a Graph where the nodes are connected by edges representing acquaintances. The labelled data points decide the unlabeled points based on this graph structure, and the model can spread label information through this graph structure, which makes good use of both the labelled points and the unlabeled points.

Applications of Semi-Supervised Machine Learning

Some of the applications of semi-supervised learning are:

  • Image Classification: Semi-supervised learning is often used in computer vision tasks, where a small set of labelled images can be combined with a large pool of unlabeled images to improve accuracy in classifying objects and scenes.
  • Text Classification: This approach is beneficial in natural language processing, particularly for tasks like sentiment analysis and topic categorization, where labelled datasets are scarce and unlabeled text data is abundant.
  • Image Classification: The application domain of semi-supervised learning includes computer vision where a small number of images can be labeled while a large number of similar images are unlabeled in order to obtain high accuracy during object and scene classification.
  • Text Classification: This approach is useful in natural language processing, especially where labelled data is sparse, or not available at all and there is a lot of text data.
  • Speech Recognition: In speech processing, semi-supervised learning has been demonstrated to improve the models by using a small amount of transcribed audio together with a much larger amount of untranscribed audio to teach the model about the spoken language.

4. Reinforcement Learning

Reinforcement Learning is one of the types of machine learning. It is a way for AI to learn by trial and error. It interacts with its environment, tries different actions, and gets rewards or penalties. Over time, the AI figures out what works best and adjusts its behavior to achieve its goals.

Image showing the working of reinforcement learning

This type of learning is useful in situations like playing games, controlling robots, or running automated systems, where the AI needs to make smart decisions and adapt to its surroundings.

Categories of Reinforcement Learning

Reinforcement learning can be categorized into:

  • Positive Reinforcement Learning: It is a way to teach AI by rewarding good behavior. When the AI does something correctly, it gets a reward, which strengthens the connection between the action and the desired outcome. This makes the AI more likely to repeat the same behavior in the future because it learns that it leads to a positive result. Some of the most used examples include offering a treat to the pet after obedience and offering the player a bonus, for example, after scoring a goal in a game to ensure that they continue doing the right thing or, in this case, scoring positive results in the game.
  • Negative Reinforcement Learning: Negative Reinforcement Learning is when an AI learns to do something because it stops a bad or uncomfortable thing from happening. For example, a student might read to avoid getting a bad grade, or a gamer might play to avoid losing. This type of learning doesn’t punish the AI but instead encourages it to repeat the behavior by removing the unpleasantness. It’s like the AI learns to do something because it makes a bad feeling go away.

Applications of Reinforcement Learning

Some of the applications of reinforcement learning are:

  • Autonomous Vehicles: Self-driving cars utilize reinforcement learning so as to move around and respond to their surroundings. It enables the vehicle to figure out the best strategy through simulation of other driving situations and in response to feedback in the form of signals from sensors and the surroundings.
  • Robotics: Reinforcement learning opens the way to the acquisition of complex tasks by robots, for example, through interactive learning as a process based on practice. This approach is used in robotic grasping, manipulation, and navigation to enable the robot to increase its performance in real-time.
  • Game Playing: Reinforcement learning has been highly successful in game playing and is especially evident in games such as AlphaGo, where AI programs carry out many games to come up with a strategy that would better that of human beings.
  • Healthcare: Reinforcement learning frameworks work to enhance the plan of treatment with respect to the patient responses over time so that it may enhance patient health outcomes in the case of chronic disease management and clinical decisions.
  • Finance and Trading: RL is used in finance for algorithmic trading, portfolio optimization, and for evaluating risks. These models learn to buy or sell in accordance with market conditions to maximize profit and, at the same time, minimize the risks.

These are the four types of Machine Learning available to use currently.

Frequently Asked Questions

Q1. What are the four types of Machine Learning?

There are four machine learning types, including:

  • Supervised ML
  • Unsupervised ML
  • Semi-Supervised ML
  • Reinforcement ML

Q2. What is NLP?

NLP is a machine-learning technology that gives computers the ability to interpret, manipulate, and understand languages.

Q3. What is a CV in AI?

CV stands for Computer Vision (CV) in AI. It allows machines to accurately interpret visual data from images, videos, and sensors.

Q4. What is Deep Learning in AI?

Deep learning is a type of machine learning that uses artificial neural networks to learn from data.

Q5. Are Machine Learning and Artificial Intelligence different?

Yes, Machine Learning is different from Artificial Intelligence as AI is a model that replicates the human tasks smartly, whereas machine learning is a subset of AI that focuses on making the decision making of AI better.

Conclusion

In conclusion, understanding the different types of machine learning assists in recommending the correct approaches to certain matters. Whether one should use supervised, unsupervised, semi-supervised, or reinforcement learning mostly depends on the type of dataset and on the necessary set of requirements to solve the task. That is why every machine learning types mentioned in this blog has its uses and advantages and that is why machine learning is a powerful technology in the modern data world.

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