Machine Learning (ML) is a rapidly evolving technology that automatically allows computers to learn from previous data. It uses various algorithms to create mathematical models to make predictions based on already existing data or knowledge. The most common use cases for ML are image and video analysis, speech recognition, email filtering, recommending and forecasting systems, and many more. This article covers different types of Machine Learning technics and how you can use them to solve your own technical and business problems.
Table of contents
- Supervised Learning
- Unsupervised Machine Learning
- Semi-supervised Machine Learning
- Reinforcement Machine Learning
- Related articles
As we already discussed, Machine Learning is a type of data analysis that automates the creation of analytical models. It’s a field of artificial intelligence based on the premise that computers can learn from data, recognize patterns, and make judgments with little or no human input.
Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in an autonomous fashion, by feeding them data and information in the form of observations and real-world interactionsEmerj, the AI Research and Advisory company
There are four different types of Machine Learning, including supervised, unsupervised, semi-supervised, and reinforcement learning. Semi-supervised learning is not so much popular. That’s why it is not defined as a separate category in most books.
The following diagram shows different Machine Learning algorithms splitter by these categories:
Supervised learning is a set of algorithms that train Machine Learning models using labeled examples, such as an input where the desired output is known. To use these algorithms, you need to have a historical dataset that contains input data and its classification. Supervised learning is commonly used in applications that predict likely future events based on historical data.
For example, you can create a Machine Learning model, which will classify:
|Email content||– Spam|
– Not spam
|Product review||– Positive|
Let’s take a look at how a historical dataset can train the Machine Learning model to classify spam emails. For every email from the historical email dataset:
- The model receives a set of inputs along with correct outputs
- The model uses its algorithm to calculate predicted output
- Predicted output compared with correct output to find errors
- Adjust the model in case of error
To train the Machine Learning model, the historical data set is usually split into three categories:
- Training data – used to train ML model parameters
- Validation data – used to determine which model hyperparametes of the model to adjust
- Test data – used to measure ML model “real” performance
Supervised learning can be divided into two sub-categories based on the output type.
- Classification: predicsts which category the data belogs to, eg: spam detection, sentiment analysis. Linear classifiers, Support Vector Machines (SVM), decision trees, k-nearest neighbor, and random forest are examples of common classification techniques.
- Regression: predicts a numerical value from the previously observed data, eg: house or stock price prediction. Popular regression algorithms include linear regression, logistical regression, and polynomial regression.
How supervised Machine Learning works?
A training set is used in supervised learning to teach models to produce the desired output. This training dataset contains inputs and actual outputs, allowing the model to improve over time. Different types of loss functions are used to assess the algorithm’s efficiency, and it is adjusted until the error is suitably minimized. The following diagram shows the learning process of Supervised Machine learning.
As shown in the diagram above, we provide training data set that includes inputs and outputs. Our model trains itself based on the provided data and the type of algorithm that we use in the training part. Once the model training is done, our model is ready to predict the output data based on its training.
Applications of Supervised Machine Learning
As we have already discussed, the purpose of supervised learning is to learn the mapping function (f), which refers to the ability to grasp how input (X) and output (Y) should be matched using existing data. We need to have labeled data to train our machine and predict the new input based on the training data set. The following are some of the applications of Supervised Machine Learning.
- Weather Prediction: Predicting weather conditions in a certain region is a particularly interesting topic that necessitates the consideration of many different parameters. To make accurate weather predictions, we must consider a variety of factors, such as historical temperature data, precipitation, wind, humidity, and so on. This may require the creation of complicated supervised models with many tasks. Predicting today’s temperature is a regression problem with continuous variables as output labels. Predicting whether or not it will snow tomorrow, on the other hand, is a binary classification problem.
- Image Classification: In the field of computer vision, image classification is a common problem. The idea here is to guess which class an image belongs to. We’re looking for the class label of an image using supervised machine learning.
- Text Classification: Text categorization problems are another fantastic example of supervised learning. The purpose of these set of questions is to guess the class label of a given piece of text. Predicting the sentiment of a piece of text, such as a tweet or a product review, is a common examples in text categorization. This is commonly used in the e-commerce industry to assist organizations in determining negative customer remarks.
- House Prices prediction: Predicting housing values is another example of supervised learning.
- Predictive analytics: The creation of predictive analytics systems to provide deep insights into diverse business data points is a common use case for supervised learning models. This enables businesses to predict specific outcomes depending on a given output variable, assisting business executives in justifying actions or pivoting for the organization’s advantage.
- Spam detection: Another example of a supervised learning model is spam detection. Organizations can train databases to spot patterns or abnormalities in fresh data using supervised classification algorithms, allowing them to effectively categorize spam and non-spam correspondences.
Unsupervised Machine Learning
Unsupervised learning is a machine learning technique in which models are not supervised using a training dataset. On the other hand, models use the data to uncover hidden patterns and insights. It is comparable to the learning in the human brain while learning new things. It can be defined as a type of Machine Learning in which models are trained using an unlabeled dataset and are allowed to act on that data without any supervision.
Unsupervised machine learning attempts to identify previously unseen patterns in data, but these patterns are frequently poor approximations of what supervised machine learning can produce. Unsupervised machine learning is the ideal option when we don’t have any data on intended outcomes, such as selecting a target market for a completely new product that our company has never sold before.
Unsupervised Machine Learning can be divided into two sub-categories.
- Clustering – is a way of organizing things into clusters so that those with the most similarities stay in one group while those with less or no similarities stay in another. Cluster analysis identifies commonalities among data objects and classifies them according to the presence or absence of such commonalities.
- Associatin Rules – is a type of unsupervised learning strategy for discovering associations between variables in a large database. It identifies the group of items that appear in the dataset together. The association rule improves the effectiveness of marketing strategies. For example people who buy X (let’s say a loaf of bread) are more likely to buy Y (butter/jam).
How Unsupervised Machine Learning works?
Unsupervised learning aims to uncover a dataset’s underlying structure, categorize data based on similarities, and compactly display the dataset. Because we have the input data but no corresponding output data, we cannot immediately apply unsupervised learning to a regression or classification task. For example, see the diagram below, which explains the working of Unsupervised Machine Learning.
As shown in the above diagram, we provide unlabeled data as input. Then, the specified algorithm processes the data. Unsupervised learning divides the unlabeled data into different groups based on similarities and differences.
Applications of Unsupervised Machine Learning
As we have already discussed, the method of inferring underlying hidden patterns from previous data is known as unsupervised machine learning. A machine learning model, in this case, strives to detect any similarities, differences, patterns, or structures in data on its own. The following are some of the applications of Unsupervised Machine Learning.
- Anomaly detection: Clustering can be used to find any type of outlier in data. For example, companies in the transportation and logistics industry might utilize anomaly detection to spot logistical bottlenecks or uncover faulty mechanical components.
- Customer and market segmentation: Unsupervised algorithms can be used to group people with similar characteristics and generate customer profiles for more effective marketing and targeting.
- Clinical cancer studies: Clustering methods are used to study cancer gene expression data (tissues) and predict cancer at early stages.
- Recommender systems: The association rules method is widely used to analyze buyer baskets and detect cross-category purchase correlations. Amazon’s “Frequently bought together” recommendations are a wonderful example. The organization wants to improve up-selling and cross-selling methods, as well as make product recommendations based on the frequency of specific goods discovered in a single shopping basket.
- Target marketing: Regardless of the industry, association rules can be utilized to extract rules to aid in the development of more effective target marketing strategies. A travel firm, for example, might use customer demographic data as well as historical data from prior campaigns to determine which client categories to target for a new marketing campaign.
Semi-supervised Machine Learning
Semi-Supervised learning is a type of Machine Learning algorithm that represents the intermediate ground between Supervised and Unsupervised learning algorithms. It uses the combination of labeled and unlabeled datasets during the training period. Although semi-supervised learning acts on data with a few labels and is the middle ground between supervised and unsupervised learning, It largely consists of unlabeled data.
The notion of semi-supervised learning is introduced to solve the shortcomings of supervised and unsupervised learning methods. The primary downside of supervised learning is that it necessitates manual labeling by machine learning experts or data scientists and a high processing cost. Furthermore, the range of applications for unsupervised learning is limited.
How Semi-supervised Machine Learning works?
As we have already discussed, Semi-supervised Learning is a sort of machine learning that trains models using a combination of labeled data and huge amounts of unlabeled data. This method of machine learning combines supervised and unsupervised learning. Supervised learning employs labeled training data, whereas unsupervised learning uses unlabeled data. The diagram below shows the training process of Semi-supervised learning.
By adopting pseudo labeling, semi-supervised learning can train the model with less labeled training data than supervised learning. This can combine many neural network models and training methods. A Semi-Supervised Machine Learning assumes the following about the data:
- Continuity Assumption: The algorithm predicts that points that are closer together have a higher probability of having the same output label.
- Cluster Assumption: The data can be separated into discrete clusters, with points in the same cluster having a higher chance of having the same output label.
- Manifold Assumption: The data are roughly distributed over a manifold with a substantially smaller size than the input space. This assumption allows distances and densities defined on a manifold to be used.
Applications of Semi-supervised Machine Learning
In the industry, semi-supervised learning models are becoming more popular. The following are some of the most common applications.
- Speech Analysis: It is the most well-known use of semi-supervised learning. Because classifying audio data is the most difficult operation that necessitates a large number of human resources, this problem can be naturally solved by using SSL in a semi-supervised learning model.
- Web content classification: However, labeling each page on the internet is absolutely critical and impossible because it requires significant human interaction. Even yet, using Semi-Supervised Learning Algorithms, this problem can be reduce. For example, Google ranks a webpage for a particular query uses semi-supervised learning algorithms.
- Protein sequence classification: DNA strands are larger, they require active human intervention. So, the rise of the Semi-supervised model has been proximate in this field.
- Text document classifier: As we all know, finding a large amount of labeled text data is nearly impossible, thus semi-supervised learning is a great way to get around this.
Reinforcement Machine Learning
The last type of Machine learning is Reinforcement learning. It is a feedback-based Machine Learning technique in which an agent learns how to behave in a given environment by executing actions and seeing the outcomes of those actions. The agent receives positive feedback for each excellent action and negative feedback or a penalty for each bad action. Here the agent does not need any labeled data; rather, it is learned by its own experience. The agent interacts with and explores the environment on its own. An agent’s primary goal is to increase performance by obtaining the most positive rewards in reinforcement learning. There are mainly two types of reinforcement learning, which are:
- Positive Reinforcement: Positive reinforcement learning entails doing something to increase the likelihood of the desired behavior occurring again. It has a positive effect on the agent’s behavior and raises the strength of the conduct. This form of reinforcement can last a long period, but too much positive reinforcement might result in an overload of states, which can lessen the consequences.
- Negative Reinforcement: Negative reinforcement learning is the total opposite of positive reinforcement learning. It enhances the likelihood of the given behavior recurring by avoiding the negative situation. Depending on the situation and behavior, it may be more successful than positive reinforcement, but it provides reinforcement only to meet minimum behavior.
How Reinforcement Machine Learning works?
Reinforcement Learning is built on the idea that the maximizing of expected cumulative reward may be used to represent any goal. An agent explores an unknown environment to achieve a goal. To maximize reward, the agent must learn to sense and perturb the state of the environment through its activities. The main elements of RL are the agent, the environment, the policy that the agent follows, and the reward single. The following diagram shows the working principle of Reinforcement learning.
An RL algorithm’s goal is to find the action strategy that maximizes the average value it can extract from each system state. The value function is a useful abstraction of the reward signal since it accurately represents the ‘goodness’ of a condition. The value function captures the cumulative reward predicted to be received from that stage onwards, while the reward signal indicates the immediate benefit of being in that state.
Applications of Reinforcement Machine Learning
Let us now look at the real-life applications of Reinforcement Learning which have confidently changed the dynamics of sectors like Healthcare, Marketing, Robotics, and many more. The following are some of the applications of Reinforcement Machine learning.
- Manufacturing: Manufacturing is all about creating items that meet our most fundamental requirements and desires. Manufacturers of Collaborative Robots or Manufacturers of Collaborative Robots capable of doing diverse production operations with a workforce of more than 100 workers, are assisting many businesses with their own RL solutions for packaging and quality testing.
- Image Processing: Image Processing is another important method of enhancing the current version of an image to extract some useful information from it. Deep Neural Networks (whose framework is Reinforcement Learning) can be used to simplify this popular image processing method. You can use Deep Neural Networks to either improve the quality of an image or hide its information.
- Gaming: We may expect greater performance from our favorite adventure, action, or mystery games because of game optimization using Reinforcement Learning algorithms.
- Robotics: Deep Reinforcement Learning is used in rebotics to learn from their environment.
Machine learning is a type of data analysis that automates the creation of analytical models. In this article, we discussed four different types of machine learning, including Supervised Learning, Unsupervised Learning, Semi-supervised Learning, and Reinforcement Learning. We also covered the working principles of these algorithms along with their daily life applications.
This is Bashir Alam, majoring in Computer Science and having extensive knowledge of Python, Machine learning, and Data Science. I have been working with different organizations and companies along with my studies. I have solid knowledge and experience of working offline and online, in fact, I am more comfortable in working online.
I love to learn new technologies and skills and I believe I am smart enough to learn new technologies in a short period of time.