Types of Machine learning
Machine learning (ML) is a subset of artificial intelligence (AI) that enables software programs to grow increasingly effective at predicting outcomes without explicitly programming them to do so. Technically, The study of computer programs that use algorithms and statistical models to learn through inference and patterns without being explicitly programmed is referred to as machine learning.
So, how does the Machine Learning algorithm work? A machine learning algorithm is taught by forming a model using a training data set. When new input data is fed into this algorithm, it tends to predict based on the model. The accuracy of this prediction is then determined. And as soon as this accuracy meets acceptable levels, the ML algorithm is ready for deployment.
Types of Machine Learning
In an era of excessive usage of artificial intelligence and machine learning, it is vital to distinguish between different forms of machine learning. Classical machine learning is usually categorized by how an algorithm learns to improve its prediction accuracy.
It may now be classified in various ways. The three basic acknowledged categories of machine learning are supervised learning, unsupervised learning, and reinforcement learning. In this article, we'll explore these categories in detail, one at a time.
Today, the most prevalent category of machine learning, arguably, is supervised learning. In most cases, novice machine learning practitioners will start with supervised learning algorithms. Supervised Machine Learning is a learning method that uses labeled training data to predict outcomes for unlabeled data.
Supervised machine learning algorithms are characterized by using labeled datasets to train algorithms that properly segregate data or predict outcomes. As input data is fed into the model, the weights are adjusted until the model is well fitted, which occurs as part of the cross-validation process.
There are two key domains where supervised machine learning is often useful: classification and regression. Thus Supervised Learning is split into two different categories.
Classification makes use of an algorithm to properly allocate test results to specific groups. It detects certain entities within the dataset and attempts to infer how those items should be labeled or described. In classification tasks, our output often comprises groups or categories. Examples of such groups produced by classification include demographic data such as marital status, gender, or age. Classification of an email as spam or not is also a common use case.
Some of the common algorithms used under this category of problems include Logistic Regression, Support Vector Machines[SVM], K-Nearest Neighbours[KNN], Naïve Bayes, etc.
Regression is a technique for determining the connection between dependent and independent variables. It is a statistical method that aims to identify the critical link between dependent and independent variables. A regression algorithm aims to predict a continuous number such as sales, income, or test results. Some of the popular regression algorithms include Linear regression, Ridge regression, Decision Tree regression, etc.
Below are some of the common use-cases you'll find of supervised learning in today's world.
- Spam detection
Companies can train databases to spot patterns or abnormalities in fresh data using supervised classification algorithms, allowing them to efficiently categorize spam and non-spam email exchanges.
- Image/Object recognition
Supervised learning methods may be used to find, isolate, and categorize objects in movies or pictures, making them valuable in computer vision techniques and visual analysis.
- Predictive analytics
The use of Predictive analytics enables organizations to forecast certain outcomes depending on a particular output variable, assisting business executives in justifying actions or pivoting for the benefit of the firm.
Unsupervised learning is diametrically opposed to supervised learning. There are no labels on the data. Instead, our system would be fed a large amount of data and given the means to grasp the data's features. It analyses and clusters unlabeled datasets using machine learning methods. These algorithms uncover hidden patterns or data groupings.
The fact that the vast majority of data in the world is unlabeled makes unsupervised learning such an exciting field. Intelligent algorithms that can make sense of our large datasets of unlabeled data are a big source of potential profit for many enterprises. This alone has the potential to increase production in a variety of industries.
Unsupervised learning models are used primarily for three kinds of problems: clustering, association, and dimensionality reduction.
Clustering is a data mining technique that organizes unlabeled data into groups based on similarities and differences. Clustering techniques are used to arrange raw, unclassified data items into groups characterized by information structures or patterns.
This is a rule-based approach for determining associations between variables in a given dataset. These methodologies are commonly used in market basket analysis, helping businesses better understand the linkages between various items. Recommendation engines on multiple sites are the best-known example of this.
- Dimensionality reduction
While more data typically gives more accurate findings, it can also influence the performance of machine learning algorithms (overfitting) and make the visualization of datasets challenging. Dimensionality reduction is a strategy that is employed when the amount of characteristics, or dimensions, in a given dataset is excessive. It minimizes the amount of data inputs to a reasonable quantity while keeping the dataset's integrity as much as feasible.
The following are some of the most popular real-world uses of unsupervised learning:
- Anomaly detection
Unsupervised learning methods can sift through enormous volumes of data to find anomalous data points. These abnormalities might raise awareness of malfunctioning equipment, human mistakes, or security breaches.
- Recommendation engines
Unsupervised learning can aid in the discovery of data trends that can be utilized to generate more successful cross-selling tactics by using historical purchase behavior data.
- Medical Imaging
Unsupervised machine learning gives critical aspects to medical imaging technologies, such as image identification, classification, and segmentation, which are utilized in radiology and pathology to swiftly and effectively diagnose patients.
When compared to supervised and unsupervised learning, reinforcement learning is quite distinct. Whereas the link between supervised and unsupervised learning (the presence or absence of labels) is clear, the relationship to reinforcement learning is a little hazier. RL algorithms are about learning the best response in a given situation in order to maximize reward. This ideal behavior is discovered via interactions with the environment and observation of how it reacts.
To put it simply, It is an algorithm that executes a job by attempting to maximize the rewards it receives for its efforts.
A possible application of RL is any real-world situation in which an agent must interact with an unpredictable environment in order to achieve a certain objective.
- Autonomous driving
In an unpredictable environment, an autonomous driving system must execute many perceptual and planning tasks. Vehicle route planning and motion prediction are two particular applications where RL might be helpful.
- Video games
Learning to play video games is one of the most popular applications of reinforcement learning. Consider Google's reinforcement learning applications, AlphaZero and AlphaGo, which learned to play Go.
- Managing resources
Reinforcement learning is effective in navigating complicated surroundings. It may deal with the necessity to balance various requirements. Take Google's data centers, for example. They employed reinforcement learning to balance the need to meet our power demand while being as efficient as possible, resulting in significant cost savings.
Here’s a table that sums up the types of machine learning we’ve covered in this post.
|Supervised ML||Unsupervised ML||Reinforcement ML|
|Learns by using labeled data||Trained using unlabelled data without any guidance.||Works on interacting with the environment|
Type of data
|Labeled data||Unlabelled data||No – predefined data|
Type of problems
|Regression and classification||Association and Clustering||Exploitation or Exploration|
|Calculate outcomes||Discover underlying patterns||Learn a series of action|
|Risk Evaluation, Forecast Sales||Recommendation System, Anomaly Detection||Autonomous Cars, Gaming, Healthcare|
Frequently Asked Questions
- Which algorithm is the best for you?
There is no singular definition of the “right” algorithm, it solely depends on the situation and the data available. Before making a choice, make sure to define your use case. Following this would help you make a better choice.
- What is semi-supervised learning? When do you use it?
Semi-supervised learning is a learning scenario in which there are a few labeled samples and a large number of unlabeled samples. It provides the benefits of both, supervised and unsupervised learning and skips the hassle of labeling the data. Unsupervised and semi-supervised learning may be more tempting options since relying on domain expertise to categorize data accurately for supervised learning may be time-consuming and costly.
- Can a problem be solved by either Supervised or Unsupervised learning?
Yes! Consider the example of the recommendation engine we mentioned as an application under unsupervised learning. If, instead, we were given the users' watch history, we could work on the data and use supervised learning to serve our purpose. In the end, it's essential to understand the best way to blend the algorithms to suit our needs.
Now that we've covered the different types of machine learning, it's crucial to notice that the distinctions between these types of learning frequently overlap. Furthermore, there are several activities that may be simply framed as one type of learning and then turned into a different concept. If you're interested in learning more about ML, check out this course.