Types Of Machine Learning
In a world filled to the brim with theories about artificial intelligence, machine learning, it’s fascinating to learn to identify the many forms of machine learning we could encounter.
For the end-user, this means knowing how many forms of machine learning exist and how to apply them. For developers, it means understanding these types and distinguishing between them to identify the best solution.
Broadly speaking, we can divide machine learning into three types. But before getting into the types of algorithms, we must first look at the data.
There are two types of data in Machine Learning: labeled data and unlabeled data.
Labeled data includes both the input and output parameters in a totally machine-readable manner, however labeling the data needs a significant amount of human effort.
Unlabeled data contains just one or even no parameters in machine-readable form. This eliminates the need for human labor, but also necessitates more complicated solutions.
1. Supervised Machine Learning
This is one of the most elementary forms of machine learning. Here, labeled data is used to train the algorithm. Despite the fact that the data must be appropriately labeled for this approach to operate, supervised learning is incredibly effective when you apply them to the right conditions.
In supervised learning, the system works with a limited training dataset. In terms of features, the training dataset is fairly close to the actual dataset, and it gives the algorithm with the labeled parameters. After training, the algorithm understands how the data behaves and the relation between the input and the output.
Supervised learning can be split into two subtypes:
Classification:
Predicts discrete values that correspond to a specific class and assess them based on their accuracy. There are two kinds. Binary classification and multi-class classification.
Regression:
Predicts approximate, continuous values closest to the real value. There are two kinds. Binary classification and multi-class classification. Then evaluate it by computing the error value
Lesser the error value, better the model.
Example of Supervised Learning Algorithms:
- Random Forest
- Linear Regression
- Decision Trees
- Support Vector Machine (SVM)
- Gaussian Naive Bayes
- Nearest Neighbor
Applications of Supervised Learning:
a. Digital marketing:
It is impossible to go two minutes on the internet without encountering an ad. Supervised learning algorithms place ads in prominent, eye-catching locations. This algorithm is supplied with historical data of the ads and locations that garnered the most interest. They make predictions off of that.
b. Spam Filtering:
Most modern email providers have a spam eradication system in place. These systems learn how to filter out emails from spammers using supervised learning algorithms. We feed these algorithms with data and labels (spam/not spam)
c. Face Recognition:
Supervised learning models that are trained on a labeled (face/not face) dataset look for faces in pictures. These algorithms can even identify a particular person’s face from others
2. Unsupervised Machine Learning
Unsupervised learning algorithms work with unlabelled data. This means that we don’t require human labor to make the dataset, so these algorithms suit very large amounts of data. Instead of a predefined and fixed problem statement, unsupervised learning algorithms adapt to the input by identifying patterns in the data dynamically.
Clustering:
Groups data into groups based on similarities.
Suppose we present a model with images of apples and oranges, based on some patterns in the images, it creates clusters and groups the images into those clusters. Now if the model gets new data, it is automatically grouped to one of these.
Association:
These algorithms find a relationship between groups of data. Shopping stores, for example, utilize algorithms based on this method to determine the link between one product’s sale and other product sales based on user behavior. Once properly educated, such models may be utilized to improve sales by devising various offers.
Examples of unsupervised learning techniques include:
- Hierarchical Clustering
- K-Means Clustering
- BIRCH – Balanced Iterative Reducing and Clustering using Hierarchies
- DBSCAN – Density-Based Spatial Clustering of Applications with Noise
Applications of Unsupervised Machine Learning:
a. Content recommendation:
Netflix and Prime use details like movie length, genre, and users’ watch histories to recommend other shows or movies to them. This is done using unsupervised algorithms. These algorithms spot associations in the data and predict similar content.
b. Product recommendation:
Based on users’ purchase history and wishlists, product recommendations are made. Unsupervised learning algorithms find the associations between the data provided and make their predictions.
3. Reinforcement Machine Learning
Reinforcement learning uses trial-and-error approach to better itself and learn from new scenarios. Favorable outcomes are promoted or reinforced, whilst unfavorable outcomes are discouraged or punished. This eventually leads to a more accurate version of the algorithm, which assesses if the outcome is beneficial or not.
In the event that the program finds the proper answer, the algorithm is rewarded. If the results are unfavorable, the algorithm is compelled to repeat until a better result is found.
Examples of reinforcement learning algorithms:
- Deep Adversarial Networks
- Temporal Difference (TD)
- Q-Learning
Applications of Reinforcement Learning:
a. Production:
Assembly lines involve monotony work that human resources are wasted on. Having robotic applications learn to do the work without being hardcoded is ideal. We can apply reinforcement learning here.
b. Resource Management:
Reinforcement learning is useful for handling difficult situations. For example, it can deal with the necessity to strike a balance between several criteria. Google’s data centers for instance, reinforcement learning to strike a compromise between the necessity to meet our electricity demand while also reducing expenditures.
4. Semi-supervised Learning Method
This is a hybrid of supervised and unsupervised learning methods. This strategy helps to mitigate the drawbacks of both of the previous learning techniques.
Labeling data in supervised learning is a manual process that is time consuming and expensive due to the large amount of data. The domains of applicability for unsupervised learning are quite restricted. Semi-supervised learning alleviates these issues.
The model is first trained using unsupervised learning in this method. This guarantees that the majority of the unlabeled data is clustered. For the remaining unlabeled data, labels are generated and categorization is carried out with simplicity. Speech recognition and analysis, protein categorization, text classification, and other domains benefit greatly from this method. This is an example of a hybrid learning scenario.
5. Multiple Instance Learning Method
You may think of it as a very sophisticated kind of unsupervised learning that also involves supervisory information. Only in this scenario, humans do not label the data. The data is extracted and labeled by the model automatically. It accomplishes this with the use of embedded information that serves as supervisory data.
We can deduce this from information on cats and dogs. The algorithm places a lot of importance on the position of the photos’ squares. It analyzes the location of squares in one image to the position of squares in another image. It has a variety of applications and is quite future, and it leverages spatial context as supervisory data in this situation.
This is comparable to the approaches of supervised, unsupervised, and semi-supervised learning. So we call it hybrid learning.
6. Inductive Learning Method
Inductive learning entails the development of a generalized rule that applies to all the information. In this case, we have data as input and outcomes as output; we must determine the relationship between the two.
However, it is an effective approach in machine learning. It works in a variety of industries such as facial recognition technology, illness treatment, and diagnostics, and so on. It employs a bottom-up strategy.This technique is critical because it establishes a link between data that may be referenced in the future. It’s employed when human knowledge isn’t enough, when the results aren’t consistent, and so forth. To put it another way, we generalize inferences from given data through inductive learning.
This field of machine learning is currently under investigation, since there are several ideas for improving the algorithm’s performance and effectiveness. Inductive reasoning is another moniker for the field. It’s much like learning under supervision.
7. Deductive Learning Method
Deductive learning or reasoning is a type of reasoning similar to inductive reasoning. In fact, reason is an AI concept that incorporates both inductive and deductive insights.
In contrast to inductive learning, which focuses on the extension of specific facts, deductive learning relies on the data and details currently accessible to reach a correct conclusion. It employs a top-down strategy.
One thing to keep in mind is that the results of deductive learning are definite, i.e., yes or no. Inductive learning is probability-based, which means it can range from strong to weak.
8. Transductive Learning Method
Both the training and testing data are pre-analyzed in transductive learning. The information gleaned from these databases is the most valuable. After training from the training data, the model attempts to predict the labels for testing datasets. When it comes to labeling, patterns and the learning process are quite useful. This form of learning is common in TSVM (transductive SVM) and various LPAs (label propagation algorithms).
The model in inductive learning only operates with the training data, which is one of the primary contrasts between transductive and inductive learning. The trained model is now up against a fresh hurdle. It is executed on a brand-new dataset that the model has never seen before.
Inductive learning uses predictive models. It forecasts a new data point quickly in the case of a new data point. Transductive learning, on the other hand, evaluates both training and testing data and lacks a prediction model.
When we receive a new data point, the complete model is re-run and re-trained. This is expensive in terms of resources, time and money.
9. Multi-task Learning Method
Many businesses are now focusing on this method of learning since it stresses a model that allows people to execute numerous activities at once without difficulty. It is particularly valuable in the fields of deep learning and neural network technology.
This form of learning is beneficial in NLP, speech recognition, and other areas. It aids in forecasting as well as improving the accuracy of discovering results. It might also aid in the development of multiprocessor technology.
10. Active Learning
It’s a method of learning that’s semi-supervised. We create a sophisticated classifier to analyse the information in this step. We must also bear in mind that the dataset must only contain important data points and not any irrelevant information.
Amount of the data affects processing speed. This is preferable to passive learning, which requires the analysis of bigger datasets with a wider range of data. Unwanted data is also included. Active learning reduces this by selecting data points depending on specific circumstances.
11. Self-Supervised Learning
It is a far more sophisticated kind of unsupervised learning that needs some labeled data as well. But, it does not need human intervention to label data. The data is extracted and labeled by the model itself. For example, to contrast between two images, the algorithm compares the location of rectangles in one image to the location of rectangles in another image. It employs geographical context, has a wide variety of applications, and is extremely futuristic.
Summary
Machine learning is arguably one of the most pervasive technologies of our age. In this article, we’ve analyzed all of these algorithms and learning techniques. We have a fast summary of the three major subtypes in this post, along with a few more recent developments.