Hi Friends! From this article, we will introduce you about different types of machine learning and its examples in detail. After reading this article, you will be getting fully learnt about Machine Learning Types with ease.
Introduction of Machine Learning
Machine learning is a subset of the Artificial Intelligence (AI) that serves the machines along with the capacity to automatically learn from data and past experience by getting to identify patterns to create the predictions for new processed with less human being intervention. Machine learning helps to the rescue in many circumstances where it is not possible to apply the strict algorithms.
When merged with the deep learning, computer vision, neural networks, and big data; then machine learning has amazing potential to transform every sector and increase the customer experience.
Machine Learning Types Tutorial Headlines:
In this section, we will show you all headlines about this entire article; you can check them as your choice; below shown all:
- Introduction of Machine Learning
- Types of Machine Learning
- Supervised Machine Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
- FAQs (Frequently Asked Questions)
- What are the four different types of learning in AI (Artificial Intelligence)?
- How many types of machine learning are there?
- What are the different types of machine learning with examples?
Let’s Get Started!!
Types of Machine Learning
Machine learning is getting to involve showing the vast amount of data to a machine so that it is able to learn and make prediction, find patterns, and classify data. So, here we are going to describe the major 4 types of machine learning with examples; below shown each one, you can check them:
Also Read: 35 Applications of Machine Learning | Uses of Machine Learning in Daily Life
Supervised Machine Learning:
Like as the name; Supervised machine learning is totally depend on the supervision that means, we proceed to get the train machine by using ‘Labelled‘ dataset and based on the training, and machine to be predict the outcome result. The labelled data identifies the some of the input that are already mapped to the output. Then, we can say that first we have to train the machine along with input and respectively output, and then we will ask the machine to predict the outcome result by helping the test dataset.
Here, we are going to explore with an suitable example; assume we have the input dataset of cupcakes, then first of all we have to offer the training to the machine to the understand the picture like as the shape and portion size of the food stuff, shape of dish while serving it, ingredients, colour, accompaniments and more. Once done the training, we proceed to input the image of cupcake and ask the machine to get identification the object and predict the outcome result.
The machine is well completely trained, therefore it will identify the all features of the object, like as shape, height, color, topping, and appearance and then searching out that it is a cupcake. Therefore, it will insert it in the dessert category. This entire process is about how to identify the several objects by machine in the supervised learning.
Supervised machine learning is classified into two different categories into two kinds of problems; such as:
If output variable is a binary or categorical response, then classification algorithms is implemented to fix this problem. Answer might be: ‘Available or Unavailable’, ‘Yes or Not’, and ‘Pink or Blue’, and so on. These types of categories are already available into the dataset and data is classified depend on the labelled set that is giving during the training. This is also used over the worldwide into spam detection.
Few Classification Algorithms Are:
- Decision Tree Algorithm
- Logistic Regression Algorithm
- Random Forest Algorithm
- Support Vector Machine Algorithm
Apposite the classification, the regression algorithms is going to use for solving the problem, where present the linear relationship in between the input and output variables. The regression is also used to make the predictions such as market conditions and weather.
Few Regression Algorithms Are:
- Multivariate Regression Algorithm
- Decision Tree Algorithm
- Simple Linear Regression Algorithm
- Lasso Regression
Applications of Supervised Learning:
Image Recognition: Image recognition that makes deal with cataloguing and identify the feature otherwise an object in the digital picture. This is not important and focuses able machine learning and Artificial Intelligence technology. This concept works with for many further analyses like as face detection, face recognition and pattern recognition.
Healthcare & Medical Diagnosis: Machine learning makes deal with many techniques and tools along with diagnostic and prognostic problems in the diverse medical realms. The machine learning is widely going to use for many areas in the medical.
Speech Recognition: Google allows user the best feature that is ‘Search by Voice’. It is a good example of speed recognition. In the speech recognition process, to convert the voice instruction into text format that is also called the ‘speech to Text’ otherwise ‘computer Speech Recognition’. Now these days, machine learning algorithms are mostly working in several areas of speech recognition like as Alexa, Cortana, Siri, and Google assistance; in which to use the speech recognition techniques to follow the sound instructions.
Online Fraud Detection: GPay and PayPal are also good application of machine learning that help to track the transactions and differentiating in between the illegitimate and legitimate transactions. Hence, machine learning allows to get sound cyber security by preventing the online monetary fraud.
Positive and Negative Points of Supervised Learning
Pros of Supervised Learning:
- The supervised learning is able to work with the labelled dataset; therefore we are also capable to exact ideas about the classes of objects.
- These algorithms are most useful for making the prediction the outcome result on the base of prior experience.
Cons of Supervised Learning:
- These kinds of algorithms are not capable to fix the complex tasks.
- It could be predicted the mislead output, if the test data is different from the training data.
- It has to need the most of computational time to get train the algorithm.
Apposite to supervised machine learning, unsupervised machine learning is not getting to use any labels in their dataset. In which much more unorganized data is presented and tools to specify the properties of data. This algorithm gives the leverages these kinds of tools to group, cluster, and organized the provided data in a way that any intelligent algorithm otherwise a human being can make sense of the outcome result like as the newly organized data.
It has the ability to organized vast amount of unorganized and unlabelled data makes unsupervised machine learning on demanding and interesting region. There is an overwhelming majority of the unlabelled data present around us. Whenever you are capable to make anything sensible out of this data, then it can prove higher beneficial. Hence, this machine learning algorithms make it is possible and brings highly profits. Unsupervised learning helps to make the use of data and its properties, and then we are able to call it data-driven. The result of unsupervised machine learning tasks based on the data and their formatting.
Unsupervised machine learning is further divided into two different varieties like as:
In this concept, you are the capable to find out the inherent groups from the complex data and make ensure the object classification. Therefore, this machine bucket the data depended on the features, differences, and similarities. This technique is widely using to keep understand the customer segments and purchasing behaviour, especially across the geographies.
Few popular clustering algorithms are:
- Mean-shift algorithm
- DBSCAN Algorithm
- Principal Component Analysis
- K-Means Clustering algorithm
- Independent Component Analysis
In this technique, the machines help to find interesting relations and connections among of the variables along with enlarge datasets that are offered as input. The main objective of using this technique is to search the dependency of one data item on another data item and proceed to map those variables accordingly so that it can make huge profit. This concept is most convincing to apply on the Web usage mining, continuous production, and Market Basket analysis.
Few popular association algorithms are:
- Apriori Algorithm
- Eclat Algorithm
- FP-Growth Algorithm
Applications of Unsupervised Learning:
Recommendation Systems: The recommendation systems are mostly using to unsupervised learning concept for making recommendation applications for many web apps and e-commerce websites.
Anomaly Detection: Anomaly detection is the most eminent application of the unsupervised learning that helps to identify the unusual data points along with the dataset. It is also implemented to discover the fraudulent transactions.
Grouping User Logs: The unsupervised learning can be also used to make group users’ logs and problems. It is considered as low user facing but it is still relevant sufficient to be used. Most of firms also use this as the tool to get understand the central theme of issues faced by its users and then work on it to purify like as issues. It is also going to use for designing of the product and preparing the FAQs (Frequently Asked Questions). Whenever you proceed to report an issue or the bug, then you might have possibly put the data to an unsupervised learning algorithm that then clusters it along with other identically problems.
Singular Value Decomposition: SVD system is using to help the extraction the specific information from the database. For instance, to extract information of every user who are locating the specific region?
Positive and Negative sides of Unsupervised Learning
Pros of Unsupervised Learning:
- This algorithm is going to use for complex work as compare to the supervised ones because these algorithms work on the unlabelled dataset.
- This algorithms is most preferable for many tasks for getting the unlabelled dataset is easy than to the labelled dataset.
Cons of Unsupervised Learning:
- The outcome of the unsupervised algorithm can be low accurate as the dataset is not labelled, and algorithms are not getting to train along with the exact result in prior.
- The working of unsupervised learning is more hassle as it work along with the unlabelled dataset that does not map along with output.
Semi supervised learning consists the characteristics and features of both supervised and unsupervised machine learning. It utilizes the combination of labelled and unlabelled datasets for getting to train its algorithms. By using of both kinds of datasets; semi-supervised learning short out the limitations of the option shown above.
Also Read: 25 Advantages and Disadvantages of Machine Learning | Pros and Cons
For example; a student is learning the concept under the teacher’s supervision at the campus its termed supervised learning. And, in unsupervised learning, the student self-learns the similar technique at the home without a teacher’s help. That means, student gets the revision the concept after learning under the guidelines of the teacher in campus is a semi-supervised form of learning.
It has two different variants, like as:
Self-Supervised Learning: Unsupervised learning problem is framed as the supervised issue in the order to apply the supervised learning algorithms to fix it.
Multi-Instance Learning: It is a supervised learning issue, but separately examples are unlabelled. Beyond of this, cluster or group of data are labelled.
Positive & Negative sides of Semi-supervised Learning
- Simple and easy to understand this algorithm
- It executes as higher efficient.
- To fix the limitations of supervised and unsupervised learning algorithms
- It has not stability as the iterations outputs.
- We are unable to apply these algorithms to the network level data.
- Less accuracy
In the reinforcement learning, there is no any technique of labelled data. Machine learning gets only from experience. By helping the trial and error concept, learning works on the feedback based process. Artificial Intelligent the data, notes features, learns from the prior experience, and enhance its overall the performance. The AI agent is getting to reward, if outcome is perfectly. But, it will be punished whenever output is not accurate.
For example, when the corporate employee has been offered the new project then their success will be measured depend on the positive output at the end of stint. They obtain the feedback from superior into form of reward otherwise punishments.
Reinforcement learning executes on the feedback-based process, in which the AI agent get automatically explore its surrounding by hitting and trail, taking action, learning from experience, and improving its performance. Therefore, its ways of working, this learning is employed in different areas like as Game theory, Operation Research, Information theory, multi-agent systems.
Reinforcement Learning is also classified into two different algorithms like as:
Positive Reinforcement Learning: In these algorithms, to add a reinforcing stimulus after a certain behavior of the agent, that makes it more likely that behavior might happen again in the future.
Negative Reinforcement Learning: It refers to strengthening a certain behavior that ignore the negative result.
Few popular algorithms of reinforcement learning are:
- Monte Carlo
- Deep Q network
Applications of Reinforcement Learning:
Industrial Simulation: In such industries, where are going to use robots to perform many different tasks, it becomes vital to make them able to completing its tasks without getting to any monitor. It is an inexpensive and sufficient option and it helps to eliminate the chances of failure. This machine can be programmed to use low electricity, then decrease its costs.
Resource Management: The reinforcement learning is used by the Google’s data center that helps to make balance the need to satisfy our power needs, but do it a sufficiently as possible, cutting most cost. Cheaper data storage prices for us as well and low of an impact on the environment we can share all.
Text Mining: It is also amazing application of NLP, then it is now going to implement along with the help of reinforcement learning by Salesforce Company.
Video Games: Reinforcement learning algorithms are getting more eminent in the gaming applications. Few most popular games that are utilized the RL algorithms like as AlphaGO and AlphaGO Zero. Mario game is a prime example of reinforcement learning application.
Positive and Negative sides of Reinforcement Learning
- To solve the complicated real-world problems that are most difficult to be fixed by general techniques.
- The reinforcement learning model is most closed of human beings, so it is most perfected output can be found.
- To achieving the long term outcome results
- This algorithm is not suitable for the simplest problem.
- It has to need vast data and computations.
- RL can make lead to an overload of the states that can weaken the output.
FAQs (Frequently Asked Questions)
What are the four different types of learning in AI (Artificial Intelligence)?
At the based on the techniques and way of learning, machine learning is classified into mainly four different types, which are:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-Supervised Machine Learning
- Reinforcement Learning
How many types of machine learning are there?
There are widely using three kinds of machine learning like as Supervised, Unsupervised, and Reinforcement Learning.
What are the different types of machine learning with examples?
In this article, already we have been shown above major types of machine learning in detail, you can check them.
Now, i hope that, you have been completely aware about different types of machine learning and its examples in detail. If this article is helpful for you, then please share it along with your friends, family members or relatives over social media platforms like as Facebook, Instagram, Linked In, Twitter, and more.
Also Read: 20 Examples of Machine Learning in Real Life
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