Types of Machine Learning
Machine Learning Algorithms is a type of AI. Machine learning learns from the data, predicts, and improves based on previous performance. The extensive data is on machine learning as machine learning works on a large amount of data. Machine Learning Algorithms solves different types of problems like – regression, classification, and clustering.
There are types of machine learning with examples that help you better understand the whole topic.
Supervised Machine Learning
So the type of machine learning with the example we can say that suppose we want to go to Rishikesh and we want to know how will be the weather in Rishikesh after one week in this place the supervised machine learning steps in. as from the name we can make guesses that it is a supervision type of machine learning. So in supervised machine learning, the training of the machine is done through labeled datasets, and after the training, the machine makes predictions according to the training provided. The label specifies that the input and the output are mapped by each other.
Types of machine learning with examples in supervised learning is that suppose we have pictures of lions and tigers. The first step would provide the machine with training, which includes understanding the image, such as the shape and length of the tail, shape of eyes, height, and color. After the training ends, we would provide the machine with the lion’s image as input and tell it to give us the output of the image. Now the machine is trained, and the output would be like the shape of the eye, height and the length of the tail, etc. the main objective of supervised machine learning is to map the input variable with the output variable like risk assessment spam detection, etc.
Unsupervised Machine Learning
In this type of machine learning, there is no need for supervision in unsupervised machine learning. The training of the machine is done with the unlabeled dataset. The machine predicts the output without any supervision. The main goal of unsupervised learning is to categorize or group the unsorted datasets based on their difference, patterns, and similarities.
An example of understanding an unsupervised type of machine learning with example is that suppose you have a bouquet. Now the image is unknown to the machine. Now the machine has to make the prediction which are the patterns or categories of the object.
Semi-supervised learning
This type of machine learning lies between supervised and unsupervised learning. It is like amidst supervised and unsupervised learning. The semi-supervised learning uses a mixture of unlabeled and labeled data during the training process. It primarily operates on unlabeled data as the labeled data is costly. Still, if the work is for the corporate sector, then the labeled datasets are sued by semi-supervised machine learning.
Reinforcement Machine Learning
The working of reinforcement learning is based on feedback. The AI agent automatically explores its surrounding by the hit and trial method, taking action and improving its performance. The AI agent gets the award for the new action, and there is a punishment for the wrong action. In the machine learning example – a child learns multiple experiences from day-to-day life. The reinforcement machine learning helps in giving outcomes to the following fields – game theory and multiple agent systems.
Types of Machine Learning Algorithm
There are various types of machine learning algorithms. We are going to discuss some of them-
Linear regression
linear regression is one of the most simple and popular types of machine learning algorithms used for predictive analysis. The prediction here could mean like – salary or age. This algorithm shows the connection between independent and dependent variables and the line, which is known as the regression line. The formula for calculating linear regression is y= a0+ a*x+ b. Here y is the independent variable, and x is the dependent variable.
Logistics regression
machine learning algorithm is the supervised machine learning algorithm. It helps predict categorical variables or discrete values. It is used for solving classification problems.
Principle component analysis
Principle component analysis is the unsupervised type of machine learning algorithm which helps in giving the outputs for dimensionality reduction. It helps minimize the dimensions of the database, which has various features correlated with each other.
Apriori algorithm
It is a unsupervised type of machine learning algorithm used to solve the association problem. It works on the database that has transactions and also uses frequent item sets to generate association rules. It determines how strongly and weakly the models are connected.
K-Nearest Neighbour
The K-Nearest Neighbor algorithm assumes the correlation between the new and available data points. The new data points are put in the same categories based on these correlations. The K-Nearest Neighbour is also known as the lazy learner algorithm, as it stores all the available datasets and classifies each new case with the help of K-neighbours. The new class is the nearest class with the most similarities, and the distance between the data points is assigned. The distance function measures it. The distance functions can be Euclidean, Minkowski, Manhattan, or Hamming distance based on the requirement.
You May Also Like to Read About: Top 10 Machine Learning Algorithms for Every Beginner
Types of Machine Learning Models
There are different types machine learning models which come in various versions.
Classification model
The classification model predicts the number of groups or classes according to the limited number of options. The classification model output is always in the form of a category, whether this is spam mail or not.
Regression model
The different machine learning models have different functions in the regression model. The output variable can predict continuous variables. For example – predicting the per barrel of sesame oil for the commodity market is a regression model task. The regression model can be further divided into line regression: decision tree and random forest model.
Clustering
The model involves gathering the same objects into groups. This process helps analyze the same objects automatically without the intervention of humans. Must train effective supervised machine learning models with labeled or manually curated data. They need homogeneous data, and clustering provides a more innovative way to do it.
Dimensionality Reduction
Sometimes, the number of possible variables in real-world data sets is very high, which leads to problems. But all those variables do not have countless variables that even contribute vitally to the goal. Turning to dimensionality reduction preserves variances with a small number of variables.
Deep Learning
This different machine learning model involves neural networks. Neural networks are networks that contain mathematical equations. The network input variable is taken, then the equation is run through them, and then the output variable is produced.
Conclusion
This article was about the types of machine learning with examples- these types are supervised, unsupervised machine learning, semi-supervised machine learning, and regression machine learning explained them all through examples. There are also types of machine learning algorithms: linear regression, logistic regression, principle component analysis, apriori analysis, and k nearest neighbor. The different machine learning models are categorized as – classification, regression, clustering, dimensionality reduction, and deep learning.
