These ML algorithms help to solve different business problems like Regression, Classification, Forecasting, Clustering, and Associations, etc.
Based on the methods and way of learning, machine learning is divided into mainly four types, which are:
1.Supervised Machine Learning
2.Unsupervised Machine Learning
3.Semi-Supervised Machine Learning
4.Reinforcement Learning
Supervised learning is a type of machine learning that uses labeled data to train machine learning models. In labeled data, the output is already known. The model just needs to map the inputs to the respective outputs.
An example of supervised learning is to train a system that identifies the image of an animal.
Attached below, you can see that we have our trained model that identifies the picture of a cat.
Algorithms:
Some of the most popularly used supervised learning algorithms are:
Linear Regression
Logistic Regression
Support Vector Machine
K Nearest Neighbor
Decision Tree
Random Forest
Naive Bayes
Unsupervised Learning
Overview:
Unsupervised learning is a type of machine learning that uses unlabeled data to train machines. Unlabeled data doesn’t have a fixed output variable. The model learns from the data, discovers the patterns and features in the data, and returns the output.
Depicted below is an example of an unsupervised learning technique that uses the images of vehicles to classify if it’s a bus or a truck. The model learns by identifying the parts of a vehicle, such as a length and width of the vehicle, the front, and rear end covers, roof hoods, the types of wheels used, etc. Based on these features, the model classifies if the vehicle is a bus or a truck.
Algorithms:
Selecting the right algorithm depends on the type of problem you are trying to solve. Some of the common examples of unsupervised learning are:
K Means Clusterin
Hierarchical Clustering
DBSCAN
Principal Component Analysis
Reinforcement Learning
Overview
Reinforcement Learning trains a machine to take suitable actions and maximize its rewards in a particular situation. It uses an agent and an environment to produce actions and rewards. The agent has a start and an end state. But, there might be different paths for reaching the end state, like a maze. In this learning technique, there is no predefined target variable.
An example of reinforcement learning is to train a machine that can identify the shape of an object, given a list of different objects. In the example shown, the model tries to predict the shape of the object, which is a square in this case.
Algorithms
Some of the important reinforcement learning algorithms are:
Q-learning
Sarsa
Monte Carlo
Deep Q network
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