ML algorithms enable computers to learn from the available data to make predictions and inferences without requiring explicit programming instructions to perform the required tasks. Machine learning is a subset of AI. It is essentially a combination of mathematical algorithms & statistical models that power AI. These ML algorithms categorised under 3 major types.
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There are three major types of Machine Learning algorithms:
In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data.
For example: from a bunch of molecules and information about which are drugs and train a model to answer whether a new molecule is also a drug.
We will discuss 7 differnt types of supervised learning algorithms. They are:
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data.One variable is considered to be an explanatory variable(X), and the other is considered to be a dependent variable(Y).
Linear regressions can be used in business to evaluate trends and make estimates or forecasts.
For example, Medical researchers often use linear regression to understand the relationship between drug dosage and blood pressure of patients.
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Logistic regression models the probabilities for classification problems with two possible outcomes. It’s an extension of the linear regression model for classification problems.
For example, Spam Detection.
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Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter.
Applications of this Decision Tree Machine Learning Algorithm is identifying disease and risk trends.
Random forest is a supervised learning algorithm. The “forest” it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.
The random forest algorithm is used in industrial applications such as finding out whether a loan applicant is low-risk or high-risk.
Model in which artificial neurons make an input later, one or more hidden layers where calculations take place, and an output layer.
Use case, Predict the probability of a person signing up for a life insurance policy
A technique that’s typically used for classification but can be transformed to perform regression. It draws a division between classes that’s as wise as possible
Use case, Predict how many patients a hospital will need to serve in a specific time period
Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier would consider all of these properties to independently contribute to the probability that this fruit is an apple.
Use case, Check a piece of text expressing positive emotions, or negative emotions.
Classification or regression technique that generates decision trees sequentially, where each tree focuses on correcting the previous tree model. The final output is a combination of the results from all trees.
Use case, Forecast product demand and inventory.
An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own.
For example, clustering DNA patterns to analyze evolutionary biology.
Reinforcement Learning is defined as a Machine Learning method that is concerned with how software agents should take actions in an environment. Reinforcement Learning is a part of the deep learning method that helps to maximize some portion of the cumulative reward.
For example, Robotics for industrial automation.
This discussion we come to know,
There are other supervised machine learning algorithm which we did not cover here, In future we will discuss about Unsupervised Learning and Reinforcement Learning.
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