Hello troubleshooters! This article is a progressive part of the series of 100days of machine learning do check out previous articles too although previous days of the series were about the what is ML and difference between AI vs ML vs DL. Now in today's article we'll be heading towards machine learning algorithms lets discuss each type alongwith examples!

VARIOUS TYPES OF MACHINE LEARNING ALGORITHMS

1. Supervised Learning: Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output.

1.1 Regression algorithm: Regression algorithms are used if there is a relationship between the input variable and the output variable. It is used for the prediction of continuous variables, such as Weather forecasting, Market Trends, etc.

1.2 Classification algorithm:Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc.

2. Unsupervised learning: Unsupervised learning is a machine learning technique in which models are not supervised using training dataset. Instead, models itself find the hidden patterns and insights from the given data. It can be compared to learning which takes place in the human brain while learning new things.

2.1 Clustering algorithm: Clustering is the process of dividing uncategorized data into similar groups or clusters. This process ensures that similar data points are identified and grouped. Clustering algorithms is key in the processing of data and identification of groups (natural clusters).

2.2 Dimensionality reduction: There are always too many variables in machine learning classification problems based on which the final classification is performed. These factors are variables called features. The greater the number of characteristics, the harder it becomes to imagine and then work on the training package. Most of these characteristics are often correlated, and thus redundant. This is where algorithms for dimensionality reduction come into play. Dimensionality reduction is the method of reducing, by having a set of key variables, the number of random variables under consideration. It can be divided into feature discovery and extraction of features.

2.3 Anomaly detection: This method does require any training data and instead assumes two things about the data ie Only a small percentage of data is anomalous and Any anomaly is statistically different from the normal samples. Based on the above assumptions, the data is then clustered using a similarity measure and the data points which are far off from the cluster are considered to be anomalies.

2.4 Association rule learning: Association rule learning is a type of unsupervised learning technique that checks for the dependency of one data item on another data item and maps accordingly so that it can be more profitable. It tries to find some interesting relations or associations among the variables of dataset. It is based on different rules to discover the interesting relations between variables in the database

3. Semisupervised learning: Semi-Supervised learning is a type of Machine Learning algorithm that represents the intermediate ground between Supervised and Unsupervised learning algorithms. It uses the combination of labeled and unlabeled datasets during the training period.

4. Reinforcement learning: Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty. In Reinforcement Learning, the agent learns automatically using feedbacks without any labeled data, unlike supervised learning.

That's it for the today, upcoming articles would be also driven towards the basic building blocks of machine learning before diving into core concepts of the ML.

Read till here thanks a bunch:)