These are some of the important machine learning algorithms

### Decision tree

- Belongs to the family of supervised learning algorithms.
- Can be used for solving regression and classification problems too.The general motive of using
- Decision Tree is to create a training model which can use to predict class or value of target variables by learning decision rules inferred from prior data(training data)

Ex : Banker deciding whether to grant a loan.

### Neural Network

- Neural networks are one of the learning algorithms used within machine learning. They consist of different layers for analyzing and learning data.
- Every hidden layer tries to detect patterns on the picture. When a pattern is detected the next hidden layer is activated and so on.

The picture of the Audi A7 above illustrates this perfectly. The first layer detects edges. Then the following layers combine other edges found in the data, ultimately a specified layer attempts to detect a wheel pattern or a window pattern. Depending on the amount of layers, it will be or not be able to define what is on the picture, in this case a car.The more layers in a neural network, the more is learned and the more accurate the pattern detection is. Neural Networks learn and attribute weights to the connections between the different neurons each time the network processes data. This means the next time it comes across such a picture, it will have learned that this particular section of the picture is probably associated with for example a tire or a door.

### Bayesian

- Applies Bayesian theorem for regression and classification problems involved with probability.
- It attempts to show the probabilistic relationship between different variables and determine, given the variables, which category it more likely belongs to.

### K-Means

- K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups).
- The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K.
- The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. Data points are clustered based on feature similarity

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