5 Machine Learning Algorithms Commonly Used in Python

The following are 5 machine learning algorithms commonly used in Python:

Linear regression This is a supervised learning algorithm that is used to predict continuous values, such as house prices or sales figures. It works by fitting a line to the data and then using that line to make predictions for new data points.

Logistic regression This is another supervised learning algorithm, but it is used to predict binary values, such as whether or not a customer will click on an ad or whether or not a patient has a disease. It works by calculating the probability of each outcome and then predicting the outcome with the highest probability.

Decision tree This is a supervised learning algorithm that is used to classify data points into different categories. It works by building a tree-like structure that represents the different decision rules that can be used to classify the data.

Support vector machine (SVM) This is a supervised learning algorithm that can be used for both classification and regression tasks. It works by finding a hyperplane that separates the data points into different categories or by finding a hyperplane that best fits the data points.

Naive Bayes This is a supervised learning algorithm that is used for classification tasks. It works by assuming that the features of the data are independent of each other and then using Bayes' theorem to calculate the probability of each class.

These books are all well-written and easy to follow, making them a great choice for beginners. They cover the basics of Python programming, such as variables, data types, loops, and functions. They also provide hands-on projects that will help you learn by doing.

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