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9 Best Machine Learning Models for Beginners
Models you should learn like linear regression, logistic regression to support vector machines, and PCA
Machine learning is one of the most fascinating fields in computer science.
It has applications across numerous industries, and it’s something that anyone can learn about.
In this blog post, I’m going to go over some of the top 9 machine learning models for beginners so you can get started with ML!
1. Linear Regression
Linear regression is one of the first machine learning models that you should learn about. It’s a simple way to measure how variables are related, which makes it pretty easy to understand.
If you want to predict house prices based on square footage or number of bedrooms — this would be one way to do so! After training, linear regression produces an equation for the line that best fits the data.
Why use Linear Regression?
The main benefit of linear regression is that it’s very interpretable. You can easily understand the relationship between two variables after training a model, which works well in some cases when you need to explain how your machine learning model makes decisions like fraud detection or churn prediction.
The linear regression equation is a good way to summarize the relationship between two variables. It can be used to predict values for one variable, based on known values for the other.
Real-world applications you can use linear regression on include:
- Predicting the price of a home based on square footage or number of bedrooms
- Forecasting sales given stock levels and other factors
- Determining which variables are important to customers’ buying decisions
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