Prediction Models

What are Prediction Models in Artificial Intelligence?

Prediction models are a cornerstone of Machine Learning (ML). They are mathematical constructs used to forecast future data points or trends based on historical data. These models can help in a variety of contexts, from predicting stock prices to forecasting weather patterns, diagnosing diseases, and beyond.

Prediction models can be linear or non-linear, supervised or unsupervised, and they can also be categorized as classification or regression models, depending on the nature of the prediction and the type of output they produce.

What will I learn in these lessons?

You will learn about both linear and non-linear prediction models, which are fundamental in machine learning/AI.

Linear Regression: Linear Regression is a statistical approach for modeling the relationship between a dependent variable and one or more independent variables. In simple terms, it predicts a linear relationship between these variables. For example, it could predict sales based on advertising spend, or the house price based on its size and location.

Non-Linear Regression: While Linear Regression assumes a straight-line relationship between variables, Non-Linear Regression models a curvilinear relationship. This makes it a powerful tool for handling more complex, real-world situations where the relationship between variables isn't straight-line.

This lesson will cover the theory behind these models, how to implement them in Python using libraries such as scikit-learn, and how to evaluate their performance. You'll also learn to understand their strengths and limitations and when it's appropriate to use each model.

It'll be replete with hands-on projects and examples, you'll get to apply these models to real datasets, solidifying your understanding and preparing you to use these tools in your future machine learning projects.