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.
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.