Introduction

Machine Leaning is a branch of artificial intelligence that focuses on developing algorithms and models which enable computers to learn from and make predictions or decisions based on data.

Key Steps in Creating a Machine Learning Model:

• Gathering Data: Collecting a relevant and representative dataset for training and testing machine learning models.
• Creating a Prediction Model: Writing prediction models capable of making predictions or classifications based on the input data, and they can range from simple linear models to complex neural networks.
• Formulating a Loss Function: Choosing a loss function that quantifies how well the prediction model chosen is performing.
• Optimizing the Loss Function: Optimization techniques, such as gradient descent, are used to adjust the model's parameters to reduce the loss and improve the model's accuracy and performance.

General Equations to know:

Symbol	Description	Equation
SSE	Sum of Squared Errors	\(\sum_{i=1}^{n} \left(y_i - y'_i\right)^2 \)
SSR	Sum of Squared Regression	\( \sum_{i=1}^{n} (y'_i - \bar{y'}_i)^2 \)
SST	Sum of Squared Total	\( \sum_{i=1}^{n} (y_i - \bar{y})^2 \)
MSE	Mean Square Error	\( \frac{1}{n}\sum_{i=1}^{n} \left(y_i - y'_i\right)^2 \)
\( \sigma_x^2 \)	Variance of X	\( \frac{1}{n}\sum_{i=1}^{n}(x_i - \bar{x_i})^2 \)
\( \sigma_y^2 \)	Variance of Y	\( \frac{1}{n}\sum_{i=1}^{n}y_i - \bar{y_i})^2 \)
\( \sigma_{xy} \)	Co-Variance of X & Y	\( \frac{1}{n}\sum_{i=1}^{n}(y_i - \bar{y_i})(x_i - \bar{x_i}) \)
r	Correlation	\(\frac{\sigma_{xy}}{\sigma_{x}\sigma_{y}} \)
\(R^2\)	R-Squared	\(\frac{SST}{SSR} = 1 - \frac{SSE}{SSR} = 1 - \frac{MSE}{\sigma_y^2} \)