Straight Line Equation
A straight line equation represents a linear relationship between two variables, typically written as y = mx + b, where m is the slope (gradient) and b is the y-intercept.
- Slope or Gradient — represents the rate of change of the dependent variable (y) with respect to the independent variable (x).
- Gradient = Change in Y / Change in X
Linear Regression
A statistical method used to model the relationship between a dependent variable (y) and one or more independent variables (x) by fitting a linear equation to observed data. It assumes a straight-line relationship between x and y — meaning change in y is proportional to change in x.
- Equation:
y = mx + b
Logistic Regression
A statistical method used for binary classification problems, where the goal is to predict a categorical dependent variable based on one or more independent variables. It transforms a linear combination of input variables using the sigmoid function to output probabilities between 0 and 1.
- Sigmoid function — a mathematical function whose graph has a characteristic S-shaped curve. It maps any real number input to a value between 0 and 1.
Matrices & Vectors
In machine learning, matrices and vectors are used to represent datasets — rows are data points and columns are features. They form the backbone of many algorithms used in machine learning.
Calculus
Differentiation and Integration are widely used in AI and machine learning for optimization, neural networks, and data analysis.
- Differentiation — finding the rate of change.
- Integration — finding the total or area under a curve.
Probability Theory
Understanding probability is essential for evaluating model performance. These are the four key outcomes in classification:
- Probability — the likelihood of an event happening.
- Bayesian Probability — a measure of belief or confidence in an event occurring, based on prior knowledge and new evidence.
Descriptive Statistics
These five measures are foundational to understanding your data before running any model:
- Mean — the average value.
- Median — the middle value when sorted.
- Mode — the most frequently occurring value.
- Variance — how spread out the data is from the mean.
- Standard Deviation — the square root of variance; easier to interpret.
These concepts form the mathematical foundation you'll need to properly understand machine learning. A solid grasp of them will help you quickly learn and implement ML models without getting lost in the jargon.
In the upcoming articles, I'll cover machine learning concepts in detail — types, use cases, and code snippets. Happy Learning!