AI & ML Made Simple: A Beginner’s Guide - Prerequisite

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Before diving into machine learning, it’s important to get familiar with the basic concepts and key terms often used in this field. As you go deeper into the subject, one of the biggest challenges will be remembering what certain terms mean. Without a solid understanding of these basics, learning advanced topics can feel much harder than it needs to be.

This introduction covers the essential high-level concepts you should know — many of which are concepts from high school / engineering days. Taking a moment to revisit them now will make your learning journey much smoother later on.

Let’s begin!

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 that there is a straight-line relationship between the two variables (x & y), i.e., change in y is proportional to the change in x.

  • Equation: ( y = mx + b )

Logistic Regression

It is 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 (logistic) function to output probabilities between 0 and 1.

  • Sigmoid function: Any mathematical function whose graph has a characteristic S-shaped or Sigmoid 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 & columns are features). In linear regression, a vector can represent the coefficients, and a matrix can represent the features of the data. Matrices and vectors form the backbone of many algorithms used in machine learning.

  • Matrix: A rectangular array of numbers, symbols, or expressions arranged in rows and columns.
  • Vector: A one-dimensional array of numbers. If a matrix has only one row or only one column, it is called a vector.

Understand matrix multiplication.

Calculus

Differentiation & 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

Understand these:

  • True Positive (TP): Correctly predicted positive.
  • False Positive (FP): Incorrectly predicted positive (Type I Error).
  • False Negative (FN): Incorrectly predicted negative (Type II Error).
  • True Negative (TN): Correctly predicted negative.
  • 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

  • Mean
  • Median
  • Mode
  • Variance
  • Standard Deviation

Conclusion

In this article, I have tried to list the common concepts that are required to properly understand Machine Learning. A thorough understanding of these will help you to quickly learn and implement machine learning models.

In the upcoming series of articles, I will provide detailed explanations of machine learning concepts, types, use cases, and code snippets.

Happy Learning!