Image by Author | Ideogram
# Introduction
From your email spam filter to music recommendations, machine learning algorithms power everything. But they don’t have to be supposedly complex black boxes. Each algorithm is essentially a different approach to finding patterns in data and making predictions.
In this article, we’ll learn essential machine learning algorithms that every data professional should understand. For each algorithm, I’ll explain what it does and how it works in plain language, followed by when you should use it and when you shouldn’t. Let’s begin!
# 1. Linear Regression
What it is: Linear regression is a simple and effective machine learning algorithm. It finds the best straight line through your data points to predict continuous values.
How it works: Imagine you’re trying to predict house prices based on square footage. Linear regression tries to find the best fit line that minimizes the distance between all your data points and the line. The algorithm uses mathematical optimization to find the slope and intercept that best fit your data.
Where to use it:
- Predicting sales based on advertising spend
- Estimating stock prices
- Forecasting demand
- Any problem where you expect a roughly linear relationship
When it is useful: When your data has a clear linear trend and you need interpretable results. It’s also great when you have limited data or need quick insights.
When it is not: If your data has complex, non-linear patterns, or has outliers and dependent features, linear regression will not be the best model.
# 2. Logistic Regression
What it is: Logistic regression is also simple and is often used in classification problems. It predicts probabilities, values in the range [0,1].
How it works: Instead of drawing a straight line, logistic regression uses an S-shaped curve (sigmoid function) to map any input to a value between 0 and 1. This creates a probability score that you can use for binary classification (yes/no, spam/not spam).
Where to use it:
- Email spam detection
- Medical diagnosis (disease/no disease)
- Marketing (will customer buy/not buy)
- Credit approval systems
When it is useful: When you need probability estimates along with your predictions, have linearly separable data, or need a fast, interpretable classifier.
When it is not: For complex, non-linear relationships or when you have multiple classes that aren’t easily separable.
# 3. Decision Trees
What it is: Decision trees work exactly like human decision-making. They ask a series of yes/no questions to reach a conclusion. Think of it as a flowchart that makes predictions.
How it works: The algorithm starts with your entire dataset and finds the best question to split it into more homogeneous groups. It repeats this process, creating branches until it reaches pure groups (or stops based on predefined criteria). Therefore, the paths from roots to leaves are decision rules.
Where to use it:
- Medical diagnosis systems
- Credit scoring
- Feature selection
- Any domain where you need naturally explainable decisions
When it is useful: When you need highly interpretable results, have mixed data types (numerical and categorical), or want to understand which features matter most.
When it is not: They’re often prone to overfitting, unstable (small data changes can create very different trees).
# 4. Random Forest
What it is: If one decision tree is good, many trees are better. Random forest combines multiple decision trees to make more robust predictions.
How it works: It creates multiple decision trees. Each of the decision trees is trained on a random subset of the data using a random subset of features. For predictions, it takes a vote from all trees and uses the majority wins for classification. As you can already guess, it uses the average in regression problems.
Where to use it:
- Classification problems like network intrusion detection
- E-commerce recommendations
- Any complex prediction task
When it is useful: When you want high accuracy without much tuning, need to handle missing values, or want feature importance rankings.
When it is not: When you need very fast predictions, have limited memory, or require highly interpretable results.
# 5. Support Vector Machines
What it is: Support vector machines (SVM) finds the optimal boundary between different classes by maximizing the margin. Margin is the distance between the boundary and the nearest data points from each class.
How it works: Think of it as finding the best fence between two neighborhoods. SVM doesn’t just find any fence; it finds the one that’s as far as possible from both neighborhoods. For complex data, it uses “kernel tricks” to work in higher dimensions where linear separation becomes possible.
Where to use it:
- Multiclass classification
- On small to medium datasets with clear boundaries
When it is useful: When you have clear margins between classes, limited data, or high-dimensional data (like text). It’s also memory efficient and versatile with different kernel functions.
When it is not: With very large datasets (slow training), noisy data with overlapping classes, or when you need probability estimates.
# 6. K-Means Clustering
What it is: K-means is an unsupervised algorithm that groups similar data points together without knowing the “right” answer beforehand. It’s like organizing a messy room by putting similar items together.
How it works: You specify the number of clusters (k), and the algorithm places k centroids randomly in your data space. It then assigns each data point to the nearest centroid and moves the centroids to the center of their assigned points. This process repeats until the centroids stop moving.
Where to use it:
- Customer segmentation
- Image quantization
- Data compression
When it is useful: When you need to discover hidden patterns, segment customers, or reduce data complexity. It’s simple, fast, and works well with globular clusters.
When it is not: When clusters have different sizes, densities, or non-spherical shapes. It also isn’t robust to outliers and requires you to specify k beforehand.
# 7. Naive Bayes
What it is: Naive Bayes is a probabilistic classifier based on Bayes’ theorem. It’s called “naive” because it assumes all features are independent of each other, which is rarely true in real life but works surprisingly well in practice.
How it works: The algorithm calculates the probability of each class given the input features by using Bayes’ theorem. It combines the prior probability (how common each class is) with the likelihood (how likely each feature is for each class) to make predictions. Despite its simplicity, it’s remarkably effective.
Where to use it:
- Email spam filtering
- Text classification
- Sentiment analysis
- Recommendation systems
When it is useful: When you have limited training data, need fast predictions, work with text data, or want a simple baseline model.
When it is not: When feature independence assumption is severely violated, you have continuous numerical features (though Gaussian Naive Bayes can help), or need the most accurate predictions possible.
# Conclusion
The algorithms we’ve discussed in this article form the foundation of machine learning, including: linear regression for continuous predictions; logistic regression for binary classification; decision trees for interpretable decisions; random forests for robust accuracy; SVMs for simple but effective classification; k-means for data clustering; and Naive Bayes for probabilistic classification.
Start with simpler algorithms to understand your data, then use more complex methods when needed. The best algorithm is often the simplest one that effectively solves your problem. Understanding when to use each model is more important than memorizing technical details.
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.
