Machine Learning Algorithms Cheat Sheet
Last Updated :
22 Apr, 2025
Machine Learning Algorithms are a set of rules that help systems learn and make decisions without giving explicit instructions. They analyze data to find patterns and hidden relationships. And using this information, they make predictions on new data and help solve problems. This cheatsheet will cover most common machine learning algorithms.
For example, they can recognize images, make predictions for the future using the historical data or group similar items together while continuously learning and improving over time. It is a smart way for computers to evolve and become better at different tasks.
There are different types of machine learning algorithms, each used to solve a different kind of problems. They can be divided into 4 types:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Semi-Supervised Learning
Supervised Learning Algorithms
Supervised Learning involves training a model on a labeled dataset, that is each training point is paired with its output label. The goal is to learn from these pairs so that the model can predict labels for new, unseen data. It involves regression and classification. Common Supervised Learning Algorithms are:
- Linear regression
- Logistic regression
- Decision trees
- Random forest
- SVMs
- KNN
- Naive Bayes
- Ensemble learning
| Supervised Learning Algorithms | Description | Purpose | Best Use-Cases |
|---|
| Linear Regression | Predicts continuous output based on input features. | Predict continuous numerical outcomes. | Predicting house prices, Forecasting sales or revenue. |
|---|
| Logistic Regression | Predicts probability of input belonging to a certain class. | Classify data between two distinct classes. | Spam detection, Predicting customer purchases. |
|---|
| Decision Trees | Splits data into subsets based on input features. | Simplify decision-making processes. | Customer segmentation, Diagnosing diseases. |
|---|
| Random Forest | Ensemble learning method combining multiple decision trees. | Improve prediction accuracy and control overfitting. | Credit scoring, Predicting stock prices. |
|---|
| Support Vector Machines (SVM) | Finds the hyperplane that best separates classes. | Maximize the margin between classes. | Image classification, Handwriting recognition. |
|---|
| k-Nearest Neighbors (k-NN) | Predicts based on proximity to known data points. | Classify and predict based on proximity to known data points. | Recommender systems, Intrusion detection. |
|---|
| Naive Bayes | Classifies based on probabilistic relationships. | It assumes independence between features. | Spam filtering, Sentiment analysis. |
|---|
Ensemble Learning | Combines multiple models like decision trees to improve prediction. | Improve model accuracy and robustness. | Fraud detection from multiple models, Large-scale prediction tasks. |
|---|
Unsupervised Learning Algorithms
Unsupervised Learning deals with unlabeled data. The goal is to find hidden patterns or structures in the input data. Unsupervised learning consists of clustering and association. Unsupervised Learning Algorithms include:
| Unsupervised Learning Algorithms | Description | Purpose | Best Use-Cases |
|---|
| k-Means Clustering | Partitions data into k clusters based on nearest mean. | Groups similar data points together. | Market segmentation, Document clustering. |
|---|
| Hierarchical Clustering | Builds a hierarchy of clusters using agglomerative or divisive approaches. | Create a hierarchy of nested clusters. | DNA gene data analysis, Social network analysis. |
|---|
| Principal Component Analysis (PCA) | Reduces dimensionality by transforming data into a new coordinate system. | Reduce the dimensionality of data. | Image compression, Feature extraction. |
|---|
| t-Distributed Stochastic Neighbor Embedding (t-SNE) | Non-linear dimensionality reduction for visualizing high-dimensional datasets. | Visualize high-dimensional data. | Visualizing clusters in data, Exploring patterns in large datasets. |
|---|
| Apriori Algorithm | Mines frequent item sets and learns association rules. | Discover association rules in large datasets. | Market basket analysis, Recommender systems. |
|---|
Reinforcement Learning Algorithms
Reinforcement learning (RL) involves training an agent to make a sequence of decisions by rewarding it for good actions and punishing it for bad ones. Common Reinforcement Learning Algorithms include:
| Reinforcement Learning Algorithms | Description | Purpose | Best Use-Cases |
|---|
| Q-Learning | RL algorithm that learns the value of an action in a particular state using a Q-table. | Learn optimal actions in a given environment. | Game playing, Robotics. |
|---|
| Deep Q-Networks (DQN) | Combines Q-learning with deep neural networks for high-dimensional spaces. | Handle complex state spaces. | Autonomous driving, Complex strategy games. |
|---|
| Actor-Critic Methods | Combine value-based and policy-based approaches to balance exploration and exploitation. | Balance exploration and exploitation. | Real-time strategy games, Dynamic resource allocation. |
|---|
Semi-Supervised Learning Algorithms
Semi-supervised learning is a hybrid approach that uses both supervised and unsupervised learning. It uses a small amount of labeled data with a larger amount of unlabeled data to supervise the learning process while extracting patterns from the unlabeled data. This approach is useful when whole labeled data is not available or when we want to train a model for quick adaptions.
For example, in medical analysis we want to diagnose a rare disease as it is rare its data is very limited so we use semi supervised learning to train model on unlabeled data using very few labeled data. Semi-Supervised Learning Algorithms include:
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