Overview
In today’s world, where automation is taking over many manual tasks, our understanding of “manual work” is rapidly changing. A wide variety of machine learning algorithms are available—some capable of performing complex tasks such as playing chess or even assisting in surgeries. As technology continues to evolve, examining the past developments in computing helps us predict future innovations.
List of Algorithms
· Linear Regression
· Logistic Regression
· Decision Tree
· Support Vector Machine (SVM)
· Naive Bayes
· K-Nearest Neighbors (KNN)
· K-Means
· Random Forest
· Dimensionality Reduction Methods
· Gradient Boosting & AdaBoost
Algorithm Summaries
Linear Regression
A technique that fits an equation that is linear to the observed connection between independent and dependent variables in order to forecast continuous outcomes.Logistic Regression
Designed for binary classification tasks (e.g., yes/no predictions), this algorithm uses a logistic function to estimate probabilities.
Decision Trees
Use a sequence of rules based on input features to predict a target variable’s value.
Random Forest
A collection of decision trees used for classification or regression, aimed at increasing accuracy and preventing overfitting.
Support Vector Machines (SVM)
Best suited for classification in high-dimensional spaces, though also applicable to regression tasks.
Neural Networks
Highly advanced models that can capture intricate, non-linear relationships—commonly applied in deep learning.
Clustering Techniques
Methods like K-means, hierarchical clustering, and DBSCAN group data points based on similarity, making intra-group items more alike than those in different groups.
Association Algorithms
Identify relationships within datasets, such as frequent item sets in market basket analysis.
Principal Component Analysis (PCA)
A dimensionality reduction technique that transforms correlated variables into a set of linearly uncorrelated components.
Q-Learning
A reinforcement learning method that evaluates the usefulness of an action in a particular state without requiring a model of the environment.
Deep Q-Networks (DQN)
Integrate Q-learning with deep neural networks to derive optimal strategies directly from complex inputs like images.
Policy Gradient Methods
These algorithms directly adjust a policy’s parameters to improve performance, rather than estimating the value of each action.
Monte Carlo Tree Search (MCTS)
Primarily used in decision-making problems (like strategy games), it simulates future possibilities to determine the best actions.
| Algorithm | Category | Common Applications | Main Benefits | Notes / Examples |
|---|---|---|---|---|
| Linear Regression | Supervised (Regression) | Predicting continuous outcomes like sales or prices | Easy to interpret, quick to train | Works best with linear relationships |
| Logistic Regression | Supervised (Classification) | Binary or multi-class predictions, such as churn detection | Produces probability scores, straightforward to use | Often a strong baseline model |
| Decision Trees | Supervised (Classification & Regression) | Customer profiling, risk evaluation | Intuitive, handles non-linear patterns | Can easily overfit without pruning |
| Random Forests | Ensemble (Supervised) | Credit risk, feature selection | Reduces overfitting, robust to noise | Averages multiple decision trees |
| Support Vector Machines (SVM) | Supervised (Classification & Regression) | Image recognition, text classification | Effective in high-dimensional spaces | Requires careful parameter tuning |
| K-Nearest Neighbors (KNN) | Supervised (Classification & Regression) | Recommender systems, anomaly spotting | Simple concept, no explicit training | Slow on large datasets |
| Naive Bayes | Supervised (Classification) | Spam detection, sentiment analysis | Extremely fast, works well on small data | Assumes features are independent |
| K-Means Clustering | Unsupervised (Clustering) | Market segmentation, grouping patterns | Simple, scalable for large data | Must predefine the number of clusters |
| Hierarchical Clustering | Unsupervised (Clustering) | Gene studies, social network analysis | Automatically builds nested clusters | Computationally demanding |
| Principal Component Analysis (PCA) | Unsupervised (Dimensionality Reduction) | Image compression, noise reduction | Simplifies data, aids visualization | Only captures linear relationships |
| Neural Networks / Deep Learning | Supervised (also Unsupervised variants) | Speech & image recognition, NLP | Excels with complex, large datasets | Needs extensive data & computing power |
| Gradient Boosting (e.g. XGBoost, LightGBM) | Ensemble (Supervised) | Fraud detection, ranking tasks | High predictive power, captures complex patterns | Must be tuned to avoid overfitting |
| Reinforcement Learning (Q-Learning, DQN) | Reinforcement | Robotics, game AI, adaptive systems | Learns optimal strategies over time | Often requires many iterations |