Throughout our academic or tech career, we have encountered different AI and ML algorithms. Terms such as Supervised Learning, Reinforcement Learning, K-Means algorithm, K-Nearest Neighbor algorithm, DBSCAN algorithm, etc. repeatedly show up. However, we often fail to organize all these terms into a cohesive framework.
This article organizes and categorizes most of the widely used machine learning algorithms.
Artificial Intelligence (AI) has six subsets:
- Machine Learning
- Natural Language Processing
- Deep Learning
- Robotics
- Speech Recognition
- Expert Systems
Each of these learning types use various algorithms and they are listed below.
1. Supervised Learning
- used in Classification and Regression problems.
Under Classification domain, some popularly used algorithms are:
i. Logistic regression algorithm
ii. Naive Bayes algorithm
iii. K-Nearest Neighbor algorithm
iv. Support Vector Machine
v. Decision Tree
vi. Random Forest
vii. Gradient Boosting Machines (XGBoost, LightGBM)
viii. Neural Network algorithms
Under Regression domain, some popularly used algorithms are:
i. Linear Regression
ii. Polynomial Regression
iii. Support Vector Machine
iv. Decision Tree
v. Random Forest
vi. Gradient boosting
Note: Some algorithms like Decision tree, Random forest, Support Vector and Gradient boosting can be used for both classification and regression problems.
3. Semi-Supervised Learning
- some of the popular algorithms under this learning are:
i. Self-training algorithm
ii. Mixture Models
iii. Graph based methods
iv. Transductive support vector machines
Visualizing the AI/ML terminologies in a broader picture helps to better understand the algorithms and its use cases.
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