Machine Learning Algorithms

16 August, 2024 | Ehsan Masoudi | Artifitial Intelegence

Machine learning algorithms are computational models that allow computers to understand patterns and forecast or make judgments based on data without the need for explicit programming. These algorithms form the foundation of modern artificial intelligence and are used in a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, fraud detection, autonomous cars etc.

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This Machine learning Algorithms article will cover all the essential algorithms of machine learning like Support vector machine, decision-making, logistics regression, naive bayees classifier, random forest, k-mean clustering, reinforcement learning, vector, hierarchical clustering, xgboost, adaboost, logistics, etc.

Types of Machine Learning Algorithms

There are three types of machine learning algorithms.

Supervised Learning
- Regression
- Classification
Unsupervised Learning
- Clustering
- Dimensionality Reduction
Reinforcement Learning

Types of Machine Learning Algorithms

1. Supervised Learning Algorithm

Supervised learning is a type of machine learning algorithms where we used labeled dataset to train the model or algorithms. The goal of the algorithm is to learn a mapping from the input data to the output labels, allowing it to make predictions or classifications on new, unseen data.

Supervised Machine Learning Algorithms
Linear Model: Regression Ordinary Least Square Regression Simple Linear Regression Multiple Linear Regression Polynomial Regression Orthogonal Matching Pursuit (OMP) Bayesian Regression Quantile Regression Isotonic regression Stepwise regression Least-angle regression (LARS) Classification: Logistic Regression Sigmoid & Softmax functions Regularization: Lasso (L1 Regularization) Ridge (L2 Regularization) Ridge regression Ridge Classifier Elastic Net LARS Lasso K-Nearest Neighbors (KNN): Brute Force Algorithms Ball Tree and KD Tree Algorithms K-Nearest Neighbors (KNN) Classifier K-Nearest Neighbors (KNN) Regressor Support Vector Machines: Support Vector Machines Classifier Support Vector Machines Regressor Different Kernel functions in SVM Stochastic Gradient Descent Stochastic Gradient Descent Classifier Stochastic Gradient Descent Regressor Different Loss functions in SGD Decision Tree: Decision Tree Algorithms Iterative Dichotomiser 3 (ID3) Algorithms C5. Algorithms Classification and Regression Trees Algorithms Decision Tree Classifier Decision Tree Regressor Ensemble Learning: Bagging (Bootstrap Aggregating) Random Forest Extra Trees Boosting AdaBoost XGBoost CatBoost Gradient Boosting Machines (GBM) LightGBM Stacking Generative Model Naive Bayes Gaussian Naive Bayes Multinomial Naive Bayes Bernoulli Naive Bayes Gaussian Processes Gaussian Process Regression (GPR) Gaussian Process Classification (GPC) Gaussian Discriminant Analysis Linear Discriminant Analysis (LDA) Quadratic Discriminant Analysis (QDA) Bayesian Belief Networks Hidden Markov Models (HMMs) Time Series Forecasting: Time Series Visualization and Analysis: Time Series Components: Trend, Seasonality, and Noise Time Series Decomposition Techniques Seasonal Adjustment and Differencing Autocorrelation and Partial Autocorrelation Functions Stationarity Augmented Dickey-Fuller Test Seasonal Decomposition of Time Series (STL Decomposition) Box-Jenkins Methodology for ARIMA Models Time Series Forecasting Algorithms: Moving Average (MA) and Weighted Moving Average Exponential Smoothing Methods (Simple, Double, and Triple) Autoregressive (AR) Models Moving Average (MA) Models Autoregressive Integrated Moving Average (ARIMA) Models Seasonal Decomposition of Time Series by Loess (STL) Seasonal Autoregressive Integrated Moving Average (SARIMA) Models ARIMAX and SARIMAX Models Supervised Dimensionality Reduction Technique: Linear Discriminant Analysis (LDA) Some of the Supervised Machine Learning Algorithms can be used used for both Classification & Regression with a little bit of modification Multiclass and multioutput algorithms: Multiclass classification OneVsRest Classifier Multilabel classification Multioutput Regression Metrics for Classification & Regression Algorithms: Regression Metrics: Mean squared error (MSE) Root mean squared error (RMSE) Mean absolute error (MAE) R-squared Adjusted R-squared Classification Metrics: Confusion matrix Accuracy Precision Recall Specificity F1 score Area under the ROC curve (AUC-ROC) Probability calibration Calibration curves Calibrating a classifier Cross Validation Technique: K-fold cross-validation Stratified k-Fold Cross-Validation Leave-one-out cross-validation Shuffle Split Cross-Validation Time Series Cross-Validation Optimization Technique: Gradient Descent Stochastic Gradient Descent Mini-batch Gradient Descent Momentum-based Gradient Descent Newton-Based Optimization Techniques Newton’s algorithm Quasi-Newton Methods (BFGS, L-BFGS) Conjugate Gradient Local Search Optimization Techniques Hill Climbing Tabu Search

Supervised Machine Learning Algorithms

Linear Model:
- Regression
- Classification:
  - Logistic Regression
    - Sigmoid & Softmax functions
- Regularization:
  - Lasso (L1 Regularization)
  - Ridge (L2 Regularization)
    - Ridge regression
    - Ridge Classifier
  - Elastic Net
  - LARS Lasso
K-Nearest Neighbors (KNN):
Support Vector Machines:
Stochastic Gradient Descent
Decision Tree:
- Decision Tree Algorithms
- Decision Tree Classifier
- Decision Tree Regressor
Ensemble Learning:
Generative Model
Time Series Forecasting:
- Time Series Visualization and Analysis:
- Time Series Forecasting Algorithms:
Supervised Dimensionality Reduction Technique:
- Linear Discriminant Analysis (LDA)

Some of the Supervised Machine Learning Algorithms can be used used for both Classification & Regression with a little bit of modification

Multiclass and multioutput algorithms:

Metrics for Classification & Regression Algorithms:

Regression Metrics:
Classification Metrics:
Probability calibration
- Calibration curves
- Calibrating a classifier

Cross Validation Technique:

Optimization Technique:

Gradient Descent
Newton-Based Optimization Techniques
- Newton’s algorithm
- Quasi-Newton Methods (BFGS, L-BFGS)
- Conjugate Gradient
Local Search Optimization Techniques
- Hill Climbing
- Tabu Search

2. Unsupervised Learning Algorithm

Unsupervised Learning is a type of machine learning algorithms where the algorithms are used to find the patterns, structure or relationship within a dataset using unlabled dataset. It explores the data’s inherent structure without predefined categories or labels.

Unsupervised Machine Learning Algorithms
Clustering Centroid-based Methods K-Means clustering K-Means++ clustering K-Mode clustering Fuzzy C-Means (FCM) Clustering Distribution-based Methods Gaussian mixture models (GMMs) Expectation-Maximization Algorithms Dirichlet process mixture models (DPMMs) Connectivity based methods Hierarchical clustering Agglomerative Clustering Divisive clustering Affinity propagation Density Based methods DBSCAN (Density-Based Spatial Clustering of Applications with Noise) OPTICS (Ordering Points To Identify the Clustering Structure) Association Rule Mining Apriori algorithm FP-Growth (Frequent Pattern-Growth) ECLAT (Equivalence Class Clustering and bottom-up Lattice Traversal) Anomaly Detection: Z-Score Local Outlier Factor (LOF) Isolation Forest Dimensionality Reduction Technique: Principal Component Analysis (PCA) t-distributed Stochastic Neighbor Embedding (t-SNE) Non-negative Matrix Factorization (NMF) Independent Component Analysis (ICA) Factor Analysis Latent Dirichlet Allocation (LDA) Isomap Locally Linear Embedding (LLE) Latent Semantic Analysis (LSA)

Unsupervised Machine Learning Algorithms

Clustering
- Centroid-based Methods
- Distribution-based Methods
- Connectivity based methods
  - Hierarchical clustering
    - Agglomerative Clustering
    - Divisive clustering
  - Affinity propagation
- Density Based methods
  - DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  - OPTICS (Ordering Points To Identify the Clustering Structure)
Association Rule Mining
Anomaly Detection:
Dimensionality Reduction Technique:

3. Reinforcement Learning

Reinforcement Learning is a type of machine learning algorithms where an agent learns to make successive decisions by interacting with its surroundings. The agent receives the feedback in the form of incentives or punishments based on its actions. The agent’s purpose is to discover optimal tactics that maximize cumulative rewards over time through trial and error. Reinforcement learning is frequently employed in scenarios in which the agent must learn how to navigate an environment, play games, manage robots, or make judgments in uncertain situations.

Reinforcement Learning
Model-Based Methods: Markov decision processes (MDPs) Bellman equation Value iteration algorithm Monte Carlo Tree Search Model-Free Methods: Value-Based Methods: Q-Learning SARSA Monte Carlo Methods Policy-based Methods: REINFORCE Algorithm Actor-Critic Algorithm Actor-Critic Methods Asynchronous Advantage Actor-Critic (A3C)

Discover the fundamental concepts driving machine learning by learning the top 10 algorithms, such as linear regression, decision trees, and neural networks.

Machine Learning Algorithm – FAQs

1. What is an algorithm in Machine Learning?

Machine learning algorithms are techniques based on statistical concepts that enable computers to learn from data, discover patterns, make predictions, or complete tasks without the need for explicit programming. These algorithms are broadly classified into the three types, i.e supervised learning, unsupervised learning, and reinforcement learning.

2. What are types of Machine Learning?

There are mainly three types of machine learning:

Supervised Algorithm

Unsupervised Algorithm

Reinforcement Algorithm

3. Which ML algorithm is best for prediction?

The ideal machine learning method for prediction is determined by a number of criteria, including the nature of the problem, the type of data, and the unique requirements. Support Vector Machines, Random Forests, and Gradient Boosting approaches are popular for prediction workloads. The selection of an algorithm, on the other hand, should be based on testing and evaluation of the specific problem and dataset at hand.

4. What‌ ‌are‌ ‌the‌ ‌10 ‌Popular‌ ‌Machine‌ ‌Learning Algorithms?‌

Below is the list of Top 10 commonly used Machine Learning (ML) Algorithms:

Linear Regression

Logistic Regression

SVM (Support Vector Machine)

KNN (K-nearest Neighbour)

Decision Tree

Random Forest

Naive Bayes

PCA (Principal Component Analysis)

Apriori algorithms

K-Means Clustering

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