Types and Application of Machine Learning Algorithms

Before seeing types of Machine Learning let us see first what mean by machine learning.

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.

Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn—or improve performance—based on the data they ingest. Artificial intelligence is a broad word that refers to systems or machines that resemble human intelligence. Machine learning and AI are frequently discussed together, and the terms are occasionally used interchangeably, although they do not signify the same thing. A crucial distinction is that, while all machine learning is AI, not all AI is machine learning.

There are three types of machine learning algorithms.

1. Supervised Learning

Supervised learning involves training a model on a labeled dataset, where the algorithm learns to map input data to the corresponding output labels. The primary objective is to predict the output accurately for unseen data based on patterns learned during training. Common algorithms in supervised learning include linear regression, logistic regression, decision trees, support vector machines, and neural networks.

Applications of supervised learning encompass:

Classification: Identifying email spam, sentiment analysis, disease diagnosis, and handwriting recognition.

Regression: Predicting house prices, stock market trends, and weather forecasts.

1.1. Supervised Machine Learning Algorithms:

?1.1.0 Linear model:

1.1.1 ?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)

1.1.2. 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 SGDDecision 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

2. Unsupervised Learning

Unsupervised learning deals with unlabeled data, where the algorithm explores the underlying structure or patterns in the data without explicit guidance. Unlike supervised learning, there are no predefined output labels. Unsupervised learning algorithms include k-means clustering, hierarchical clustering, principal component analysis (PCA), and autoencoders.

Applications of unsupervised learning include:

Clustering:?Market segmentation, customer profiling, and anomaly detection.

Dimensionality Reduction:?Feature extraction, data compression, and visualization.

2.1. Unsupervised Machine Learning Algorithms:

2.1.0. 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)

3. Reinforcement Learning

Reinforcement learning involves training an agent to make sequential decisions in an environment to maximize cumulative rewards. The agent learns through trial and error by interacting with the environment and receiving feedback in the form of rewards or penalties. Key components of reinforcement learning include the agent, environment, actions, rewards, and policies. Popular algorithms in reinforcement learning include Q-learning, Deep Q-Networks (DQN), and policy gradients.

Applications of reinforcement learning include:

Game Playing:?Chess, Go, and video games.

Robotics:?Autonomous navigation, robotic control, and task automation.

Recommendation Systems:?Personalized recommendations and content optimization.

3. Reinforcement Learning algorithms:?

3.1. 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)

References:

1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

3. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.

4. Geeksforgeeks