Part 0 - Essential Machine Learning Algorithms for AI Engineers

Machine Learning Algorithms - A Comprehensive Guide to Supervised, Unsupervised, and Specialized Algorithms

This article offers a comprehensive overview of essential machine learning algorithms that every AI engineer should know. These algorithms are categorized into supervised, unsupervised, and specialized categories, providing a solid understanding of their applications and implementations.

Supervised Learning Algorithms

Supervised learning algorithms are trained on labeled data, where the target outcomes are already known. These algorithms are commonly used for prediction, classification, and regression tasks.

The main supervised learning algorithms include:

• Regression Algorithms:

  • Linear Regression

  • Ridge Regression

  • Lasso Regression

  • Polynomial Regression

• Classification Algorithms:

  • Logistic Regression

  • K-Nearest Neighbors (KNN)

  • Support Vector Machines (SVM)

  • Decision Trees

  • Random Forest

  • Gradient Boosting

  • Naive Bayes

Unsupervised Learning Algorithms

Unsupervised learning algorithms work with unlabeled data, aiming to discover hidden patterns or structures within the data. They are often used for clustering, dimensionality reduction, and anomaly detection.

Key unsupervised learning algorithms include:

• Clustering Algorithms:

  • K-Means Clustering

  • Hierarchical Clustering

  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

  • Gaussian Mixture Models (GMM)

• Dimensionality Reduction Algorithms:

  • Principal Component Analysis (PCA)

  • T-distributed Stochastic Neighbor Embedding (TSNE)

  • Autoencoders

Specialized Machine Learning Algorithms

These algorithms do not fit strictly into the supervised or unsupervised categories and are designed for specific types of tasks or data.

The specialized machine learning algorithms discussed are:

• Semi-Supervised Learning:

  • Self-Training

• Reinforcement Learning:

  • Q-Learning

  • Deep Q-Networks (DQN)

  • Policy Gradient Methods

• Anomaly Detection Algorithms:

  • One-Class SVM

  • Isolation Forest

• Neural Networks (Deep Learning):

  • Convolutional Neural Networks (CNN)

  • Recurrent Neural Networks (RNN)

  • Long Short-Term Memory (LSTM)

  • Transformers

Conclusion

Each of these algorithms has its strengths and ideal use cases, ranging from basic prediction to handling complex, high-dimensional data. This article provides a comprehensive list of essential machine learning algorithms that an AI engineer should know, organized into supervised, unsupervised, and specialized categories.