Machine learning (ML) is a subset of artificial intelligence that uses algorithms to teach computers to learn from data and make decisions without explicit programming,. Rather than following static instructions, ML models identify patterns within data to perform tasks such as prediction, classification, and clustering.

Supervised Learning

In supervised learning, models are trained on labeled data, meaning the input data is paired with the correct output. The goal is to learn a mapping function to predict labels for new, unseen data.

🌿 Machine Learning - Supervised Learning

Unsupervised Learning

Unsupervised learning works with unlabelled data to discover hidden patterns, structures, or groupings without predefined answers.

🌿 Machine Learning - Unsupervised Learning

Model Evaluation and Validation

To ensure a model generalizes well to new data, specific validation techniques and metrics are used.

🌿 Evaluating and Validating Machine Learning Models

Machine Learning tools

🌿 Machine Learning tools

Examples of algorithms

• Supervised learning
  • Linear regression
  • LASSO and ridge regression
  • Logistic regression
  • Decision tree
  • Random decision forests
  • Support Vector machines (SVM)
• Unsupervised learning
  • k-means
  • Naive Bayesian Classifier
• Reinforcement Learning
  • Q-learning
  • Policy gradient
• Deep Learning
  • Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
  • Encoders and transformers

Sources:

• LinkedIn Learning: Artificial Intelligence Foundations Machine Learning
• Said Business School Oxford AI Course, Module 2
• https://www.coursera.org/learn/machine-learning-with-python/