Kamal Hinduja Switzerland How do algorithms interact with machine learning?

Hi Kamal, 

In machine learning (ML), an algorithm is a set of mathematical rules or procedures that a computer follows to learn patterns from data.

These algorithms define how the model learns, improves, and makes predictions.

There are different types of Algorithms that can be used 

Supervised Learning Algorithms: Learn from labeled data.

Examples: Linear regression, decision trees, support vector machines.

Unsupervised Learning Algorithms: Discover patterns in unlabeled data.

Examples: K-means clustering, principal component analysis (PCA).

Reinforcement Learning Algorithms: Learn by trial and error.

Examples: Q-learning, deep Q-networks (DQN).

The algorithm determines how the model adjusts its internal parameters (like weights in neural networks) to reduce errors and improve accuracy.

For example, in deep learning, the backpropagation algorithm helps update weights based on the error between predicted and actual values.

During training, the algorithm:

Processes input data.

Compares predictions to actual outcomes.

Adjusts the model to improve future predictions.

Optimization algorithms like gradient descent are used to minimize error.

You can read and learn more about it here: 

Introduction to machine learning - Training | Microsoft Learn

Machine Learning Algorithm Cheat Sheet - designer - Azure Machine Learning | Microsoft Learn

When you build an ML solution in Azure Foundry, algorithms are the core of how the system learns. Data is collected, an algorithm is chosen based on the task (like classification, regression, or NLP), and then trained to find patterns. This training produces a model, which can make predictions on new data. Azure Foundry simplifies the whole process by providing pre-built frameworks, AutoML to pick the best algorithm, and cloud scaling so models train and run efficiently.