Machine Learning

What is machine learning?

Machine learning is a subfield of artificial intelligence that focuses on building algorithms that can learn from data and improve tasks with more experience. The goal is to create mathematical models that can analyse input data to predict output values or make decisions without being explicitly programmed to do so.

Some common tasks include:

• Image recognition: Identifying objects or patterns in images
• Natural language processing: Understanding and generating human language
• Predictive analytics: Making predictions about future outcomes based on past data
• Anomaly detection: Detecting unusual data points that differ from expected patterns

What is the difference between machine learning and artificial intelligence?

Artificial intelligence is a broad term for the simulation of human intelligence in machines. Machine learning is a subset of AI, focusing specifically on learning from data to make predictions without explicit programming.

As a result:

• Machine learning relies on pattern recognition and statistical analysis of data to learn and improve. AI encompasses a broader range of techniques.

How does it work?

There are different methods, but the core process includes:

1. Collection of relevant data.
2. Pre-processing and cleaning of the data.
3. Splitting the data into training data and test data.
4. Choosing and applying a suitable algorithm like supervised or unsupervised learning.
5. Training the model on the training data to find patterns.
6. Testing model performance on the test data.
7. Refining and optimising the model until the accuracy is high enough.
8. Deploying the model to make relevant predictions for new data.

The quality and amount of data is crucial for developing good models. The algorithms seek to maximise prediction accuracy while minimising errors.