What is machine learning (ML)?

Machine Learning (ML) is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The core idea behind machine learning is to enable machines to make decisions and predictions based on data. It involves the development of algorithms that can process input data and use statistical analysis to predict an output while updating outputs as new data becomes available. Machine learning is used in a variety of applications, such as in recommendation systems, speech recognition, predictive analytics, and autonomous vehicles, among others.

The process of machine learning typically includes:

1. Data Preparation: Involves cleaning and partitioning the data into training and testing sets.
2. Choice of Model: Selection of an appropriate algorithm or model that suits the problem at hand.
3. Training the Model: The model learns from the processed data by adjusting its parameters to minimize errors.
4. Evaluation: The model’s performance is evaluated using the test set to see how well it predicts new data.
5. Parameter Tuning and Improvement: Adjusting model parameters and possibly revisiting the choice of model based on performance.
6. Deployment: Once the model performs satisfactorily, it is deployed to perform its intended task in real-world applications.

Machine learning algorithms are categorized into three main types:

1. Supervised Learning: The algorithm learns from a labeled dataset, trying to predict outcomes for new data based on the patterns it has learned from the training data.
2.