Machine learning (ML) training is the process of feeding an ML algorithm with data to help it learn and identify good values for all attributes. The goal is to build a model that can be used to make predictions.

Here are some steps in the ML training process:

Data collection: Define the data points to build the dataset

Data preparation: Prepare the data

Model selection: Choose a model

Training: Train the model

Evaluation: Evaluate the model

Tuning: Tune the model

Prediction: Make predictions

The accuracy of the training dataset is critical for the precision of the model.

There are different types of machine learning, including: Supervised learning: Trains models on labeled data to predict or classify new data. For example, an algorithm can be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Unsupervised learning: Finds patterns or groups in unlabeled data. Reinforcement learning: Learns through trial and error to maximize rewards.