When it comes to “training a machine learning model”, we’re referring to the process of educating the algorithm on a dataset so that it can learn to recognize and predict patterns in the data.
The process of training a machine learning model typically involves the following steps:
- Preparing the dataset : Selecting a relevant and coherent set of data for the specific application.
- Choosing the model : Selecting the right algorithm for the task at hand, such as a convolutional neural network (CNN) for images or a decision-making model based on linear algorithms for numerical data.
- Setting hyperparameters : Setting the parameters that govern the behavior of the model, such as the number of layers, neuron dimensions, and optimization algorithm used.
- Training : Training the model on the dataset using an optimization algorithm, such as Gradient Descent (GD) or Stochastic Gradient Descent (SGD), to minimize error and improve the model’s accuracy.
- Validation : Evaluating the model on a subset of the data used for training to verify its performance and identify any issues.
The goal of training a machine learning model is to create a model that can:
- Recognize patterns and models in data
- Predict values or classes based on the information contained in the dataset
- Make accurate decisions based on real-world conditions
Training a machine learning model is an iterative process, so it’s necessary to continue training and refining the model to achieve increasingly more accurate results.