The Complete Guide to Training Machine Learning Models
I wanted to learn about training a model to see if I can train my own little model for my own needs. The Complete Guide to Training Machine Learning Models Training a machine learning model involves teaching it to make predictions or decisions by learning from data. Here’s a simple explanation of the process: 1. Data Collection and Preparation Gather relevant data from various sources Clean the data by handling missing values and outliers Normalize or standardize features to ensure equal importance Split the data into training, validation, and test sets (typically 70-15-15 or 80-10-10) 2. Feature Engineering Select relevant features that contribute to predictions Create new features from existing ones to improve model performance Reduce dimensionality if necessary to prevent overfitting 3. Choose a Model Select an appropriate algorithm based on your problem and data Linear models (e.g., linear regression, logistic regression) for simpler problems Tree-based models (e.g., random forests, gradient boosting) for complex data with non-linear relationships Neural networks (e.g., transformers or CNNs) for more complex tasks. 4. Train the Model Feed the training data into the model and adjust its parameters to minimize errors. The process typically involves: ...