Welcome to the AI Toolkit's Machine Learning Models section. Here, you'll find an array of models designed to assist with various machine learning tasks. Whether you're new to machine learning or an experienced practitioner, our collection aims to provide you with the tools you need.
Available Models
Classification Models: These models are used for predicting categorical outcomes. They include algorithms like Logistic Regression, Support Vector Machines, and Random Forests.
Regression Models: Ideal for predicting continuous outcomes. You'll find models like Linear Regression, Ridge, and Lasso Regression here.
Clustering Models: Use these models for unsupervised learning to group data points into clusters. Examples include K-Means, Hierarchical Clustering, and DBSCAN.
How to Use
To get started, you can import the models from our AI Toolkit library. Here's an example of how to use the Logistic Regression model:
from ai_toolkit.en.models.ml import LogisticRegression
# Create a Logistic Regression model
model = LogisticRegression()
# Fit the model with your data
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
More Resources
If you're looking to dive deeper into machine learning, consider visiting our Machine Learning Guide. It's packed with tutorials and best practices.
And don't forget to check out our API Documentation for more details on how to integrate our models into your projects.
Visual Insights
Machine learning is not just about algorithms; it's about understanding the data. Here's a visual representation of a machine learning workflow: