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Conda manages both Python and non-Python dependencies, making it ideal for data science, scientific computing, and machine learning projects. It simplifies library installation and environment management.
Get PyTorch installed for AI development with this guide covering system requirements, different installation methods, and setup tips.
Scikit-learn is a key Python machine learning library. Learn the best ways to install it for your system to start building models efficiently.
Linear regression is a simple machine learning tool that finds a best-fit straight line to make predictions based on observed data patterns.
Logistic regression is a classification algorithm used to predict binary outcomes. It's simple, interpretable, and widely applied for tasks like spam detection and medical diagnosis.
Learn how to deploy machine learning models using FastAPI, a high-performance Python framework. Ideal for web, mobile, or programmatic access with simplicity and speed.
Learn to deploy machine learning models using Flask, from environment setup to making API predictions, making AI integration simple and accessible.
Object detection enables computers to identify and locate multiple objects in images, essential for applications like surveillance, autonomous vehicles, and more.
Overfitting occurs when a model learns training data too well, including noise, making it perform poorly on new data. Underfitting happens when a model fails to capture patterns in the data, resulting in poor performance overall.
Pandas and NumPy are essential Python libraries for data manipulation in machine learning. They provide the foundation for preparing and handling data before model building.