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Avoid common machine learning pitfalls by understanding your data before building models and ensuring feature scaling for better algorithm performance.
Learn to build a functional to-do app using Django. This tutorial covers models, views, forms, and templates, providing a solid foundation for web development.
Learn how grid search automates hyperparameter tuning to systematically find the best model settings, boosting performance without manual guesswork.
Logistic regression predicts categorical outcomes like spam detection. This tutorial builds a binary classification model from scratch using Python and NumPy.
Linear regression is a simple machine learning tool that finds a best-fit straight line to make predictions based on observed data patterns.
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.
Random search offers a more efficient alternative to grid search for hyperparameter tuning in machine learning, especially in high-dimensional spaces, and can be implemented using Python.
XGBoost is a powerful machine learning algorithm known for winning competitions and delivering outstanding results. This tutorial explains what makes it special and how to use it.