Data Science - уроки и проекты

📊 24 Mathematical Concepts Every Data Scientist Should Understand Data Science isn’t just Python and ML libraries. A strong math foundation helps you understand how models learn, optimize, and make predictions. 🔹 Statistics & Probability • Normal Distribution • Z-Score • Correlation • Entropy • Naive Bayes • MLE 🔹 ML Fundamentals • Gradient Descent • Regression • Sigmoid • ReLU • Softmax • SVM • F1 Score • MSE • Log Loss • R² 🔹 Linear Algebra • Eigenvectors • SVD • Cosine Similarity • PCA 🔹 Optimization & Information Theory • Lagrange Multipliers • KL Divergence • OLS 💡 Don’t just memorize formulas. Understand: What? → Why? → Where? → What problem does it solve? 📌 Save this as your quick Mathematics reference for Data Science.