Dot products, transposition, and inversion.
Powering Dimensionality Reduction (PCA).
SVD (Singular Value Decomposition) for compression. 📈 Calculus Calculus optimizes the models we build. Differentiation: Calculating slopes to find minima.
💡 : You don't need to be a mathematician, but you must understand how these concepts influence your model's accuracy.
Normal, Binomial, and Poisson patterns in data. Bayes’ Theorem: Updating beliefs based on new evidence.
Mathematical Foundations of Data Science Using Python focuses on the core principles that drive machine learning algorithms . It bridges the gap between theoretical math and practical implementation. 🔢 Linear Algebra Linear algebra is the language of data. Representing datasets and features.
Determining if results are statistically significant.