Mathemathics For Machine Learning

Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning.

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine.

Master Machine Learning by getting your hands dirty on Real Life Case studies. Be A Kaggle and Industry Grandmaster.

The Third Course in a Series for Mastering Python for Machine Learning Engineers

Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA etc

Much of machine learning is built around the idea of loss functions and optimizing for them. To understand them, you first need to understand Calculus.