Machine Learning: Data Scientist

Dig deep into the math, science, and statistics behind machine learning

This path is designed for learners skilled in math, statistics, and analysis who want become machine learning (ML) subject matter experts within their organization. Progress through fundamental, intermediate, and advanced courses to learn how machine learning frameworks and analysis tools can apply to your work and improve collaboration.

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Math for Machine Learning

To understand modern machine learning, you also need to understand vectors and matrices, linear algebra, probability theorems, univariate calculus, and multivariate calculus.

Linear and Logistic Regression

Explore models for regression, least squares error, maximum likelihood estimate, regularization, logistic regression, empirical loss minimization, and gradient-based optimization methods.

The Elements of Data Science

Learn to build and continuously improve machine learning models by covering cover problem formulation, exploratory data analysis, feature engineering, model training, tuning and debugging, as well as model evaluation and productionizing.

Data Science Capstone: Real World ML Decisions

Use machine learning to solve a real-life business challenges. Build, train, and test a machine learning model from the ground up.