Get the foundational machine learning skills you need to grow your career as a data analyst or data scientist. You’ll learn how to extract, prepare, analyze and visualize data with Python — and how to build basic models. By the end, you’ll be able to make predictions using statistics and machine learning.
In this path, you’ll learn the fundamentals of Python so you can prepare data and clean and correct errors. You’ll also learn to master various components and techniques of machine learning, like calculus, linear algebra, linear regression, k-nearest neighbors, k-means clustering, and decision trees.
Best of all, you’ll learn by doing — you’ll write code and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
- Machine learning basics
- Avoiding common mistakes
- Evaluating model performance
- Common techniques like k-nearest neighbors, k-means clustering, and decision trees
- Mathematics for machine learning, including calculus and linear algebra
- Basics of linear and logistic regression
