Develop the intermediate machine learning skills that will take your data analyst or data scientist career to the next level. You’ll learn the mechanisms of advanced machine learning techniques step by step. By the end, you’ll be able to build complex models to predict the future, measure their performance, and then improve the models.
In this path, you’ll learn the basics of machine learning and its foundational mathematics, including linear and logistic regression, calculus, and linear algebra. You’ll also learn how to build a model from start to finish with techniques such as k-nearest neighbors, k-means clustering, decision trees, and neural networks — and how to avoid common pitfalls, how to evaluate the quality and accuracy of your model, and how to fine-tune it.
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.
- How to work with neural networks
- The basics of building a machine learning project
- How to build a machine learning model for your first Kaggle submission
- How to select the best algorithm and optimize your model
