In our Machine Learning Fundamentals course, you will learn about the basics of machine learning. We’ll cover concepts such as K-Nearest Neighbors (KNN) Algorithms and learn about error metrics such as the Mean Squared Error and the Root Mean Squared Error. You'll also learn about hyperparameter optimization, a technique used to optimize machine learning algorithms to boost the accuracy and performance of trained models. Then you’ll dig into some k-fold cross-validation to perform more rigorous testing for your model.
Throughout this machine learning course, you won’t just learn how to use these models, you’ll also build an understanding of what is happening in the model training process. You'll get an introduction to sci-kit learn, which is an open-source machine learning library for the Python programming language. Scikit learn supports many of the models and validation metrics you will learn about in this course.
As you learn these new skills, you’ll be working with AirBnB prices data from Washington D.C. to predict the optimal price for becoming generating profit from a D.C. home rental.
At the end of the course, you'll complete a portfolio project in which you will use the K-Nearest Neighbors algorithm to predict car prices. This project is a chance for you to combine the skills you learned in this course and practice the machine learning workflow. This project also serves as a portfolio project that you can showcase to your future employer.
By the end of this course, you'll be able to:
Learn Machine Learning Fundamentals
Introduction to K-Nearest Neighbors
Learn the basics of machine learning to suggest optimal AirBnB list prices.
Evaluating Model Performance
Learn how to test models using error metrics and simple validation.
Multivariate K-Nearest Neighbors
Improve your predictions by using more features.
Vary the k value to improve performance.
Learn how to use k-fold cross validation to perform more rigorous testing.
Predicting Car Prices
Practice the machine learning workflow using k-nearest neighbors to predict car prices.