This course is intended to show the student advanced regression techniques that he can use to improve his score on Kaggle's House Price competition.

He will be given an overview of Python's machine learning library, sklearn, which contains many machine learning functions. In addition, he will learn the code for several of those functions, which will help him to get closer to the machine.

The student will study several different regressors, which will be needed to make accurate and low error predictions on the dataset.

The student will learn several different techniques to impute null values in the dataframe, which he will need to be able to do to make predictions on it later.

The student will learn how to scale the data by using standard formulas or using functions in the sklearn library.

The student will learn techniques to detect and remove outliers from the training dataset.

The student will learn about the various feature selection techniques to use to reduce the number of selections the model will predict on.

The student will learn about the various models in sklearn that he can use to make predictions on the datasets.

The student will learn how to use sklearn's GridSearchCV, which will enable him to optimally tune parameters in the models he has selected to make predictions on the dataset.

The student will learn about the various ensemble methods that are present in the sklearn library, and will choose one of those methods in an attempt to improve the accuracy and reduce the error of his predictions.

Finally, the student will be invited to enter Kaggle's House Price competition and use all of the techniques he has learned about in the course to achieve a high score.