This course covers important techniques in data preparation, data cleaning and feature selection that are needed to set your machine learning model up for success. You will also learn how to use imputation to deal with missing data and strategies for identifying and coping with outliers.
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to prepare the data going into the model in a manner appropriate to the problem we are trying to solve. In this course, Preparing Data for Machine Learning* you will gain the ability to explore, clean, and structure your data in ways that get the best out of your machine learning model. First, you will learn why data cleaning and data preparation are so important, and how missing data, outliers, and other data-related problems can be solved. Next, you will discover how models that read too much into data suffer from a problem called overfitting, in which models perform well under test conditions but struggle in live deployments. You will also understand how models that are trained with insufficient or unrepresentative data suffer from a different set of problems, and how these problems can be mitigated. Finally, you will round out your knowledge by applying different methods for feature selection, dealing with missing data using imputation, and building your models using the most relevant features. When you’re finished with this course, you will have the skills and knowledge to identify the right data procedures for data cleaning and data preparation to set your model up for success.
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to prepare the data going into the model in a manner appropriate to the problem we are trying to solve. In this course, Preparing Data for Machine Learning* you will gain the ability to explore, clean, and structure your data in ways that get the best out of your machine learning model. First, you will learn why data cleaning and data preparation are so important, and how missing data, outliers, and other data-related problems can be solved. Next, you will discover how models that read too much into data suffer from a problem called overfitting, in which models perform well under test conditions but struggle in live deployments. You will also understand how models that are trained with insufficient or unrepresentative data suffer from a different set of problems, and how these problems can be mitigated. Finally, you will round out your knowledge by applying different methods for feature selection, dealing with missing data using imputation, and building your models using the most relevant features. When you’re finished with this course, you will have the skills and knowledge to identify the right data procedures for data cleaning and data preparation to set your model up for success.
