Author Description

Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. In this step-by-step guide you will learn: How to download free datasets What tools and machine learning libraries you need Data scrubbing techniques, including one-hot encoding, binning, and dealing with missing data Preparing data for analysis, including k-fold Validation Regression analysis to create trend lines Clustering, including k-means clustering, to find new relationships The basics of Neural Networks Bias/Variance to improve your machine learning model Decision Trees to decode classification How to build your first Machine Learning Model to predict house values using Python