Data Cleaning And Exploration With Machine Learning Get To Grips With Machine Learning Techniques To Achieve Sparklingclean Data Quickly Walker by Walker, Michael; instant download after payment.

Explore supercharged machine learning techniques to take care of your data laundry loads
Key Features:
    Learn how to prepare data for machine learning processes
    Understand which algorithms are based on prediction objectives and the properties of the data
    Explore how to interpret and evaluate the results from machine learning
Book Description:
Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results.
As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You'll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you'll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You'll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book.
By the end of this book, you'll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.