num_pages: 102
description:  This starter course is for anyone curious about machine learning but who are not sure where to begin. If you have ever found yourself lost halfway through other introductory materials on this topic this is the book for you. If you don't understand set terminology such as vectors hyperplanes and centroids then this is also the book for you. This starter course isn't a picture story book but does include many visual examples that break algorithms down into a digestible and practical format. As a starter course this book connects the dots and offers the crash course I wish I had when I first started. The kind of guide I wish had before I started taking on introductory courses that presume youre two days away from an advanced mathematics exam. Thats why this introductory course doesnt go further on the subject than other introductory books but rather goes a step back. A half-step back in order to help everyone make his or her first strides in machine learning and is an ideal study companion for the visual learner. In this step-by-step guide you will learn: - How to download free datasets - What tools and software packages 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 and k-nearest Neighbors - Naive Bayes Classifier to predict new classes - Anomaly detection and SVM algorithms to combat anomalies and outliers - The basics of Neural Networks - Bias/Variance to improve your machine learning model - Decision Trees to decode classification Please feel welcome to join this starter course by buying a copy or sending a free sample to your preferred device. 
language: None
title: Machine Learning: A Visual Starter Course For Beginner's
publisher: None