Book Image

Statistics for Machine Learning

By : Pratap Dangeti
Book Image

Statistics for Machine Learning

By: Pratap Dangeti

Overview of this book

Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more. By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.
Table of Contents (16 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

ANN classifier applied on handwritten digits using scikit-learn


An ANN classifier example has been illustrated with the handwritten digits example from the scikit-learn datasets, in which handwritten digits are created from 0 to 9 and their respective 64 features (8 x 8 matrix) of pixel intensities between 0 and 255, as any black and white (or grayscale) image can be represented. In the case of color images, RGB (red, green, and blue) channels will be used to represent all the colors:

# Neural Networks - Classifying hand-written digits  
>>> import pandas as pd 
>>> from sklearn.datasets import load_digits 
>>> from sklearn.cross_validation import train_test_split 
>>> from sklearn.pipeline import Pipeline 
>>> from sklearn.preprocessing import StandardScaler 
 
>>> from sklearn.neural_network import MLPClassifier 
>>> digits = load_digits() 
>>> X = digits.data 
>>> y = digits.target 

# Checking dimensions...