Book Overview & Buying
Table Of Contents
Feedback & Rating

Mastering Machine Learning with scikit-learn

By : Gavin Hackeling

5 (2)

Buy this Book

Mastering Machine Learning with scikit-learn

5 (2)

By: Gavin Hackeling

Buy this Book

Overview of this book

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

The Fundamentals of Machine Learning

Defining machine learning

Learning from experience

Machine learning tasks

Training data, testing data, and validation data

Bias and variance

An introduction to scikit-learn

Installing scikit-learn

Installing pandas, Pillow, NLTK, and matplotlib

Summary

Simple Linear Regression

Simple linear regression

Evaluating the model

Summary

Classification and Regression with k-Nearest Neighbors

K-Nearest Neighbors

Lazy learning and non-parametric models

Classification with KNN

Regression with KNN

Summary

Feature Extraction

Extracting features from categorical variables

Standardizing features

Extracting features from text

Extracting features from images

Summary

From Simple Linear Regression to Multiple Linear Regression

Multiple linear regression

Polynomial regression

Regularization

Applying linear regression

Gradient descent

Summary

From Linear Regression to Logistic Regression

Binary classification with logistic regression

Spam filtering

Tuning models with grid search

Multi-class classification

Multi-label classification and problem transformation

Summary

Naive Bayes

Bayes' theorem

Generative and discriminative models

Naive Bayes

Naive Bayes with scikit-learn

Summary

Nonlinear Classification and Regression with Decision Trees

Decision trees

Training decision trees

Decision trees with scikit-learn

Summary

From Decision Trees to Random Forests and Other Ensemble Methods

Bagging

Boosting

Stacking

Summary

The Perceptron

The perceptron

Limitations of the perceptron

Summary

From the Perceptron to Support Vector Machines

Kernels and the kernel trick

Maximum margin classification and support vectors

Classifying characters in scikit-learn

Summary

From the Perceptron to Artificial Neural Networks

Nonlinear decision boundaries

Feed-forward and feedback ANNs

Multi-layer perceptrons

Training multi-layer perceptrons

Summary

K-means

Clustering

K-means

Evaluating clusters

Image quantization

Clustering to learn features

Summary

Dimensionality Reduction with Principal Component Analysis

Principal component analysis

Visualizing high-dimensional data with PCA

Face recognition with PCA

Summary

Customer Reviews

5 (2)

5 star

100%

4 star

3 star

2 star

1 star

Face recognition with PCA

Now let's apply PCA to a face recognition problem. Face recognition is the supervised classification task of identifying a person from an image of his or her face. In this example, we will use a dataset called Our Database of Faces from AT&T Laboratories Cambridge. The dataset contains 10 images of each of 40 people. The images were created under different lighting conditions, and the subjects varied their facial expressions. The images are grayscale and in pixels. The following is an example image:

While these images are small, a feature vector that encodes the intensity of every pixel will have 10,304 dimensions. Training from such high-dimensional data could require many samples to avoid overfitting. Instead, we will use PCA to compactly represent the images in terms of a small number of principal components. We can reshape the matrix of...