Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow / Sebastian Raschka, Vahid Mirajalili.

Format:

Book

Author/Creator:

Raschka, Sebastian, author.

Mirajalili, Vahid, author.

Series:

Expert Insight

Language:

English

Subjects (All):

Python (Computer program language).

Machine learning.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

Second edition, fully revised and updated.

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Biography/History:

Raschka Sebastian: Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence. Mirjalili Vahid: Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph. D. degree in both Mechanical Engineering and Computer Science from Michigan State University.

Summary:

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from s...

Contents:

Cover

Copyright

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Packt is Searching for Authors Like You

Table of Contents

Preface

Chapter 1: Giving Computers the Ability to Learn from Data

Building intelligent machines to transform data into knowledge

The three different types of machine learning

Making predictions about the future with supervised learning

Classification for predicting class labels

Regression for predicting continuous outcomes

Solving interactive problems with reinforcement learning

Discovering hidden structures with unsupervised learning

Finding subgroups with clustering

Dimensionality reduction for data compression

Introduction to the basic terminology and notations

A roadmap for building machine learning systems

Preprocessing - getting data into shape

Training and selecting a predictive model

Evaluating models and predicting unseen data instances

Using Python for machine learning

Installing Python and packages from the Python Package Index

Using the Anaconda Python distribution and package manager

Packages for scientific computing, data science, and machine learning

Summary

Chapter 2: Training Simple Machine Learning Algorithms for Classification

Artificial neurons - a brief glimpse into the early history of machine learning

The formal definition of an artificial neuron

The perceptron learning rule

Implementing a perceptron learning algorithm in Python

An object-oriented perceptron API

Training a perceptron model on the Iris dataset

Adaptive linear neurons and the convergence of learning

Minimizing cost functions with gradient descent

Implementing Adaline in Python

Improving gradient descent through feature scaling

Large-scale machine learning and stochastic gradient descent

Summary.

Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn

Choosing a classification algorithm

First steps with scikit-learn - training a perceptron

Modeling class probabilities via logistic regression

Logistic regression intuition and conditional probabilities

Learning the weights of the logistic cost function

Converting an Adaline implementation into an algorithm for logistic regression

Training a logistic regression model with scikit-learn

Tackling overfitting via regularization

Maximum margin classification with support vector machines

Maximum margin intuition

Dealing with a nonlinearly separable case using slack variables

Alternative implementations in scikit-learn

Solving nonlinear problems using a kernel SVM

Kernel methods for linearly inseparable data

Using the kernel trick to find separating hyperplanes in high-dimensional space

Decision tree learning

Maximizing information gain - getting the most bang for your buck

Building a decision tree

Combining multiple decision trees via random forests

K-nearest neighbors - a lazy learning algorithm

Chapter 4: Building Good Training Sets - Data Preprocessing

Dealing with missing data

Identifying missing values in tabular data

Eliminating samples or features with missing values

Imputing missing values

Understanding the scikit-learn estimator API

Handling categorical data

Nominal and ordinal features

Creating an example dataset

Mapping ordinal features

Encoding class labels

Performing one-hot encoding on nominal features

Partitioning a dataset into separate training and test sets

Bringing features onto the same scale

Selecting meaningful features

L1 and L2 regularization as penalties against model complexity

A geometric interpretation of L2 regularization.

Sparse solutions with L1 regularization

Sequential feature selection algorithms

Assessing feature importance with random forests

Chapter 5: Compressing Data via Dimensionality Reduction

Unsupervised dimensionality reduction via principal component analysis

The main steps behind principal component analysis

Extracting the principal components step by step

Total and explained variance

Feature transformation

Principal component analysis in scikit-learn

Supervised data compression via linear discriminant analysis

Principal component analysis versus linear discriminant analysis

The inner workings of linear discriminant analysis

Computing the scatter matrices

Selecting linear discriminants for the new feature subspace

Projecting samples onto the new feature space

LDA via scikit-learn

Using kernel principal component analysis for nonlinear mappings

Kernel functions and the kernel trick

Implementing a kernel principal component analysis in Python

Example 1 - separating half-moon shapes

Example 2 - separating concentric circles

Projecting new data points

Kernel principal component analysis in scikit-learn

Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Streamlining workflows with pipelines

Loading the Breast Cancer Wisconsin dataset

Combining transformers and estimators in a pipeline

Using k-fold cross-validation to assess model performance

The holdout method

K-fold cross-validation

Debugging algorithms with learning and validation curves

Diagnosing bias and variance problems with learning curves

Addressing over- and underfitting with validation curves

Fine-tuning machine learning models via grid search

Tuning hyperparameters via grid search

Algorithm selection with nested cross-validation.

Looking at different performance evaluation metrics

Reading a confusion matrix

Optimizing the precision and recall of a classification model

Plotting a receiver operating characteristic

Scoring metrics for multiclass classification

Dealing with class imbalance

Chapter 7: Combining Different Models for Ensemble Learning

Learning with ensembles

Combining classifiers via majority vote

Implementing a simple majority vote classifier

Using the majority voting principle to make predictions

Evaluating and tuning the ensemble classifier

Bagging - building an ensemble of classifiers from bootstrap samples

Bagging in a nutshell

Applying bagging to classify samples in the Wine dataset

Leveraging weak learners via adaptive boosting

How boosting works

Applying AdaBoost using scikit-learn

Chapter 8: Applying Machine Learning to Sentiment Analysis

Preparing the IMDb movie review data for text processing

Obtaining the movie review dataset

Preprocessing the movie dataset into more convenient format

Introducing the bag-of-words model

Transforming words into feature vectors

Assessing word relevancy via term frequency-inverse document frequency

Cleaning text data

Processing documents into tokens

Training a logistic regression model for document classification

Working with bigger data - online algorithms and out-of-core learning

Topic modeling with Latent Dirichlet Allocation

Decomposing text documents with LDA

LDA with scikit-learn

Chapter 9: Embedding a Machine Learning Model into a Web Application

Serializing fitted scikit-learn estimators

Setting up an SQLite database for data storage

Developing a web application with Flask

Our first Flask web application

Form validation and rendering

Setting up the directory structure.

Implementing a macro using the Jinja2 templating engine

Adding style via CSS

Creating the result page

Turning the movie review classifier into a web application

Files and folders - looking at the directory tree

Implementing the main application as app.py

Setting up the review form

Creating a results page template

Deploying the web application to a public server

Creating a PythonAnywhere account

Uploading the movie classifier application

Updating the movie classifier

Chapter 10: Predicting Continuous Target Variables with Regression Analysis

Introducing linear regression

Simple linear regression

Multiple linear regression

Exploring the Housing dataset

Loading the Housing dataset into a data frame

Visualizing the important characteristics of a dataset

Looking at relationships using a correlation matrix

Implementing an ordinary least squares linear regression model

Solving regression for regression parameters with gradient descent

Estimating coefficient of a regression model via scikit-learn

Fitting a robust regression model using RANSAC

Evaluating the performance of linear regression models

Using regularized methods for regression

Turning a linear regression model into a curve - polynomial regression

Adding polynomial terms using scikit-learn

Modeling nonlinear relationships in the Housing dataset

Dealing with nonlinear relationships using random forests

Decision tree regression

Random forest regression

Chapter 11: Working with Unlabeled Data - Clustering Analysis

Grouping objects by similarity using k-means

K-means clustering using scikit-learn

A smarter way of placing the initial cluster centroids using k-means++

Hard versus soft clustering

Using the elbow method to find the optimal number of clusters.

Quantifying the quality of clustering via silhouette plots.

Notes:

Previous edition published: 2015.

Includes index.

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (ebrary, viewed October 18, 2017).

OCLC:

1006894361