Python Machine Learning Machine Learning and Deep Learning with Python scikit learn and TensorFlow 2nd edition by Sebastian Raschka, Vahid Mirjalili ISBN 1787125939 9781787125933

Python Machine Learning Machine Learning and Deep Learning with Python scikit learn and TensorFlow 2nd edition by Sebastian Raschka, Vahid Mirjalili ISBN 1787125939 9781787125933

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ISBN 10: 1787125939
ISBN 13: 9781787125933
Author: Sebastian Raschka, Vahid Mirjalili 

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.

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. Using Python's 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 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include 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 scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.

If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.

Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn and TensorFlow 2nd Table of contents:

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

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

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Organizing clusters as a hierarchical tree

Grouping clusters in bottom-up fashion

Performing hierarchical clustering on a distance matrix

Attaching dendrograms to a heat map

Applying agglomerative clustering via scikit-learn

Locating regions of high density via DBSCAN

Summary

12. Implementing a Multilayer Artificial Neural Network from Scratch

Modeling complex functions with artificial neural networks

Single-layer neural network recap

Introducing the multilayer neural network architecture

Activating a neural network via forward propagation

Classifying handwritten digits

Obtaining the MNIST dataset

Implementing a multilayer perceptron

Training an artificial neural network

Computing the logistic cost function

Developing your intuition for backpropagation

Training neural networks via backpropagation

About the convergence in neural networks

A few last words about the neural network implementation

Summary

13. Parallelizing Neural Network Training with TensorFlow

TensorFlow and training performance

What is TensorFlow?

How we will learn TensorFlow

First steps with TensorFlow

Working with array structures

Developing a simple model with the low-level TensorFlow API

Training neural networks efficiently with high-level TensorFlow APIs

Building multilayer neural networks using TensorFlow's Layers API

Developing a multilayer neural network with Keras

Choosing activation functions for multilayer networks

Logistic function recap

Estimating class probabilities in multiclass classification via the softmax function

Broadening the output spectrum using a hyperbolic tangent

Rectified linear unit activation

Summary

14. Going Deeper – The Mechanics of TensorFlow

Key features of TensorFlow

TensorFlow ranks and tensors

How to get the rank and shape of a tensor

Understanding TensorFlow's computation graphs

Placeholders in TensorFlow

Defining placeholders

Feeding placeholders with data

Defining placeholders for data arrays with varying batchsizes

Variables in TensorFlow

Defining variables

Initializing variables

Variable scope

Reusing variables

Building a regression model

Executing objects in a TensorFlow graph using their names

Saving and restoring a model in TensorFlow

Transforming Tensors as multidimensional data arrays

Utilizing control flow mechanics in building graphs

Visualizing the graph with TensorBoard

Extending your TensorBoard experience

Summary

15. Classifying Images with Deep Convolutional Neural Networks

Building blocks of convolutional neural networks

Understanding CNNs and learning feature hierarchies

Performing discrete convolutions

Performing a discrete convolution in one dimension

The effect of zero-padding in a convolution

Determining the size of the convolution output

Performing a discrete convolution in 2D

Subsampling

Putting everything together to build a CNN

Working with multiple input or color channels

Regularizing a neural network with dropout

Implementing a deep convolutional neural network using TensorFlow

The multilayer CNN architecture

Loading and preprocessing the data

Implementing a CNN in the TensorFlow low-level API

Implementing a CNN in the TensorFlow Layers API

Summary

16. Modeling Sequential Data Using Recurrent Neural Networks

Introducing sequential data

Modeling sequential data – order matters

Representing sequences

The different categories of sequence modeling

RNNs for modeling sequences

Understanding the structure and flow of an RNN

Computing activations in an RNN

The challenges of learning long-range interactions

LSTM units

Implementing a multilayer RNN for sequence modeling in TensorFlow

Project one – performing sentiment analysis of IMDb movie reviews using multilayer RNNs

Preparing the data

Embedding

Building an RNN model

The SentimentRNN class constructor

The build method

Step 1 – defining multilayer RNN cells

Step 2 – defining the initial states for the RNN cells

Step 3 – creating the RNN using the RNN cells and their states

The train method

The predict method

Instantiating the SentimentRNN class

Training and optimizing the sentiment analysis RNN model

Project two – implementing an RNN for character-level language modeling in TensorFlow

Preparing the data

Building a character-level RNN model

The constructor

The build method

The train method

The sample method

Creating and training the CharRNN Model

The CharRNN model in the sampling mode

Chapter and book summary

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Tags: Sebastian Raschka, Vahid Mirjalili, Python Machine, Deep Learning