Python Machine Learning (2nd Edition, Fully revised and updated, Packt Dec 2018 )
------ by Sebastian Raschka and Vahid Mirjalili


Table of Contents
Preface xi
Chapter 1: Giving Computers the Ability to Learn from Data 1
Building intelligent machines to transform data into knowledge 2
The three different types of machine learning 2
Making predictions about the future with supervised learning 3
Classification for predicting class labels 3
Regression for predicting continuous outcomes 5
Solving interactive problems with reinforcement learning 6
Discovering hidden structures with unsupervised learning 7
Finding subgroups with clustering 7
Dimensionality reduction for data compression 8
Introduction to the basic terminology and notations 8
A roadmap for building machine learning systems 11
Preprocessing – getting data into shape 12
Training and selecting a predictive model 12
Evaluating models and predicting unseen data instances 13
Using Python for machine learning 13
Installing Python and packages from the Python Package Index 14
Using the Anaconda Python distribution and package manager 14
Packages for scientific computing, data science, and machine learning 15
Summary 15
Chapter 2: Training Simple Machine Learning Algorithms
for Classification 17
Artificial neurons – a brief glimpse into the early history of
machine learning 18
The formal definition of an artificial neuron 19
The perceptron learning rule 21
Table of Contents
[ ii ]
Implementing a perceptron learning algorithm in Python 24
An object-oriented perceptron API 24
Training a perceptron model on the Iris dataset 28
Adaptive linear neurons and the convergence of learning 34
Minimizing cost functions with gradient descent 35
Implementing Adaline in Python 38
Improving gradient descent through feature scaling 42
Large-scale machine learning and stochastic gradient descent 44
Summary 50
Chapter 3: A Tour of Machine Learning Classifiers
Using scikit-learn 51
Choosing a classification algorithm 52
First steps with scikit-learn – training a perceptron 52
Modeling class probabilities via logistic regression 59
Logistic regression intuition and conditional probabilities 59
Learning the weights of the logistic cost function 63
Converting an Adaline implementation into an algorithm for
logistic regression 66
Training a logistic regression model with scikit-learn 71
Tackling overfitting via regularization 73
Maximum margin classification with support vector machines 76
Maximum margin intuition 77
Dealing with a nonlinearly separable case using slack variables 79
Alternative implementations in scikit-learn 81
Solving nonlinear problems using a kernel SVM 82
Kernel methods for linearly inseparable data 82
Using the kernel trick to find separating hyperplanes in
high-dimensional space 84
Decision tree learning 88
Maximizing information gain – getting the most bang for your buck 90
Building a decision tree 95
Combining multiple decision trees via random forests 98
K-nearest neighbors – a lazy learning algorithm 101
Summary 105
..................................................................................................................
Chapter 15: Classifying Images with Deep Convolutional
Neural Networks 493
Building blocks of convolutional neural networks 494
Understanding CNNs and learning feature hierarchies 494
Performing discrete convolutions 496
Performing a discrete convolution in one dimension 496
The effect of zero-padding in a convolution 499
Determining the size of the convolution output 501
Performing a discrete convolution in 2D 502
Subsampling 506
Putting everything together to build a CNN 508
Working with multiple input or color channels 508
Regularizing a neural network with dropout 512
Implementing a deep convolutional neural network
using TensorFlow 514
The multilayer CNN architecture 514
Loading and preprocessing the data 516
Implementing a CNN in the TensorFlow low-level API 517
Implementing a CNN in the TensorFlow Layers API 530
Summary 536
Chapter 16: Modeling Sequential Data Using Recurrent
Neural Networks 537
Introducing sequential data 538
Modeling sequential data – order matters 538
Representing sequences 539
The different categories of sequence modeling 540
RNNs for modeling sequences 541
Understanding the structure and flow of an RNN 541
Computing activations in an RNN 543
The challenges of learning long-range interactions 546
LSTM units 548
Table of Contents
[ ix ]
Implementing a multilayer RNN for sequence modeling in
TensorFlow 550
Project one – performing sentiment analysis of IMDb movie
reviews using multilayer RNNs 551
Preparing the data 552
Embedding 556
Building an RNN model 558
The SentimentRNN class constructor 559
The build method 560
Step 1 – defining multilayer RNN cells 562
Step 2 – defining the initial states for the RNN cells 562
Step 3 – creating the RNN using the RNN cells and their states 563
The train method 563
The predict method 565
Instantiating the SentimentRNN class 565
Training and optimizing the sentiment analysis RNN model 566
Project two – implementing an RNN for character-level
language modeling in TensorFlow 567
Preparing the data 568
Building a character-level RNN model 572
The constructor 573
The build method 574
The train method 576
The sample method 578
Creating and training the CharRNN Model 579
The CharRNN model in the sampling mode 580
Chapter and book summary 580
Index 583