Book Image

Python Machine Learning - Third Edition

By : Sebastian Raschka, Vahid Mirjalili
5 (1)
Book Image

Python Machine Learning - Third Edition

5 (1)
By: Sebastian Raschka, Vahid Mirjalili

Overview of this book

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

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Table of Contents (21 chapters)
Preface
Preface
Get started with machine learning
Who this book is for
What this book covers
What you need for this book
To get the most out of this book
Get in touch
Free Chapter
1
Giving Computers the Ability to Learn from Data
Giving Computers the Ability to Learn from Data
Building intelligent machines to transform data into knowledge
The three different types of machine learning
Introduction to the basic terminology and notations
A roadmap for building machine learning systems
Using Python for machine learning
Summary
2
Training Simple Machine Learning Algorithms for Classification
Training Simple Machine Learning Algorithms for Classification
Artificial neurons – a brief glimpse into the early history of machine learning
Implementing a perceptron learning algorithm in Python
Adaptive linear neurons and the convergence of learning
Summary
3
A Tour of Machine Learning Classifiers Using scikit-learn
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
Maximum margin classification with support vector machines
Solving nonlinear problems using a kernel SVM
Decision tree learning
K-nearest neighbors – a lazy learning algorithm
Summary
4
Building Good Training Datasets – Data Preprocessing
Building Good Training Datasets – Data Preprocessing
Dealing with missing data
Handling categorical data
Partitioning a dataset into separate training and test datasets
Bringing features onto the same scale
Selecting meaningful features
Assessing feature importance with random forests
Summary
5
Compressing Data via Dimensionality Reduction
Compressing Data via Dimensionality Reduction
Unsupervised dimensionality reduction via principal component analysis
Supervised data compression via linear discriminant analysis
Using kernel principal component analysis for nonlinear mappings
Summary
6
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Streamlining workflows with pipelines
Using k-fold cross-validation to assess model performance
Debugging algorithms with learning and validation curves
Fine-tuning machine learning models via grid search
Looking at different performance evaluation metrics
Summary
7
Combining Different Models for Ensemble Learning
Combining Different Models for Ensemble Learning
Learning with ensembles
Combining classifiers via majority vote
Bagging – building an ensemble of classifiers from bootstrap samples
Leveraging weak learners via adaptive boosting
Summary
8
Applying Machine Learning to Sentiment Analysis
Applying Machine Learning to Sentiment Analysis
Preparing the IMDb movie review data for text processing
Introducing the bag-of-words model
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
Summary
9
Embedding a Machine Learning Model into a Web Application
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
Turning the movie review classifier into a web application
Deploying the web application to a public server
Summary
10
Predicting Continuous Target Variables with Regression Analysis
Predicting Continuous Target Variables with Regression Analysis
Introducing linear regression
Exploring the Housing dataset
Implementing an ordinary least squares linear regression model
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
Dealing with nonlinear relationships using random forests
Summary
11
Working with Unlabeled Data – Clustering Analysis
Working with Unlabeled Data – Clustering Analysis
Grouping objects by similarity using k-means
Organizing clusters as a hierarchical tree
Locating regions of high density via DBSCAN
Summary
12
Implementing a Multilayer Artificial Neural Network from Scratch
Implementing a Multilayer Artificial Neural Network from Scratch
Modeling complex functions with artificial neural networks
Classifying handwritten digits
Training an artificial neural network
About the convergence in neural networks
A few last words about the neural network implementation
Summary
13
Parallelizing Neural Network Training with TensorFlow
Parallelizing Neural Network Training with TensorFlow
TensorFlow and training performance
First steps with TensorFlow
Building input pipelines using tf.data – the TensorFlow Dataset API
Building an NN model in TensorFlow
Choosing activation functions for multilayer neural networks
Summary
14
Going Deeper – The Mechanics of TensorFlow
Going Deeper – The Mechanics of TensorFlow
The key features of TensorFlow
TensorFlow's computation graphs: migrating to TensorFlow v2
TensorFlow Variable objects for storing and updating model parameters
Computing gradients via automatic differentiation and GradientTape
Simplifying implementations of common architectures via the Keras API
TensorFlow Estimators
Summary
15
Classifying Images with Deep Convolutional Neural Networks
Classifying Images with Deep Convolutional Neural Networks
The building blocks of CNNs
Putting everything together – implementing a CNN
Implementing a deep CNN using TensorFlow
Gender classification from face images using a CNN
Summary
16
Modeling Sequential Data Using Recurrent Neural Networks
Modeling Sequential Data Using Recurrent Neural Networks
Introducing sequential data
RNNs for modeling sequences
Implementing RNNs for sequence modeling in TensorFlow
Understanding language with the Transformer model
Summary
17
Generative Adversarial Networks for Synthesizing New Data
Generative Adversarial Networks for Synthesizing New Data
Introducing generative adversarial networks
Implementing a GAN from scratch
Improving the quality of synthesized images using a convolutional and Wasserstein GAN
Other GAN applications
Summary
18
Reinforcement Learning for Decision Making in Complex Environments
Reinforcement Learning for Decision Making in Complex Environments
Introduction – learning from experience
The theoretical foundations of RL
Reinforcement learning algorithms
Implementing our first RL algorithm
Chapter and book summary
19
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20
Index
Index

Locating regions of high density via DBSCAN

Although we can't cover the vast amount of different clustering algorithms in this chapter, let's at least include one more approach to clustering: density-based spatial clustering of applications with noise (DBSCAN), which does not make assumptions about spherical clusters like k-means, nor does it partition the dataset into hierarchies that require a manual cut-off point. As its name implies, density-based clustering assigns cluster labels based on dense regions of points. In DBSCAN, the notion of density is defined as the number of points within a specified radius, .

According to the DBSCAN algorithm, a special label is assigned to each example (data point) using the following criteria:

  • A point is considered a core point if at least a specified number (MinPts) of neighboring points fall within the specified radius, .
  • A border point is a point that has fewer neighbors than MinPts within ε, but lies within...
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