Python Machine Learning - Second Edition

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.
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Python Machine Learning - Second Edition

Sebastian Raschka, Vahid Mirjalili

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Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.
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Book Details

ISBN 139781787125933
Paperback622 pages

Book Description

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 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 in today’s world.

If you’ve read the first edition of this book, you’ll be delighted to find a new 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 more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.

Table of Contents

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
Introduction to the basic terminology and notations
A roadmap for building machine learning systems
Using Python for machine learning
Summary
Chapter 2: 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
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
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
Chapter 4: Building Good Training Sets – Data Preprocessing
Dealing with missing data
Handling categorical data
Partitioning a dataset into separate training and test sets
Bringing features onto the same scale
Selecting meaningful features
Assessing feature importance with random forests
Summary
Chapter 5: 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
Chapter 6: 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
Dealing with class imbalance
Summary
Chapter 7: 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
Chapter 8: 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
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
Turning the movie review classifier into a web application
Deploying the web application to a public server
Summary
Chapter 10: 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
Chapter 11: 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
Chapter 12: 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
Chapter 13: Parallelizing Neural Network Training with TensorFlow
TensorFlow and training performance
Training neural networks efficiently with high-level TensorFlow APIs
Choosing activation functions for multilayer networks
Summary
Chapter 14: Going Deeper – The Mechanics of TensorFlow
Key features of TensorFlow
TensorFlow ranks and tensors
Understanding TensorFlow's computation graphs
Placeholders in TensorFlow
Variables in TensorFlow
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
Summary
Chapter 15: Classifying Images with Deep Convolutional Neural Networks
Building blocks of convolutional neural networks
Putting everything together to build a CNN
Implementing a deep convolutional neural network using TensorFlow
Summary
Chapter 16: Modeling Sequential Data Using Recurrent Neural Networks
Introducing sequential data
RNNs for modeling sequences
Implementing a multilayer RNN for sequence modeling in TensorFlow
Project one – performing sentiment analysis of IMDb movie reviews using multilayer RNNs
Project two – implementing an RNN for character-level language modeling in TensorFlow
Chapter and book summary

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
  • Master machine learning techniques using challenging real-world data
  • Master deep neural network implementation using the TensorFlow library
  • Ask new questions of your data through machine learning models and neural networks
  • 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

Authors

Table of Contents

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
Introduction to the basic terminology and notations
A roadmap for building machine learning systems
Using Python for machine learning
Summary
Chapter 2: 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
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
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
Chapter 4: Building Good Training Sets – Data Preprocessing
Dealing with missing data
Handling categorical data
Partitioning a dataset into separate training and test sets
Bringing features onto the same scale
Selecting meaningful features
Assessing feature importance with random forests
Summary
Chapter 5: 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
Chapter 6: 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
Dealing with class imbalance
Summary
Chapter 7: 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
Chapter 8: 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
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
Turning the movie review classifier into a web application
Deploying the web application to a public server
Summary
Chapter 10: 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
Chapter 11: 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
Chapter 12: 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
Chapter 13: Parallelizing Neural Network Training with TensorFlow
TensorFlow and training performance
Training neural networks efficiently with high-level TensorFlow APIs
Choosing activation functions for multilayer networks
Summary
Chapter 14: Going Deeper – The Mechanics of TensorFlow
Key features of TensorFlow
TensorFlow ranks and tensors
Understanding TensorFlow's computation graphs
Placeholders in TensorFlow
Variables in TensorFlow
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
Summary
Chapter 15: Classifying Images with Deep Convolutional Neural Networks
Building blocks of convolutional neural networks
Putting everything together to build a CNN
Implementing a deep convolutional neural network using TensorFlow
Summary
Chapter 16: Modeling Sequential Data Using Recurrent Neural Networks
Introducing sequential data
RNNs for modeling sequences
Implementing a multilayer RNN for sequence modeling in TensorFlow
Project one – performing sentiment analysis of IMDb movie reviews using multilayer RNNs
Project two – implementing an RNN for character-level language modeling in TensorFlow
Chapter and book summary

Book Details

ISBN 139781787125933
Paperback622 pages
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