Python: Deeper Insights into Machine Learning

Leverage benefits of machine learning techniques using Python

Python: Deeper Insights into Machine Learning

Sebastian Raschka, David Julian, John Hearty

10 customer reviews
Leverage benefits of machine learning techniques using Python
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Book Details

ISBN 139781787128576
Paperback901 pages

Book Description

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.

The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems.

The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems.

At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.

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
An introduction to the basic terminology and notations
A roadmap for building machine learning systems
Using Python for machine learning
Summary
Chapter 2: Training 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
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 in 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
Summary
Chapter 7: Combining Different Models for Ensemble Learning
Learning with ensembles
Implementing a simple majority vote classifier
Evaluating and tuning the ensemble classifier
Bagging – building an ensemble of classifiers from bootstrap samples
Leveraging weak learners via adaptive boosting
Summary
Chapter 8: Applying Machine Learning to Sentiment Analysis
Obtaining the IMDb movie review dataset
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
Summary
Chapter 9: Embedding a Machine Learning Model into a Web Application
Serializing fitted scikit-learn estimators
Setting up a SQLite database for data storage
Developing a web application with Flask
Turning the movie classifier into a web application
Deploying the web application to a public server
Summary
Chapter 10: Predicting Continuous Target Variables with Regression Analysis
Introducing a simple linear regression model
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
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: Training Artificial Neural Networks for Image Recognition
Modeling complex functions with artificial neural networks
Classifying handwritten digits
Training an artificial neural network
Developing your intuition for backpropagation
Debugging neural networks with gradient checking
Convergence in neural networks
Other neural network architectures
A few last words about neural network implementation
Summary
Chapter 13: Parallelizing Neural Network Training with Theano
Building, compiling, and running expressions with Theano
Choosing activation functions for feedforward neural networks
Training neural networks efficiently using Keras
Summary
Chapter 14: Thinking in Machine Learning
The human interface
Design principles
Summary
Chapter 15: Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Matplotlib
Pandas
SciPy
Scikit-learn
Summary
Chapter 16: Turning Data into Information
What is data?
Big data
Signals
Cleaning data
Visualizing data
Summary
Chapter 17: Models – Learning from Information
Logical models
Tree models
Rule models
Summary
Chapter 18: Linear Models
Introducing least squares
Logistic regression
Multiclass classification
Regularization
Summary
Chapter 19: Neural Networks
Getting started with neural networks
Logistic units
Cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Summary
Chapter 20: Features – How Algorithms See the World
Feature types
Operations and statistics
Structured features
Transforming features
Principle component analysis
Summary
Chapter 21: Learning with Ensembles
Ensemble types
Bagging
Boosting
Ensemble strategies
Summary
Chapter 22: Design Strategies and Case Studies
Evaluating model performance
Model selection
Learning curves
Real-world case studies
Machine learning at a glance
Summary
Chapter 23: Unsupervised Machine Learning
Principal component analysis
Introducing k-means clustering
Self-organizing maps
Further reading
Summary
Chapter 24: Deep Belief Networks
Neural networks – a primer
Restricted Boltzmann Machine
Deep belief networks
Further reading
Summary
Chapter 25: Stacked Denoising Autoencoders
Autoencoders
Stacked Denoising Autoencoders
Further reading
Summary
Chapter 26: Convolutional Neural Networks
Introducing the CNN
Further Reading
Summary
Chapter 27: Semi-Supervised Learning
Introduction
Understanding semi-supervised learning
Semi-supervised algorithms in action
Further reading
Summary
Chapter 28: Text Feature Engineering
Introduction
Text feature engineering
Further reading
Summary
Chapter 29: Feature Engineering Part II
Introduction
Creating a feature set
Feature engineering in practice
Further reading
Summary
Chapter 30: Ensemble Methods
Introducing ensembles
Using models in dynamic applications
Further reading
Summary
Chapter 31: Additional Python Machine Learning Tools
Alternative development tools
Further reading
Summary
Chapter 32: Chapter Code Requirements

What You Will Learn

  • Learn to write clean and elegant Python code that will optimize the strength of your algorithms
  • Uncover hidden patterns and structures in data with clustering
  • Improve accuracy and consistency of results using powerful feature engineering techniques
  • Gain practical and theoretical understanding of cutting-edge deep learning algorithms
  • Solve unique tasks by building models
  • Get grips on the machine learning design process

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
An introduction to the basic terminology and notations
A roadmap for building machine learning systems
Using Python for machine learning
Summary
Chapter 2: Training 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
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 in 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
Summary
Chapter 7: Combining Different Models for Ensemble Learning
Learning with ensembles
Implementing a simple majority vote classifier
Evaluating and tuning the ensemble classifier
Bagging – building an ensemble of classifiers from bootstrap samples
Leveraging weak learners via adaptive boosting
Summary
Chapter 8: Applying Machine Learning to Sentiment Analysis
Obtaining the IMDb movie review dataset
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
Summary
Chapter 9: Embedding a Machine Learning Model into a Web Application
Serializing fitted scikit-learn estimators
Setting up a SQLite database for data storage
Developing a web application with Flask
Turning the movie classifier into a web application
Deploying the web application to a public server
Summary
Chapter 10: Predicting Continuous Target Variables with Regression Analysis
Introducing a simple linear regression model
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
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: Training Artificial Neural Networks for Image Recognition
Modeling complex functions with artificial neural networks
Classifying handwritten digits
Training an artificial neural network
Developing your intuition for backpropagation
Debugging neural networks with gradient checking
Convergence in neural networks
Other neural network architectures
A few last words about neural network implementation
Summary
Chapter 13: Parallelizing Neural Network Training with Theano
Building, compiling, and running expressions with Theano
Choosing activation functions for feedforward neural networks
Training neural networks efficiently using Keras
Summary
Chapter 14: Thinking in Machine Learning
The human interface
Design principles
Summary
Chapter 15: Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Matplotlib
Pandas
SciPy
Scikit-learn
Summary
Chapter 16: Turning Data into Information
What is data?
Big data
Signals
Cleaning data
Visualizing data
Summary
Chapter 17: Models – Learning from Information
Logical models
Tree models
Rule models
Summary
Chapter 18: Linear Models
Introducing least squares
Logistic regression
Multiclass classification
Regularization
Summary
Chapter 19: Neural Networks
Getting started with neural networks
Logistic units
Cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Summary
Chapter 20: Features – How Algorithms See the World
Feature types
Operations and statistics
Structured features
Transforming features
Principle component analysis
Summary
Chapter 21: Learning with Ensembles
Ensemble types
Bagging
Boosting
Ensemble strategies
Summary
Chapter 22: Design Strategies and Case Studies
Evaluating model performance
Model selection
Learning curves
Real-world case studies
Machine learning at a glance
Summary
Chapter 23: Unsupervised Machine Learning
Principal component analysis
Introducing k-means clustering
Self-organizing maps
Further reading
Summary
Chapter 24: Deep Belief Networks
Neural networks – a primer
Restricted Boltzmann Machine
Deep belief networks
Further reading
Summary
Chapter 25: Stacked Denoising Autoencoders
Autoencoders
Stacked Denoising Autoencoders
Further reading
Summary
Chapter 26: Convolutional Neural Networks
Introducing the CNN
Further Reading
Summary
Chapter 27: Semi-Supervised Learning
Introduction
Understanding semi-supervised learning
Semi-supervised algorithms in action
Further reading
Summary
Chapter 28: Text Feature Engineering
Introduction
Text feature engineering
Further reading
Summary
Chapter 29: Feature Engineering Part II
Introduction
Creating a feature set
Feature engineering in practice
Further reading
Summary
Chapter 30: Ensemble Methods
Introducing ensembles
Using models in dynamic applications
Further reading
Summary
Chapter 31: Additional Python Machine Learning Tools
Alternative development tools
Further reading
Summary
Chapter 32: Chapter Code Requirements

Book Details

ISBN 139781787128576
Paperback901 pages
Read More
From 10 reviews

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