Python: Deeper Insights into Machine Learning

Name: Python: Deeper Insights into Machine Learning | Deeper Insights into Machine Learning
Brand: Packt Publishing
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Deeper Insights into Machine Learning

Sebastian Raschka David Julian John Hearty(Author)

Packt Publishing

Published on 15. April 2025

901 pages

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978-1-78712-854-5 (ISBN)

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Hearty John :
John Hearty is a Manager of Data Science team with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics. Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences. Eventually, John struck out on his own as a consultant offering a comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favorite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network. After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfill a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendationRaschka Sebastian :

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.Julian David :

David Julian is a freelance technology consultant and educator. He has worked as a consultant for government, private, and community organizations on a variety of projects, including using machine learning to detect insect outbreaks in controlled agricultural environments (Urban Ecological Systems Ltd., Bluesmart Farms), designing and implementing event management data systems (Sustainable Industry Expo, Lismore City Council), and designing multimedia interactive installations (Adelaide University). He has also written Designing Machine Learning Systems With Python for Packt Publishing and was a technical reviewer for Python Machine Learning and Hands-On Data Structures and Algorithms with Python - Second Edition, published by Packt.

Content

Cover
Copyright
Credits
Preface
Table of Contents
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
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
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
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
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
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
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
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
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
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
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
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
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
Thinking in Machine Learning
The human interface
Design principles
Summary
Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Matplotlib
Pandas
SciPy
Scikit-learn
Summary
Turning Data into Information
What is data?
Big data
Signals
Cleaning data
Visualizing data
Summary
Models - Learning from Information
Logical models
Tree models
Rule models
Summary
Linear Models
Introducing least squares
Logistic regression
Multiclass classification
Regularization
Summary
Neural Networks
Getting started with neural networks
Logistic units
Cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Summary
Features - How Algorithms See the World
Feature types
Operations and statistics
Structured features
Transforming features
Principle component analysis
Summary
Learning with Ensembles
Ensemble types
Bagging
Boosting
Ensemble strategies
Summary
Design Strategies and Case Studies
Evaluating model performance
Model selection
Learning curves
Real-world case studies
Machine learning at a glance
Summary
Unsupervised Machine Learning
Principal component analysis
Introducing k-means clustering
Self-organizing maps
Further reading
Summary
Deep Belief Networks
Neural networks - a primer
Restricted Boltzmann Machine
Deep belief networks
Further reading
Summary
Stacked Denoising Autoencoders
Autoencoders
Stacked Denoising Autoencoders
Further reading
Summary
Convolutional Neural Networks
Introducing the CNN
Further Reading
Summary
Semi-Supervised Learning
Introduction
Understanding semi-supervised learning
Semi-supervised algorithms in action
Further reading
Summary
Text Feature Engineering
Introduction
Text feature engineering
Further reading
Summary
Feature Engineering Part II
Introduction
Creating a feature set
Feature engineering in practice
Further reading
Summary
Ensemble Methods
Introducing ensembles
Using models in dynamic applications
Further reading
Summary
Additional Python Machine Learning Tools
Alternative development tools
Further reading
Summary
Chapter Code Requirements
Biblography

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Python: Deeper Insights into Machine Learning

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