Large Scale Machine Learning with Python

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

Large Scale Machine Learning with Python

This ebook is included in a Mapt subscription
Bastiaan Sjardin, Luca Massaron, Alberto Boschetti

2 customer reviews
Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
$0.00
$34.00
$49.99
$29.99p/m after trial
RRP $39.99
RRP $49.99
Subscription
eBook
Print + eBook
Start 30 Day Trial
Subscribe and access every Packt eBook & Video.
 
  • 4,000+ eBooks & Videos
  • 40+ New titles a month
  • 1 Free eBook/Video to keep every month
Start Free Trial
 
Preview in Mapt

Book Details

ISBN 139781785887215
Paperback420 pages

Book Description

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy.

Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

Table of Contents

Chapter 1: First Steps to Scalability
Explaining scalability in detail
Python for large scale machine learning
Python packages
Summary
Chapter 2: Scalable Learning in Scikit-learn
Out-of-core learning
Streaming data from sources
Stochastic learning
Feature management with data streams
Summary
Chapter 3: Fast SVM Implementations
Datasets to experiment with on your own
Support Vector Machines
Feature selection by regularization
Including non-linearity in SGD
Hyperparameter tuning
Summary
Chapter 4: Neural Networks and Deep Learning
The neural network architecture
Neural networks and regularization
Neural networks and hyperparameter optimization
Neural networks and decision boundaries
Deep learning at scale with H2O
Deep learning and unsupervised pretraining
Deep learning with theanets
Autoencoders and unsupervised learning
Summary
Chapter 5: Deep Learning with TensorFlow
TensorFlow installation
Machine learning on TensorFlow with SkFlow
Keras and TensorFlow installation
Convolutional Neural Networks in TensorFlow through Keras
CNN's with an incremental approach
GPU Computing
Summary
Chapter 6: Classification and Regression Trees at Scale
Bootstrap aggregation
Random forest and extremely randomized forest
Fast parameter optimization with randomized search
CART and boosting
XGBoost
Out-of-core CART with H2O
Summary
Chapter 7: Unsupervised Learning at Scale
Unsupervised methods
Feature decomposition – PCA
PCA with H2O
Clustering – K-means
K-means with H2O
LDA
Summary
Chapter 8: Distributed Environments – Hadoop and Spark
From a standalone machine to a bunch of nodes
Setting up the VM
The Hadoop ecosystem
Spark
Summary
Chapter 9: Practical Machine Learning with Spark
Setting up the VM for this chapter
Sharing variables across cluster nodes
Data preprocessing in Spark
Machine learning with Spark
Summary

What You Will Learn

  • Apply the most scalable machine learning algorithms
  • Work with modern state-of-the-art large-scale machine learning techniques
  • Increase predictive accuracy with deep learning and scalable data-handling techniques
  • Improve your work by combining the MapReduce framework with Spark
  • Build powerful ensembles at scale
  • Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine

Authors

Table of Contents

Chapter 1: First Steps to Scalability
Explaining scalability in detail
Python for large scale machine learning
Python packages
Summary
Chapter 2: Scalable Learning in Scikit-learn
Out-of-core learning
Streaming data from sources
Stochastic learning
Feature management with data streams
Summary
Chapter 3: Fast SVM Implementations
Datasets to experiment with on your own
Support Vector Machines
Feature selection by regularization
Including non-linearity in SGD
Hyperparameter tuning
Summary
Chapter 4: Neural Networks and Deep Learning
The neural network architecture
Neural networks and regularization
Neural networks and hyperparameter optimization
Neural networks and decision boundaries
Deep learning at scale with H2O
Deep learning and unsupervised pretraining
Deep learning with theanets
Autoencoders and unsupervised learning
Summary
Chapter 5: Deep Learning with TensorFlow
TensorFlow installation
Machine learning on TensorFlow with SkFlow
Keras and TensorFlow installation
Convolutional Neural Networks in TensorFlow through Keras
CNN's with an incremental approach
GPU Computing
Summary
Chapter 6: Classification and Regression Trees at Scale
Bootstrap aggregation
Random forest and extremely randomized forest
Fast parameter optimization with randomized search
CART and boosting
XGBoost
Out-of-core CART with H2O
Summary
Chapter 7: Unsupervised Learning at Scale
Unsupervised methods
Feature decomposition – PCA
PCA with H2O
Clustering – K-means
K-means with H2O
LDA
Summary
Chapter 8: Distributed Environments – Hadoop and Spark
From a standalone machine to a bunch of nodes
Setting up the VM
The Hadoop ecosystem
Spark
Summary
Chapter 9: Practical Machine Learning with Spark
Setting up the VM for this chapter
Sharing variables across cluster nodes
Data preprocessing in Spark
Machine learning with Spark
Summary

Book Details

ISBN 139781785887215
Paperback420 pages
Read More
From 2 reviews

Read More Reviews