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Home > Data > Machine Learning > Python Machine Learning
Python Machine Learning
Python Machine Learning

Python Machine Learning: Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

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Profile Icon Sebastian Raschka
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Python Machine Learning

Chapter 2. Training Machine Learning Algorithms for Classification

In this chapter, we will make use of one of the first algorithmically described machine learning algorithms for classification, the perceptron and adaptive linear neurons. We will start by implementing a perceptron step by step in Python and training it to classify different flower species in the Iris dataset. This will help us to understand the concept of machine learning algorithms for classification and how they can be efficiently implemented in Python. Discussing the basics of optimization using adaptive linear neurons will then lay the groundwork for using more powerful classifiers via the scikit-learn machine-learning library in Chapter 3, A Tour of Machine Learning Classifiers Using Scikit-learn.

The topics that we will cover in this chapter are as follows:

  • Building an intuition for machine learning algorithms
  • Using pandas, NumPy, and matplotlib to read in, process, and visualize data
  • Implementing linear classification...

Artificial neurons – a brief glimpse into the early history of machine learning

Before we discuss the perceptron and related algorithms in more detail, let us take a brief tour through the early beginnings of machine learning. Trying to understand how the biological brain works to design artificial intelligence, Warren McCullock and Walter Pitts published the first concept of a simplified brain cell, the so-called McCullock-Pitts (MCP) neuron, in 1943 (W. S. McCulloch and W. Pitts. A Logical Calculus of the Ideas Immanent in Nervous Activity. The bulletin of mathematical biophysics, 5(4):115–133, 1943). Neurons are interconnected nerve cells in the brain that are involved in the processing and transmitting of chemical and electrical signals, which is illustrated in the following figure:

Artificial neurons – a brief glimpse into the early history of machine learning

McCullock and Pitts described such a nerve cell as a simple logic gate with binary outputs; multiple signals arrive at the dendrites, are then integrated into the cell body, and, if the accumulated...

Implementing a perceptron learning algorithm in Python

In the previous section, we learned how Rosenblatt's perceptron rule works; let us now go ahead and implement it in Python and apply it to the Iris dataset that we introduced in Chapter 1, Giving Computers the Ability to Learn from Data. We will take an objected-oriented approach to define the perceptron interface as a Python Class, which allows us to initialize new perceptron objects that can learn from data via a fit method, and make predictions via a separate predict method. As a convention, we add an underscore to attributes that are not being created upon the initialization of the object but by calling the object's other methods—for example, self.w_.

Note

If you are not yet familiar with Python's scientific libraries or need a refresher, please see the following resources:

NumPy: http://wiki.scipy.org/Tentative_NumPy_Tutorial

Pandas: http://pandas.pydata.org/pandas-docs/stable/tutorials.html

Matplotlib: http...

Adaptive linear neurons and the convergence of learning

In this section, we will take a look at another type of single-layer neural network: ADAptive LInear NEuron (Adaline). Adaline was published, only a few years after Frank Rosenblatt's perceptron algorithm, by Bernard Widrow and his doctoral student Tedd Hoff, and can be considered as an improvement on the latter (B. Widrow et al. Adaptive "Adaline" neuron using chemical "memistors". Number Technical Report 1553-2. Stanford Electron. Labs. Stanford, CA, October 1960). The Adaline algorithm is particularly interesting because it illustrates the key concept of defining and minimizing cost functions, which will lay the groundwork for understanding more advanced machine learning algorithms for classification, such as logistic regression and support vector machines, as well as regression models that we will discuss in future chapters.

The key difference between the Adaline rule (also known as the Widrow-Hoff rule...

Summary

In this chapter, we gained a good understanding of the basic concepts of linear classifiers for supervised learning. After we implemented a perceptron, we saw how we can train adaptive linear neurons efficiently via a vectorized implementation of gradient descent and on-line learning via stochastic gradient descent. Now that we have seen how to implement simple classifiers in Python, we are ready to move on to the next chapter where we will use the Python scikit-learn machine learning library to get access to more advanced and powerful off-the-shelf machine learning classifiers that are commonly used in academia as well as in industry.

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Key benefits

  • • Leverage Python’s most powerful open-source libraries for deep learning, data wrangling, and data visualization
  • • Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms
  • • Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets

Description

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.

Who is this book for?

If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

What you will learn

  • • Explore how to use different machine learning models to ask different questions of your data
  • • Learn how to build neural networks using Keras and Theano
  • • Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
  • • Discover how to embed your machine learning model in a web application for increased accessibility
  • • Predict continuous target outcomes using regression analysis
  • • Uncover hidden patterns and structures in data with clustering
  • • Organize data using effective pre-processing techniques
  • • Get to grips with sentiment analysis to delve deeper into textual and social media data

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 23, 2015
Length: 454 pages
Edition : 1st
Language : English
ISBN-13 : 9781783555147
Category :
Data
Languages :
Python
Concepts :
Machine Learning
Tools :
SciPy

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Publication date : Sep 23, 2015
Length: 454 pages
Edition : 1st
Language : English
ISBN-13 : 9781783555147
Category :
Data
Languages :
Python
Concepts :
Machine Learning
Tools :
SciPy

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Table of Contents

14 Chapters
1. Giving Computers the Ability to Learn from Data Chevron down icon Chevron up icon
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
2. Training Machine Learning Algorithms for Classification Chevron down icon Chevron up icon
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
3. A Tour of Machine Learning Classifiers Using Scikit-learn Chevron down icon Chevron up icon
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
4. Building Good Training Sets – Data Preprocessing Chevron down icon Chevron up icon
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
5. Compressing Data via Dimensionality Reduction Chevron down icon Chevron up icon
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
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning Chevron down icon Chevron up icon
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
7. Combining Different Models for Ensemble Learning Chevron down icon Chevron up icon
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
8. Applying Machine Learning to Sentiment Analysis Chevron down icon Chevron up icon
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
9. Embedding a Machine Learning Model into a Web Application Chevron down icon Chevron up icon
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
10. Predicting Continuous Target Variables with Regression Analysis Chevron down icon Chevron up icon
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
11. Working with Unlabeled Data – Clustering Analysis Chevron down icon Chevron up icon
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
12. Training Artificial Neural Networks for Image Recognition Chevron down icon Chevron up icon
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
13. Parallelizing Neural Network Training with Theano Chevron down icon Chevron up icon
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
Index Chevron down icon Chevron up icon
Index

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tauco Jul 03, 2016
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Very well explained Step by step. Easy to follow
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Keith Lancaster Mar 12, 2016
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As with many other reviewers, I have numerous books on Python machine learning. I purchased this book this morning and can already say that it is by far the best that I have encountered. Worth every cent!
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BookwormMin Jul 01, 2017
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best book on practical machine learning, including building API, having a server, and of course intro to all the algorithm as well as pre-processing.
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Best book on ML/Python - I know, because I read them all! :)
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I have nothing to add that hasn't already been written. But, I had to input a few words to confer a 5 star rating.I only add that I've been following Sebastian's blog as a reference for some time, now. So, it was only logical to buy the definitive sci-kit source compendium for reference.
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Profile icon Sebastian Raschka
Sebastian Raschka
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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.
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