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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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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
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Table of Contents

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

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(100 Ratings)
5 star 62%
4 star 17%
3 star 11%
2 star 6%
1 star 4%
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tauco Jul 03, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very well explained Step by step. Easy to follow
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Keith Lancaster Mar 12, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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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Anonymous123 Jan 22, 2016
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Best book on ML/Python - I know, because I read them all! :)
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Amazonian Drone Jan 01, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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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