Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

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Python Machine Learning Cookbook
Prateek Joshi | Vahid Mirjalili
Jun, 2016 | 304 pages
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Table of Contents (19 chapters)
Python Machine Learning Cookbook
Python Machine Learning Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
The Realm of Supervised Learning
The Realm of Supervised Learning
Introduction
Preprocessing data using different techniques
Label encoding
Building a linear regressor
Computing regression accuracy
Achieving model persistence
Building a ridge regressor
Building a polynomial regressor
Estimating housing prices
Computing the relative importance of features
Estimating bicycle demand distribution
2
Constructing a Classifier
Constructing a Classifier
Introduction
Building a simple classifier
Building a logistic regression classifier
Building a Naive Bayes classifier
Splitting the dataset for training and testing
Evaluating the accuracy using cross-validation
Visualizing the confusion matrix
Extracting the performance report
Evaluating cars based on their characteristics
Extracting validation curves
Extracting learning curves
Estimating the income bracket
3
Predictive Modeling
Predictive Modeling
Introduction
Building a linear classifier using Support Vector Machine (SVMs)
Building a nonlinear classifier using SVMs
Tackling class imbalance
Extracting confidence measurements
Finding optimal hyperparameters
Building an event predictor
Estimating traffic
4
Clustering with Unsupervised Learning
Clustering with Unsupervised Learning
Introduction
Clustering data using the k-means algorithm
Compressing an image using vector quantization
Building a Mean Shift clustering model
Grouping data using agglomerative clustering
Evaluating the performance of clustering algorithms
Automatically estimating the number of clusters using DBSCAN algorithm
Finding patterns in stock market data
Building a customer segmentation model
5
Building Recommendation Engines
Building Recommendation Engines
Introduction
Building function compositions for data processing
Building machine learning pipelines
Finding the nearest neighbors
Constructing a k-nearest neighbors classifier
Constructing a k-nearest neighbors regressor
Computing the Euclidean distance score
Computing the Pearson correlation score
Finding similar users in the dataset
Generating movie recommendations
6
Analyzing Text Data
Analyzing Text Data
Introduction
Preprocessing data using tokenization
Stemming text data
Converting text to its base form using lemmatization
Dividing text using chunking
Building a bag-of-words model
Building a text classifier
Identifying the gender
Analyzing the sentiment of a sentence
Identifying patterns in text using topic modeling
7
Speech Recognition
Speech Recognition
Introduction
Reading and plotting audio data
Transforming audio signals into the frequency domain
Generating audio signals with custom parameters
Synthesizing music
Extracting frequency domain features
Building Hidden Markov Models
Building a speech recognizer
8
Dissecting Time Series and Sequential Data
Dissecting Time Series and Sequential Data
Introduction
Transforming data into the time series format
Slicing time series data
Operating on time series data
Extracting statistics from time series data
Building Hidden Markov Models for sequential data
Building Conditional Random Fields for sequential text data
Analyzing stock market data using Hidden Markov Models
9
Image Content Analysis
Image Content Analysis
Introduction
Operating on images using OpenCV-Python
Detecting edges
Histogram equalization
Detecting corners
Detecting SIFT feature points
Building a Star feature detector
Creating features using visual codebook and vector quantization
Training an image classifier using Extremely Random Forests
Building an object recognizer
10
Biometric Face Recognition
Biometric Face Recognition
Introduction
Capturing and processing video from a webcam
Building a face detector using Haar cascades
Building eye and nose detectors
Performing Principal Components Analysis
Performing Kernel Principal Components Analysis
Performing blind source separation
Building a face recognizer using Local Binary Patterns Histogram
11
Deep Neural Networks
Deep Neural Networks
Introduction
Building a perceptron
Building a single layer neural network
Building a deep neural network
Creating a vector quantizer
Building a recurrent neural network for sequential data analysis
Visualizing the characters in an optical character recognition database
Building an optical character recognizer using neural networks
12
Visualizing Data
Visualizing Data
Introduction
Plotting 3D scatter plots
Plotting bubble plots
Animating bubble plots
Drawing pie charts
Plotting date-formatted time series data
Plotting histograms
Visualizing heat maps
Animating dynamic signals
Index
Index

Achieving model persistence

When we train a model, it would be nice if we could save it as a file so that it can be used later by simply loading it again.

How to do it…

Let's see how to achieve model persistence programmatically:

  1. Add the following lines to regressor.py:

    import cPickle as pickle

output_model_file = 'saved_model.pkl'
with open(output_model_file, 'w') as f:
    pickle.dump(linear_regressor, f)

  2. The regressor object will be saved in the saved_model.pkl file. Let's look at how to load it and use it, as follows:

    with open(output_model_file, 'r') as f:
    model_linregr = pickle.load(f)

y_test_pred_new = model_linregr.predict(X_test)
print "\nNew mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred_new), 2)

  3. Here, we just loaded the regressor from the file into the model_linregr variable. You can compare the preceding result with the earlier result to confirm that it's the same.

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