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  • Book Overview & Buying Python Machine Learning Cookbook
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Python Machine Learning Cookbook

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
4.4 (5)
Buy this Book
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Python Machine Learning Cookbook

Python Machine Learning Cookbook

4.4 (5)
By: Prateek Joshi, Vahid Mirjalili
Buy this Book

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.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Sections
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Conventions
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Reader feedback
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Customer support
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1
1. The Realm of Supervised Learning
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1. The Realm of Supervised Learning
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Introduction
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Preprocessing data using different techniques
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Label encoding
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Building a linear regressor
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Computing regression accuracy
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Achieving model persistence
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Building a ridge regressor
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Building a polynomial regressor
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Estimating housing prices
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Computing the relative importance of features
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Estimating bicycle demand distribution
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2. Constructing a Classifier
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2. Constructing a Classifier
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Introduction
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Building a simple classifier
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Building a logistic regression classifier
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Building a Naive Bayes classifier
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Splitting the dataset for training and testing
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Evaluating the accuracy using cross-validation
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Visualizing the confusion matrix
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Extracting the performance report
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Evaluating cars based on their characteristics
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Extracting validation curves
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Extracting learning curves
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Estimating the income bracket
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3. Predictive Modeling
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3. Predictive Modeling
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Introduction
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Building a linear classifier using Support Vector Machine (SVMs)
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Building a nonlinear classifier using SVMs
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Tackling class imbalance
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Extracting confidence measurements
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Finding optimal hyperparameters
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Building an event predictor
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Estimating traffic
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4. Clustering with Unsupervised Learning
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4. Clustering with Unsupervised Learning
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Introduction
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Clustering data using the k-means algorithm
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Compressing an image using vector quantization
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Building a Mean Shift clustering model
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Grouping data using agglomerative clustering
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Evaluating the performance of clustering algorithms
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Automatically estimating the number of clusters using DBSCAN algorithm
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Finding patterns in stock market data
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Building a customer segmentation model
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5. Building Recommendation Engines
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5. Building Recommendation Engines
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Introduction
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Building function compositions for data processing
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Building machine learning pipelines
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Finding the nearest neighbors
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Constructing a k-nearest neighbors classifier
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Constructing a k-nearest neighbors regressor
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Computing the Euclidean distance score
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Computing the Pearson correlation score
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Finding similar users in the dataset
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Generating movie recommendations
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6. Analyzing Text Data
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6. Analyzing Text Data
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Introduction
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Preprocessing data using tokenization
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Stemming text data
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Converting text to its base form using lemmatization
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Dividing text using chunking
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Building a bag-of-words model
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Building a text classifier
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Identifying the gender
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Analyzing the sentiment of a sentence
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Identifying patterns in text using topic modeling
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7. Speech Recognition
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7. Speech Recognition
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Introduction
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Reading and plotting audio data
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Transforming audio signals into the frequency domain
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Generating audio signals with custom parameters
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Synthesizing music
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Extracting frequency domain features
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Building Hidden Markov Models
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Building a speech recognizer
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8. Dissecting Time Series and Sequential Data
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8. Dissecting Time Series and Sequential Data
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Introduction
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Transforming data into the time series format
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Slicing time series data
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Operating on time series data
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Extracting statistics from time series data
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Building Hidden Markov Models for sequential data
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Building Conditional Random Fields for sequential text data
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Analyzing stock market data using Hidden Markov Models
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9. Image Content Analysis
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9. Image Content Analysis
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Introduction
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Operating on images using OpenCV-Python
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Detecting edges
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Histogram equalization
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Detecting corners
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Detecting SIFT feature points
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Building a Star feature detector
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Creating features using visual codebook and vector quantization
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Training an image classifier using Extremely Random Forests
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Building an object recognizer
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10. Biometric Face Recognition
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10. Biometric Face Recognition
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Introduction
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Capturing and processing video from a webcam
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Building a face detector using Haar cascades
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Building eye and nose detectors
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Performing Principal Components Analysis
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Performing Kernel Principal Components Analysis
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Performing blind source separation
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Building a face recognizer using Local Binary Patterns Histogram
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11. Deep Neural Networks
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11. Deep Neural Networks
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Introduction
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Building a perceptron
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Building a single layer neural network
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Building a deep neural network
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Creating a vector quantizer
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Building a recurrent neural network for sequential data analysis
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Visualizing the characters in an optical character recognition database
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Building an optical character recognizer using neural networks
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12. Visualizing Data
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12. Visualizing Data
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Introduction
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Plotting 3D scatter plots
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Plotting bubble plots
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Animating bubble plots
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Drawing pie charts
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Plotting date-formatted time series data
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Plotting histograms
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Visualizing heat maps
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Animating dynamic signals
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Index
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Index

Computing regression accuracy

Now that we know how to build a regressor, it's important to understand how to evaluate the quality of a regressor as well. In this context, an error is defined as the difference between the actual value and the value that is predicted by the regressor.

Getting ready

Let's quickly understand what metrics can be used to measure the quality of a regressor. A regressor can be evaluated using many different metrics, such as the following:

  • Mean absolute error: This is the average of absolute errors of all the datapoints in the given dataset.
  • Mean squared error: This is the average of the squares of the errors of all the datapoints in the given dataset. It is one of the most popular metrics out there!
  • Median absolute error: This is the median of all the errors in the given dataset. The main advantage of this metric is that it's robust to outliers. A single bad point in the test dataset wouldn't skew the entire error metric, as opposed to a mean error metric.
  • Explained variance score: This score measures how well our model can account for the variation in our dataset. A score of 1.0 indicates that our model is perfect.
  • R2 score: This is pronounced as R-squared, and this score refers to the coefficient of determination. This tells us how well the unknown samples will be predicted by our model. The best possible score is 1.0, and the values can be negative as well.

How to do it…

There is a module in scikit-learn that provides functionalities to compute all the following metrics. Open a new Python file and add the following lines:

import sklearn.metrics as sm

print "Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred), 2) 
print "Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred), 2) 
print "Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2) 
print "Explained variance score =", round(sm.explained_variance_score(y_test, y_test_pred), 2) 
print "R2 score =", round(sm.r2_score(y_test, y_test_pred), 2)

Keeping track of every single metric can get tedious, so we pick one or two metrics to evaluate our model. A good practice is to make sure that the mean squared error is low and the explained variance score is high.

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