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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

Building a polynomial regressor

One of the main constraints of a linear regression model is the fact that it tries to fit a linear function to the input data. The polynomial regression model overcomes this issue by allowing the function to be a polynomial, thereby increasing the accuracy of the model.

Getting ready

Let's consider the following figure:

Getting ready

We can see that there is a natural curve to the pattern of datapoints. This linear model is unable to capture this. Let's see what a polynomial model would look like:

Getting ready

The dotted line represents the linear regression model, and the solid line represents the polynomial regression model. The curviness of this model is controlled by the degree of the polynomial. As the curviness of the model increases, it gets more accurate. However, curviness adds complexity to the model as well, hence, making it slower. This is a trade off where you have to decide between how accurate you want your model to be given the computational constraints.

How to do it…

  1. Add the following lines to regressor.py:
    from sklearn.preprocessing import PolynomialFeatures

polynomial = PolynomialFeatures(degree=3)
  2. We initialized a polynomial of the degree 3 in the previous line. Now we have to represent the datapoints in terms of the coefficients of the polynomial:
    X_train_transformed = polynomial.fit_transform(X_train)

    Here, X_train_transformed represents the same input in the polynomial form.

  3. Let's consider the first datapoint in our file and check whether it can predict the right output:
    datapoint = [0.39,2.78,7.11]
poly_datapoint = polynomial.fit_transform(datapoint)

poly_linear_model = linear_model.LinearRegression()
poly_linear_model.fit(X_train_transformed, y_train)
print "\nLinear regression:", linear_regressor.predict(datapoint)[0]
print "\nPolynomial regression:", poly_linear_model.predict(poly_datapoint)[0]

    The values in the variable datapoint are the values in the first line in the input data file. We are still fitting a linear regression model here. The only difference is in the way in which we represent the data. If you run this code, you will see the following output:

    Linear regression: -11.0587294983
Polynomial regression: -10.9480782122

    As you can see, this is close to the output value. If we want it to get closer, we need to increase the degree of the polynomial.

  4. Let's make it 10 and see what happens:
    polynomial = PolynomialFeatures(degree=10)

    You should see something like the following:

    Polynomial regression: -8.20472183853

Now, you can see that the predicted value is much closer to the actual output value.

CONTINUE READING
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