Python Machine Learning Cookbook

100 recipes that teach you how to perform various machine learning tasks in the real world
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Python Machine Learning Cookbook

Prateek Joshi

4 customer reviews
100 recipes that teach you how to perform various machine learning tasks in the real world

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Book Details

ISBN 139781786464477
Paperback304 pages

Book Description

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

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: 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
Chapter 8: 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
Chapter 9: 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
Chapter 10: 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
Chapter 11: 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
Chapter 12: 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

What You Will Learn

  • Explore classification algorithms and apply them to the income bracket estimation problem
  • Use predictive modeling and apply it to real-world problems
  • Understand how to perform market segmentation using unsupervised learning
  • Explore data visualization techniques to interact with your data in diverse ways
  • Find out how to build a recommendation engine
  • Understand how to interact with text data and build models to analyze it
  • Work with speech data and recognize spoken words using Hidden Markov Models
  • Analyze stock market data using Conditional Random Fields
  • Work with image data and build systems for image recognition and biometric face recognition
  • Grasp how to use deep neural networks to build an optical character recognition system

Authors

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: 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
Chapter 8: 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
Chapter 9: 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
Chapter 10: 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
Chapter 11: 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
Chapter 12: 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

Book Details

ISBN 139781786464477
Paperback304 pages
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