Book Image

Python Machine Learning Cookbook - Second Edition

By : Giuseppe Ciaburro, Prateek Joshi
Book Image

Python Machine Learning Cookbook - Second Edition

By: Giuseppe Ciaburro, Prateek Joshi

Overview of this book

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.

Related Content you might be interested in

Current Title:

Small book image
Python Machine Learning Cookbook - Second Edition
Giuseppe Ciaburro | Prateek Joshi
Mar, 2019 | 642 pages
Small book image
Artificial Intelligence with Python
Prateek Joshi
Jan, 2017 | 446 pages
Small book image
Artificial Intelligence with Python
Alberto Artasanchez | Prateek Joshi
Jan, 2020 | 618 pages
Small book image
Keras Reinforcement Learning Projects
Giuseppe Ciaburro
Sep, 2018 | 288 pages
Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
Free Chapter
1
The Realm of Supervised Learning
The Realm of Supervised Learning
Technical requirements
Introduction
Array creation in Python
Data preprocessing using mean removal
Data scaling
Normalization
Binarization
One-hot encoding
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
Technical requirements
Introduction
Building a simple classifier
Building a logistic regression classifier
Building a Naive Bayes classifier
Splitting a dataset for training and testing
Evaluating accuracy using cross-validation metrics
Visualizing a confusion matrix
Extracting a performance report
Evaluating cars based on their characteristics
Extracting validation curves
Extracting learning curves
Estimating the income bracket
Predicting the quality of wine
Newsgroup trending topics classification
3
Predictive Modeling
Predictive Modeling
Technical requirements
Introduction
Building a linear classifier using SVMs
Building a nonlinear classifier using SVMs
Tackling class imbalance
Extracting confidence measurements
Finding optimal hyperparameters
Building an event predictor
Estimating traffic
Simplifying machine learning workflow using TensorFlow
Implementing a stacking method
4
Clustering with Unsupervised Learning
Clustering with Unsupervised Learning
Technical requirements
Introduction
Clustering data using the k-means algorithm
Compressing an image using vector quantization
Grouping data using agglomerative clustering
Evaluating the performance of clustering algorithms
Estimating the number of clusters using the DBSCAN algorithm
Finding patterns in stock market data
Building a customer segmentation model
Using autoencoders to reconstruct handwritten digit images
5
Visualizing Data
Visualizing Data
Technical requirements
An introduction to data visualization
Plotting three-dimensional 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
Working with the Seaborn library
6
Building Recommendation Engines
Building Recommendation Engines
Technical requirements
Introducing the recommendation engine
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
Implementing ranking algorithms
Building a filtering model using TensorFlow
7
Analyzing Text Data
Analyzing Text Data
Technical requirements
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 of a name
Analyzing the sentiment of a sentence
Identifying patterns in text using topic modeling
Parts of speech tagging with spaCy
Word2Vec using gensim
Shallow learning for spam detection
8
Speech Recognition
Speech Recognition
Technical requirements
Introducing speech recognition
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 HMMs
Building a speech recognizer
Building a TTS system
9
Dissecting Time Series and Sequential Data
Dissecting Time Series and Sequential Data
Technical requirements
Introducing time series
Transforming data into a time series format
Slicing time series data
Operating on time series data
Extracting statistics from time series data
Building HMMs for sequential data
Building CRFs for sequential text data
Analyzing stock market data
Using RNNs to predict time series data
10
Analyzing Image Content
Analyzing Image Content
Technical requirements
Introducing computer vision
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
Using Light GBM for image classification
11
Biometric Face Recognition
Biometric Face Recognition
Technical requirements
Introduction
Capturing and processing video from a webcam
Building a face detector using Haar cascades
Building eye and nose detectors
Performing principal component analysis
Performing kernel principal component analysis
Performing blind source separation
Building a face recognizer using a local binary patterns histogram
Recognizing faces using the HOG-based model
Facial landmark recognition
User authentication by face recognition
12
Reinforcement Learning Techniques
Reinforcement Learning Techniques
Technical requirements
Introduction
Weather forecasting with MDP
Optimizing a financial portfolio using DP
Finding the shortest path
Deciding the discount factor using Q-learning
Implementing the deep Q-learning algorithm
Developing an AI-based dynamic modeling system
Deep reinforcement learning with double Q-learning
Deep Q-network algorithm with dueling Q-learning
13
Deep Neural Networks
Deep Neural Networks
Technical requirements
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 OCR database
Building an optical character recognizer using neural networks
Implementing optimization algorithms in ANN
14
Unsupervised Representation Learning
Unsupervised Representation Learning
Technical requirements
Introduction
Using denoising autoencoders to detect fraudulent transactions
Generating word embeddings using CBOW and skipgram representations
Visualizing the MNIST dataset using PCA and t-SNE
Using word embedding for Twitter sentiment analysis
Implementing LDA with scikit-learn
Using LDA to classify text documents
Preparing data for LDA
15
Automated Machine Learning and Transfer Learning
Automated Machine Learning and Transfer Learning
Technical requirements
Introduction
Working with Auto-WEKA
Using AutoML to generate machine learning pipelines with TPOT
Working with Auto-Keras
Working with auto-sklearn
Using MLBox for selection and leak detection
Convolutional neural networks with transfer learning
Transfer learning with pretrained image classifiers using ResNet-50
Transfer learning using feature extraction with the VGG16 model
Transfer learning with pretrained GloVe embedding
16
Unlocking Production Issues
Unlocking Production Issues
Technical requirements
Introduction
Handling unstructured data
Deploying machine learning models
Keeping track of changes into production
Tracking accuracy to optimize model scaling
17
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Dividing text using chunking

Chunking refers to dividing the input text into pieces, which are based on any random condition. This is different from tokenization in the sense that there are no constraints, and the chunks do not need to be meaningful at all. This is used very frequently during text analysis. While dealing with large text documents, it's better to do it in chunks.

How to do it...

Let's look at how to divide text by using chunking:

  1. Create a new Python file and import the following packages (the full code is in the chunking.py file that's already been provided to you):
import numpy as np 
nltk.download('brown')
from nltk.corpus import brown
  1. Let's define a function to split the...
Previous Section
End of Section 7
Next Section