Python Machine Learning Blueprints电子书

Title Page

Copyright and Credits

Python Machine Learning Blueprints Second Edition

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Preface

Who this book is for

What this book covers

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Download the example code files

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Conventions used

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The Python Machine Learning Ecosystem

Data science/machine learning workflow

Acquisition

Inspection

Preparation

Modeling

Evaluation

Deployment

Python libraries and functions for each stage of the data science workflow

Acquisition

Inspection

The Jupyter Notebook

Pandas

Visualization

The matplotlib library

The seaborn library

Preparation

map

apply

applymap

groupby

Modeling and evaluation

Statsmodels

Scikit-learn

Deployment

Setting up your machine learning environment

Summary

Build an App to Find Underpriced Apartments

Sourcing apartment listing data

Pulling down listing data

Pulling out the individual data points

Parsing data

Inspecting and preparing the data

Sneak-peek at the data types

Visualizing our data

Visualizing the data

Modeling the data

Forecasting

Extending the model

Summary

Build an App to Find Cheap Airfares

Sourcing airfare pricing data

Retrieving fare data with advanced web scraping

Creating a link

Parsing the DOM to extract pricing data

Parsing

Identifying outlier fares with anomaly detection techniques

Sending real-time alerts using IFTTT

Putting it all together

Summary

Forecast the IPO Market Using Logistic Regression

The IPO market

What is an IPO?

Recent IPO market performance

Working on the DataFrame

Analyzing the data

Summarizing the performance of the stocks

Baseline IPO strategy

Data cleansing and feature engineering

Adding features to influence the performance of an IPO

Binary classification with logistic regression

Creating the target for our model

Dummy coding

Examining the model performance

Generating the importance of a feature from our model

Random forest classifier method

Summary

Create a Custom Newsfeed

Creating a supervised training set with Pocket

Installing the Pocket Chrome Extension

Using the Pocket API to retrieve stories

Using the Embedly API to download story bodies

Basics of Natural Language Processing

Support Vector Machines

IFTTT integration with feeds, Google Sheets, and email

Setting up news feeds and Google Sheets through IFTTT

Setting up your daily personal newsletter

Summary

Predict whether Your Content Will Go Viral

What does research tell us about virality?

Sourcing shared counts and content

Exploring the features of shareability

Exploring image data

Clustering

Exploring the headlines

Exploring the story content

Building a predictive content scoring model

Evaluating the model

Adding new features to our model

Summary

Use Machine Learning to Forecast the Stock Market

Types of market analysis

What does research tell us about the stock market?

So, what exactly is a momentum strategy?

How to develop a trading strategy

Analysis of the data

Volatility of the returns

Daily returns

Statistics for the strategies

The mystery strategy

Building the regression model

Performance of the model

Dynamic time warping

Evaluating our trades

Summary

Classifying Images with Convolutional Neural Networks

Image-feature extraction

Convolutional neural networks

Network topology

Convolutional layers and filters

Max pooling layers

Flattening

Fully-connected layers and output

Building a convolutional neural network to classify images in the Zalando Research dataset, using Keras

Summary

Building a Chatbot

The Turing Test

The history of chatbots

The design of chatbots

Building a chatbot

Sequence-to-sequence modeling for chatbots

Summary

Build a Recommendation Engine

Collaborative filtering

So, what's collaborative filtering?

Predicting the rating for the product

Content-based filtering

Hybrid systems

Collaborative filtering

Content-based filtering

Building a recommendation engine

Summary

What's Next?

Summary of the projects

Summary

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