Title Page Copyright and Credits Python Machine Learning Blueprints Second Edition About Packt Why subscribe? Packt.com Contributors About the authors About the reviewer Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Reviews 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 Other Books You May Enjoy Leave a review - let other readers know what you think