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Python Machine Learning Blueprints
Machinelearningistransformingthewayweunderstandandinteractwiththeworldaroundus.ThisbookistheperfectguideforyoutoputyourknowledgeandskillsintopracticeandusethePythonecosystemtocoverkeydomainsinmachinelearning.ThissecondeditioncoversarangeoflibrariesfromthePythonecosystem,includingTensorFlowandKeras,tohelpyouimplementreal-worldmachinelearningprojects.ThebookbeginsbygivingyouanoverviewofmachinelearningwithPython.Withthehelpofcomplexdatasetsandoptimizedtechniques,you’llgoontounderstandhowtoapplyadvancedconceptsandpopularmachinelearningalgorithmstoreal-worldprojects.Next,you’llcoverprojectsfromdomainssuchaspredictiveanalyticstoanalyzethestockmarketandrecommendationsystemsforGitHubrepositories.Inadditiontothis,you’llalsoworkonprojectsfromtheNLPdomaintocreateacustomnewsfeedusingframeworkssuchasscikit-learn,TensorFlow,andKeras.Followingthis,you’lllearnhowtobuildanadvancedchatbot,andscalethingsupusingPySpark.Intheconcludingchapters,youcanlookforwardtoexcitinginsightsintodeeplearningandyou'llevencreateanapplicationusingcomputervisionandneuralnetworks.Bytheendofthisbook,you’llbeabletoanalyzedataseamlesslyandmakeapowerfulimpactthroughyourprojects.
目錄(157章)
倒序
- coverpage
- 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 更新時間：2021-07-02 13:50:10
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