Learning Data Mining with Python - Second Edition by Robert Layton

Key FeaturesUse a wide variety of Python libraries for practical data mining purposes.Learn how to find, manipulate, analyze, and visualize data using Python.Step-by-step instructions on data mining techniques with Python that have real-world applications.Book DescriptionThis book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.What you will learnApply data mining concepts to real-world problemsPredict the outcome of sports matches based on past resultsDetermine the author of a document based on their writing styleUse APIs to download datasets from social media and other online servicesFind and extract good features from difficult datasetsCreate models that solve real-world problemsDesign and develop data mining applications using a variety of datasetsPerform object detection in images using Deep Neural NetworksFind meaningful insights from your data through intuitive visualizationsCompute on big data, including real-time data from the internetAbout the AuthorRobert Layton is a data scientist investigating data-driven applications to businesses across a number of sectors. He received a PhD investigating cybercrime analytics from the Internet Commerce Security Laboratory at Federation University Australia, before moving into industry, starting his own data analytics company dataPipeline (www.datapipeline.com.au). Next, he created Eureaktive (www.eureaktive.com.au), which works with tech-based startups on developing their proof-of-concepts and early-stage prototypes. Robert also runs www.learningtensorflow.com, which is one of the worlds premier tutorial websites for Googles TensorFlow library.Robert is an active member of the Python community, having used Python for more than 8 years. He has presented at PyConAU for the last four years and works with Python Charmers to provide Python-based training for businesses and professionals from a wide range of organisations.Robert can be best reached via Twitter @robertlaytonTable of ContentsGetting Started with Data MiningClassifying with scikit-learn EstimatorsPredicting Sports Winners with Decision TreesRecommending Movies Using Affinity AnalysisFeatures and scikit-learn TransformersSocial Media Insight using Naive BayesFollow Recommendations Using Graph MiningBeating CAPTCHAs with Neural NetworksAuthorship AttributionClustering News ArticlesObject Detection in Images using Deep Neural NetworksWorking with Big DataNext Steps...