Learning.Data.Mining.with.Python.1784396052下载

1努力加油1 2019-03-05 06:58:31

Harness the power of Python to analyze data and create insightful predictive models

About This Book
Learn data mining in practical terms, using a wide variety of libraries and techniques
Learn how to find, manipulate, and analyze data using Python
Step-by-step instructions on creating real-world ap
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Harness the power of Python to analyze data and create insightful predictive models About This Book Learn data mining in practical terms, using a wide variety of libraries and techniques Learn how to find, manipulate, and analyze data using Python Step-by-step instructions on creating real-world applications of data mining techniques Who This Book Is For If you are a programmer who wants to get started with data mining, then this book is for you. What You Will Learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Set up reproducible experiments and generate robust results Recommend movies, online celebrities, and news articles based on personal preferences Compute on big data, including real-time data from the Internet In Detail The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations. Table of Contents Chapter 1: Getting Started with Data Mining Chapter 2: Classifying with scikit-learn Chapter 3: Predicting Sports Winners with Decision Trees Chapter 4: Recommending Movies Using Affinity Analysis Chapter 5: Extracting Features with Transformers Chapter 6: Social Media Insight Using Naive Bayes Chapter 7: Discovering Accounts to Follow Using Graph Mining Chapter 8: Beating CAPTCHAs with Neural Networks Chapter 9: Authorship Attribution Chapter 10: Clustering News Articles Chapter 11: Classifying Objects in Images Using Deep Learning Chapter 12: Working with Big Data Appendix: Next Steps…

earning Data Mining with Python - Second Edition by Robert Layton English | 4 May 2017 | ASIN: B01MRP7VFV | 358 Pages | AZW3 | 2.85 MB Key Features Use 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 Description This 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 learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Perform object detection in images using Deep Neural Networks Find meaningful insights from your data through intuitive visualizations Compute on big data, including real-time data from the internet About the Author Robert Layton is a data scientist working mainly on text mining problems for industries including the finance, information security, and transport sectors. He runs dataPipeline to build algorithms for practical use, and Eurekative, helping bringing start-ups to life in regional Australia. He has presented at the last four PyCon AU conferences, at multiple international research conferences, and has been training in some capacity for five years. He has a PhD in cybercrime analytics from the Internet Commerce Security Laboratory at Federation University Australia, where he was the Inaugural Young Alumni of the Year in 2014 and is currently and Honorary Research Fellow. You can find him on LinkedIn at https://www.linkedin.com/in/drrobertlayton and on Twitter at @robertlayton. Robert writes regularly on data mining and cybercrime, in a private, consultancy, and a research capacity. Robert is an Official Member of the Ballarat Hackerspace, where he helps grow the future-tech sector in regional Victoria.

Machine Learning with scikit-learn LiveLessons 是scikit-learn库的指南，该库提供了广泛的机器学习算法，以及通用且直观的相对统一的Python API。为各种模型提供的几十个类中大多数有着相同的接口。大多数...

英文书籍，用Python实现数据挖掘的过程，介绍了python中常用的机器学习库

Key Features Dive deeper into data mining with Python – don't be complacent, sharpen your skills! From the most common elements of data mining to cutting-edge techniques, we've got you covered for any data-related challenge Become a more fluent and confident Python data-analyst, in full control of its extensive range of libraries Book Description Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy – without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding. If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get. By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics. What you will learn Explore techniques for finding frequent itemsets and association rules in large data sets Learn identification methods for entity matches across many different types of data Identify the basics of network mining and how to apply it to real-world data sets Discover methods for detecting the sentiment of text and for locating named entities in text Observe multiple techniques for automatically extracting summaries and generating topic models for text See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set About the Author Megan Squire is a professor of computing sciences at Elon University. Her primary research interest is in collecting, cleaning, and analyzing data about how free and open source software is made. She is one of the leaders of the FLOSSmole.org, FLOSSdata.org, and FLOSSpapers.org projects. Table of Contents Chapter 1. Expanding Your Data Mining Toolbox Chapter 2. Association Rule Mining Chapter 3. Entity Matching Chapter 4. Network Analysis Chapter 5. Sentiment Analysis in Text Chapter 6. Named Entity Recognition in Text Chapter 7. Automatic Text Summarization Chapter 8. Topic Modeling in Text Chapter 9. Mining for Data Anomalies

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