Mastering Data Mining With Python
DOWNLOAD
Download Mastering Data Mining With Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Mastering Data Mining With Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Learning Data Mining With Python
DOWNLOAD
Author : Robert Layton
language : en
Publisher:
Release Date : 2015
Learning Data Mining With Python written by Robert Layton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Data mining categories.
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.
Mastering Data Mining With Python Find Patterns Hidden In Your Data
DOWNLOAD
Author : Megan Squire
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-08-29
Mastering Data Mining With Python Find Patterns Hidden In Your Data written by Megan Squire and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-29 with Computers categories.
Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques About This Book 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 Who This Book Is For This book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you! 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 In Detail 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. Style and approach This book will teach you the intricacies in applying data mining using real-world scenarios and will act as a very practical solution to your data mining needs.
Mastering Data Mining With Python
DOWNLOAD
Author : Megan Squire
language : en
Publisher:
Release Date : 2016
Mastering Data Mining With Python written by Megan Squire and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Business planning categories.
Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniques About This Book 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 Who This Book Is For This book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you! 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 In Detail 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 e...
Python Data Science Handbook
DOWNLOAD
Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21
Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Hands On Data Science And Python Machine Learning
DOWNLOAD
Author : Frank Kane
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-31
Hands On Data Science And Python Machine Learning written by Frank Kane and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-31 with Computers categories.
This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.
Mastering Data Mining
DOWNLOAD
Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :
Mastering Data Mining written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Uncover Hidden Insights and Patterns in Your Data Are you ready to delve into the fascinating realm of data mining? "Mastering Data Mining" is your ultimate guide to unlocking the potential of extracting hidden insights and patterns from your data. Whether you're a data scientist aiming to uncover valuable information or a business professional seeking to make informed decisions, this book equips you with the knowledge and techniques to master the art of data mining. Key Features: 1. Journey into Data Mining: Immerse yourself in the world of data mining, understanding its significance, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Preparation: Master the art of data exploration and preparation. Learn how to clean, transform, and preprocess data for effective mining. 3. Exploratory Data Analysis: Delve into exploratory data analysis techniques. Explore visualization, statistical summaries, and data profiling to gain a deeper understanding of your dataset. 4. Supervised Learning Techniques: Uncover the power of supervised learning techniques. Learn how to build predictive models for classification and regression tasks, enabling you to make accurate predictions. 5. Unsupervised Learning and Clustering: Discover unsupervised learning and clustering methods. Explore techniques for grouping similar data points and identifying hidden patterns without predefined labels. 6. Association Rule Mining: Master association rule mining for uncovering relationships in data. Learn how to identify frequent itemsets and extract valuable associations. 7. Text and Web Mining: Explore text and web mining techniques. Learn how to extract insights from textual data and discover patterns in web-based information. 8. Time Series Mining: Delve into time series mining for analyzing sequential data. Learn how to forecast trends, identify anomalies, and make predictions based on temporal patterns. 9. Data Mining Tools and Algorithms: Uncover a range of data mining tools and algorithms. Explore classic algorithms and modern techniques for various data mining tasks. 10. Real-World Applications: Gain insights into real-world use cases of data mining across industries. From customer segmentation to fraud detection, explore how organizations leverage data mining for strategic advantage. Who This Book Is For: "Mastering Data Mining" is an indispensable resource for data scientists, analysts, and business professionals who want to excel in uncovering insights from data. Whether you're new to data mining or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data mining. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Advanced Machine Learning With Python
DOWNLOAD
Author : John Hearty
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-07-28
Advanced Machine Learning With Python written by John Hearty and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-28 with Computers categories.
Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.
Mastering Large Datasets With Python
DOWNLOAD
Author : John Wolohan
language : en
Publisher: Simon and Schuster
Release Date : 2020-01-15
Mastering Large Datasets With Python written by John Wolohan and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-15 with Computers categories.
Summary Modern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python, author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You’ll explore methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Programming techniques that work well on laptop-sized data can slow to a crawl—or fail altogether—when applied to massive files or distributed datasets. By mastering the powerful map and reduce paradigm, along with the Python-based tools that support it, you can write data-centric applications that scale efficiently without requiring codebase rewrites as your requirements change. About the book Mastering Large Datasets with Python teaches you to write code that can handle datasets of any size. You’ll start with laptop-sized datasets that teach you to parallelize data analysis by breaking large tasks into smaller ones that can run simultaneously. You’ll then scale those same programs to industrial-sized datasets on a cluster of cloud servers. With the map and reduce paradigm firmly in place, you’ll explore tools like Hadoop and PySpark to efficiently process massive distributed datasets, speed up decision-making with machine learning, and simplify your data storage with AWS S3. What's inside An introduction to the map and reduce paradigm Parallelization with the multiprocessing module and pathos framework Hadoop and Spark for distributed computing Running AWS jobs to process large datasets About the reader For Python programmers who need to work faster with more data. About the author J. T. Wolohan is a lead data scientist at Booz Allen Hamilton, and a PhD researcher at Indiana University, Bloomington. Table of Contents: PART 1 1 ¦ Introduction 2 ¦ Accelerating large dataset work: Map and parallel computing 3 ¦ Function pipelines for mapping complex transformations 4 ¦ Processing large datasets with lazy workflows 5 ¦ Accumulation operations with reduce 6 ¦ Speeding up map and reduce with advanced parallelization PART 2 7 ¦ Processing truly big datasets with Hadoop and Spark 8 ¦ Best practices for large data with Apache Streaming and mrjob 9 ¦ PageRank with map and reduce in PySpark 10 ¦ Faster decision-making with machine learning and PySpark PART 3 11 ¦ Large datasets in the cloud with Amazon Web Services and S3 12 ¦ MapReduce in the cloud with Amazon’s Elastic MapReduce
Python Data Mining Quick Start Guide
DOWNLOAD
Author : Nathan Greeneltch
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-25
Python Data Mining Quick Start Guide written by Nathan Greeneltch and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-25 with Computers categories.
Explore the different data mining techniques using the libraries and packages offered by Python Key FeaturesGrasp the basics of data loading, cleaning, analysis, and visualizationUse the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data miningYour one-stop guide to build efficient data mining pipelines without going into too much theoryBook Description Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle. What you will learnExplore the methods for summarizing datasets and visualizing/plotting dataCollect and format data for analytical workAssign data points into groups and visualize clustering patternsLearn how to predict continuous and categorical outputs for dataClean, filter noise from, and reduce the dimensions of dataSerialize a data processing model using scikit-learn’s pipeline featureDeploy the data processing model using Python’s pickle moduleWho this book is for Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.
Mastering Python For Data Science
DOWNLOAD
Author : Samir Madhavan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-08-31
Mastering Python For Data Science written by Samir Madhavan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-31 with Computers categories.
Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.