Modern Data Mining With Python

Download Modern Data Mining With Python full books in PDF, epub, and Kindle. Read online Modern Data Mining With Python ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Modern Data Mining with Python

Author : Dushyant Singh Sengar
Publisher : BPB Publications
ISBN 13 : 9355519141
Total Pages : 471 pages
Book Rating : 4.3/5 (555 download)

DOWNLOAD NOW!

Book Synopsis Modern Data Mining with Python by : Dushyant Singh Sengar

Download or read book Modern Data Mining with Python written by Dushyant Singh Sengar and published by BPB Publications. This book was released on 2024-02-26 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data miner’s survival kit for explainable, effective, and efficient algorithms enabling responsible decision-making KEY FEATURES ● Accessible, and case-based exploration of the most effective data mining techniques in Python. ● An indispensable guide for utilizing AI potential responsibly. ● Actionable insights on modeling techniques, deployment technologies, business needs, and the art of data science, for risk mitigation and better business outcomes. DESCRIPTION "Modern Data Mining with Python" is a guidebook for responsibly implementing data mining techniques that involve collecting, storing, and analyzing large amounts of structured and unstructured data to extract useful insights and patterns. Enter into the world of data mining and machine learning. Use insights from various data sources, from social media to credit card transactions. Master statistical tools, explore data trends, and patterns. Understand decision trees and artificial neural networks (ANNs). Manage high-dimensional data with dimensionality reduction. Explore binary classification with logistic regression. Spot concealed patterns with unsupervised learning. Analyze text with recurrent neural networks (RNNs) and visuals with convolutional neural networks (CNNs). Ensure model compliance with regulatory standards. After reading this book, readers will be equipped with the skills and knowledge necessary to use Python for data mining and analysis in an industry set-up. They will be able to analyze and implement algorithms on large structured and unstructured datasets. WHAT YOU WILL LEARN ● Explore the data mining spectrum ranging from data exploration and statistics. ● Gain hands-on experience applying modern algorithms to real-world problems in the financial industry. ● Develop an understanding of various risks associated with model usage in regulated industries. ● Gain knowledge about best practices and regulatory guidelines to mitigate model usage-related risk in key banking areas. ● Develop and deploy risk-mitigated algorithms on self-serve ModelOps platforms. WHO THIS BOOK IS FOR This book is for a wide range of early career professionals and students interested in data mining or data science with a financial services industry focus. Senior industry professionals, and educators, trying to implement data mining algorithms can benefit as well. TABLE OF CONTENTS 1. Understanding Data Mining in a Nutshell 2. Basic Statistics and Exploratory Data Analysis 3. Digging into Linear Regression 4. Exploring Logistic Regression 5. Decision Trees with Bagging and Boosting 6. Support Vector Machines and K-Nearest Neighbors 7. Putting Dimensionality Reduction into Action 8. Beginning with Unsupervised Models 9. Structured Data Classification using Artificial Neural Networks 10. Language Modeling with Recurrent Neural Networks 11. Image Processing with Convolutional Neural Networks 12. Understanding Model Risk Management for Data Mining Models 13. Adopting ModelOps to Manage Model Risk

Mastering Data Mining with Python - Find Patterns Hidden in Your Data

Author : Megan Squire
Publisher :
ISBN 13 : 9781785889950
Total Pages : 268 pages
Book Rating : 4.8/5 (899 download)

DOWNLOAD NOW!

Book Synopsis Mastering Data Mining with Python - Find Patterns Hidden in Your Data by : Megan Squire

Download or read book Mastering Data Mining with Python - Find Patterns Hidden in Your Data written by Megan Squire and published by . This book was released on 2016-08-29 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to create more powerful data mining applications with this comprehensive Python guide to advance data analytics techniquesAbout 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 librariesWho This Book Is ForThis 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 DetailData 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.

Learning Data Mining with Python

Author : Robert Layton
Publisher : Packt Publishing Ltd
ISBN 13 : 1784391204
Total Pages : 344 pages
Book Rating : 4.7/5 (843 download)

DOWNLOAD NOW!

Book Synopsis Learning Data Mining with Python by : Robert Layton

Download or read book Learning Data Mining with Python written by Robert Layton and published by Packt Publishing Ltd. This book was released on 2015-07-29 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Megan Squire
Publisher : Packt Publishing Ltd
ISBN 13 : 178588591X
Total Pages : 269 pages
Book Rating : 4.7/5 (858 download)

DOWNLOAD NOW!

Book Synopsis Mastering Data Mining with Python – Find patterns hidden in your data by : Megan Squire

Download or read book Mastering Data Mining with Python – Find patterns hidden in your data written by Megan Squire and published by Packt Publishing Ltd. This book was released on 2016-08-29 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Python and R for the Modern Data Scientist

Author : Rick J. Scavetta
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492093351
Total Pages : 198 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Python and R for the Modern Data Scientist by : Rick J. Scavetta

Download or read book Python and R for the Modern Data Scientist written by Rick J. Scavetta and published by "O'Reilly Media, Inc.". This book was released on 2021-06-22 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together

Data Mining for Business Analytics

Author : Galit Shmueli
Publisher : John Wiley & Sons
ISBN 13 : 111954985X
Total Pages : 608 pages
Book Rating : 4.1/5 (195 download)

DOWNLOAD NOW!

Book Synopsis Data Mining for Business Analytics by : Galit Shmueli

Download or read book Data Mining for Business Analytics written by Galit Shmueli and published by John Wiley & Sons. This book was released on 2019-10-14 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Data Mining for Business Analytics

Author : Galit Shmueli
Publisher : John Wiley & Sons
ISBN 13 : 1119549841
Total Pages : 610 pages
Book Rating : 4.1/5 (195 download)

DOWNLOAD NOW!

Book Synopsis Data Mining for Business Analytics by : Galit Shmueli

Download or read book Data Mining for Business Analytics written by Galit Shmueli and published by John Wiley & Sons. This book was released on 2019-11-05 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Web Scraping with Python

Author : Ryan Mitchell
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491910259
Total Pages : 339 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!

Book Synopsis Web Scraping with Python by : Ryan Mitchell

Download or read book Web Scraping with Python written by Ryan Mitchell and published by "O'Reilly Media, Inc.". This book was released on 2015-06-15 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn web scraping and crawling techniques to access unlimited data from any web source in any format. With this practical guide, you’ll learn how to use Python scripts and web APIs to gather and process data from thousands—or even millions—of web pages at once. Ideal for programmers, security professionals, and web administrators familiar with Python, this book not only teaches basic web scraping mechanics, but also delves into more advanced topics, such as analyzing raw data or using scrapers for frontend website testing. Code samples are available to help you understand the concepts in practice. Learn how to parse complicated HTML pages Traverse multiple pages and sites Get a general overview of APIs and how they work Learn several methods for storing the data you scrape Download, read, and extract data from documents Use tools and techniques to clean badly formatted data Read and write natural languages Crawl through forms and logins Understand how to scrape JavaScript Learn image processing and text recognition

Data Mining and Exploration

Author : Chong Ho Alex Yu
Publisher : CRC Press
ISBN 13 : 1000777790
Total Pages : 290 pages
Book Rating : 4.0/5 (7 download)

DOWNLOAD NOW!

Book Synopsis Data Mining and Exploration by : Chong Ho Alex Yu

Download or read book Data Mining and Exploration written by Chong Ho Alex Yu and published by CRC Press. This book was released on 2022-10-27 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.

Statistics, Data Mining, and Machine Learning in Astronomy

Author : Željko Ivezić
Publisher : Princeton University Press
ISBN 13 : 0691151687
Total Pages : 550 pages
Book Rating : 4.6/5 (911 download)

DOWNLOAD NOW!

Book Synopsis Statistics, Data Mining, and Machine Learning in Astronomy by : Željko Ivezić

Download or read book Statistics, Data Mining, and Machine Learning in Astronomy written by Željko Ivezić and published by Princeton University Press. This book was released on 2014-01-12 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

Data Mining with Python

Author : Di Wu
Publisher : CRC Press
ISBN 13 : 9781032598901
Total Pages : 0 pages
Book Rating : 4.5/5 (989 download)

DOWNLOAD NOW!

Book Synopsis Data Mining with Python by : Di Wu

Download or read book Data Mining with Python written by Di Wu and published by CRC Press. This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge.

Data Analysis with Python

Author : David Taieb
Publisher : Packt Publishing Ltd
ISBN 13 : 1789958199
Total Pages : 491 pages
Book Rating : 4.7/5 (899 download)

DOWNLOAD NOW!

Book Synopsis Data Analysis with Python by : David Taieb

Download or read book Data Analysis with Python written by David Taieb and published by Packt Publishing Ltd. This book was released on 2018-12-31 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key FeaturesBridge your data analysis with the power of programming, complex algorithms, and AIUse Python and its extensive libraries to power your way to new levels of data insightWork with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time seriesExplore this modern approach across with key industry case studies and hands-on projectsBook Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learnA new toolset that has been carefully crafted to meet for your data analysis challengesFull and detailed case studies of the toolset across several of today’s key industry contextsBecome super productive with a new toolset across Python and Jupyter NotebookLook into the future of data science and which directions to develop your skills nextWho this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.

Python Data Mining Quick Start Guide

Author : Nathan Greeneltch
Publisher : Packt Publishing Ltd
ISBN 13 : 1789806402
Total Pages : 181 pages
Book Rating : 4.7/5 (898 download)

DOWNLOAD NOW!

Book Synopsis Python Data Mining Quick Start Guide by : Nathan Greeneltch

Download or read book Python Data Mining Quick Start Guide written by Nathan Greeneltch and published by Packt Publishing Ltd. This book was released on 2019-04-25 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Modern Data Mining Algorithms in C++ and CUDA C

Author : Timothy Masters
Publisher : Apress
ISBN 13 : 1484259882
Total Pages : 233 pages
Book Rating : 4.4/5 (842 download)

DOWNLOAD NOW!

Book Synopsis Modern Data Mining Algorithms in C++ and CUDA C by : Timothy Masters

Download or read book Modern Data Mining Algorithms in C++ and CUDA C written by Timothy Masters and published by Apress. This book was released on 2020-06-05 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You’ll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are: Forward selection component analysis Local feature selection Linking features and a target with a hidden Markov modelImprovements on traditional stepwise selectionNominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will Learn Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set. Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods. Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets.Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is For Intermediate to advanced data science programmers and analysts.

Python Data Analysis

Author : Avinash Navlani
Publisher : Packt Publishing Ltd
ISBN 13 : 1789953456
Total Pages : 463 pages
Book Rating : 4.7/5 (899 download)

DOWNLOAD NOW!

Book Synopsis Python Data Analysis by : Avinash Navlani

Download or read book Python Data Analysis written by Avinash Navlani and published by Packt Publishing Ltd. This book was released on 2021-02-05 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key FeaturesPrepare and clean your data to use it for exploratory analysis, data manipulation, and data wranglingDiscover supervised, unsupervised, probabilistic, and Bayesian machine learning methodsGet to grips with graph processing and sentiment analysisBook Description Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data. What you will learnExplore data science and its various process modelsPerform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing valuesCreate interactive visualizations using Matplotlib, Seaborn, and BokehRetrieve, process, and store data in a wide range of formatsUnderstand data preprocessing and feature engineering using pandas and scikit-learnPerform time series analysis and signal processing using sunspot cycle dataAnalyze textual data and image data to perform advanced analysisGet up to speed with parallel computing using DaskWho this book is for This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.

Hands-On Data Science and Python Machine Learning

Author : Frank Kane
Publisher : Packt Publishing Ltd
ISBN 13 : 1787280225
Total Pages : 420 pages
Book Rating : 4.7/5 (872 download)

DOWNLOAD NOW!

Book Synopsis Hands-On Data Science and Python Machine Learning by : Frank Kane

Download or read book Hands-On Data Science and Python Machine Learning written by Frank Kane and published by Packt Publishing Ltd. This book was released on 2017-07-31 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Python for Probability, Statistics, and Machine Learning

Author : José Unpingco
Publisher : Springer Nature
ISBN 13 : 303104648X
Total Pages : 524 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Python for Probability, Statistics, and Machine Learning by : José Unpingco

Download or read book Python for Probability, Statistics, and Machine Learning written by José Unpingco and published by Springer Nature. This book was released on 2022-11-04 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.