Modern Data Mining With Python
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Modern Data Mining With Python
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Author : Dushyant Singh Sengar
language : en
Publisher: BPB Publications
Release Date : 2024-02-26
Modern Data Mining With Python written by Dushyant Singh Sengar and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-26 with Computers categories.
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
Fundamentals Of Big Data Data Mining And Machine Learning
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Author : Tarunika Chaudhari, Kamlesh W. Kelwade, K. Jasmine Mystica, M. Amshavalli
language : en
Publisher: RK Publication
Release Date : 2025-04-12
Fundamentals Of Big Data Data Mining And Machine Learning written by Tarunika Chaudhari, Kamlesh W. Kelwade, K. Jasmine Mystica, M. Amshavalli and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-12 with Computers categories.
This book offers a comprehensive introduction to Big Data, Data Mining, and Machine Learning, exploring foundational concepts, techniques, and real-world applications. It provides readers with essential tools for data analysis, pattern discovery, and predictive modeling, making it ideal for students, researchers, and professionals in data science and related fields.
Hands On Python The Ultimate Guide To Learning Programming Through Projects
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Author : Rachel Boyle
language : en
Publisher: DIGITAL BLUE INC.
Release Date : 2025-04-07
Hands On Python The Ultimate Guide To Learning Programming Through Projects written by Rachel Boyle and has been published by DIGITAL BLUE INC. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-07 with Computers categories.
Tired of staring at lines of code that seem like gibberish? Want to learn Python, but find traditional textbooks dry and overwhelming? This book offers a refreshing approach. Forget memorizing syntax; let's build! "Hands-On Python" takes you on a journey of discovery through exciting projects. No more passive learning – you'll be coding real-world applications from the very first chapter. Build a text-based adventure game, design a simple chatbot, or create a data visualization program. Each project is meticulously designed to reinforce Python concepts and unleash your creativity. This book is your guide, offering clear explanations, step-by-step instructions, and insightful tips for tackling challenges. Whether you're a complete beginner or have dabbled in programming, this book is tailored to help you succeed. Master the fundamentals of Python, explore data structures, delve into object-oriented programming, and much more. "Hands-On Python" is perfect for anyone eager to learn Python in a dynamic, engaging way. Whether you're a student, aspiring developer, or simply curious about coding, this book is your key to unlocking a world of possibilities. Embrace the joy of creation, build confidence in your programming skills, and unlock the power of Python. Ready to code? Let's get started!
Python For Beginners
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Author : Mark Matthes
language : en
Publisher: Phoenix Lab
Release Date :
Python For Beginners written by Mark Matthes and has been published by Phoenix Lab this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Are you new to software development? Are you curious about learning what artificial intelligence is? Do you want to master the Phyton programming language? Do You want to Learn Computers for Beginners? Well, this book is your best choice! There may be a lot of different languages that you can work with when it comes to the coding that you would like to work with, but none are going to provide you with the benefits that you are working with. This language is so popular and used so often that there are a few different operating systems that already have some version of Python found on them for you to use. This can make it easier to get some of the coding done that you would like, and will ensure that you will get the best benefits out of it in no time. ★★★This book covers:★★★ ★ What Is Python and His History and Why Learn Python ★ Getting Started with Python ★ Variables and Operators ★ Basic Operators ★ Data Types in Python ★ Functions and Modules ★ Defining Your Functions ★ Working with Your Module ★ Working with Files ★ Using A for Loop to Write and Read Text Files And so much more!! The Python language is more natural to read: If you take a look through some of the codes that we have later on in this guidebook, you will find that this is an easy task to read through some of the different parts of the law. Even if you have not been able to work with this language before, you will still be able to look at some of the systems and notice that you recognize the parts as well. The program is open source. This means that you won’t have to worry about someone taking over the code and ruining it. It also means that the original Python is free and available to anyone who wants to download it. This guidebook is going to take the Python language to the next level and look at some of the more advanced features that you can enjoy with this kind of writing, but when you look at some of the codes, even some of these that are more advanced than what you may have worked with in the past, you will find that it is easy to write some codes that have a lot of power, and even easy to complete your projects. If you are curious about this world, THEN CLICK TO GET YOUR COPY NOW!
Mining The Social Web
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Author : Matthew A. Russell
language : en
Publisher: O'Reilly Media
Release Date : 2018-12-04
Mining The Social Web written by Matthew A. Russell and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-04 with Computers categories.
Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers. In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter. Get a straightforward synopsis of the social web landscape Use Docker to easily run each chapter’s example code, packaged as a Jupyter notebook Adapt and contribute to the code’s open source GitHub repository Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition Build beautiful data visualizations with Python and JavaScript toolkits
Modern Data Science With R
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Author : Benjamin S. Baumer
language : en
Publisher: CRC Press
Release Date : 2021-04-13
Modern Data Science With R written by Benjamin S. Baumer and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-13 with Business & Economics categories.
From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
Pattern Recognition Machine Learning Ml Using Python
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Author : Dr. G. Prabaharan
language : en
Publisher: RK Publication
Release Date : 2024-05-28
Pattern Recognition Machine Learning Ml Using Python written by Dr. G. Prabaharan and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Computers categories.
Pattern Recognition & Machine Learning Using Python to understanding the fundamentals of pattern recognition and machine learning, with a hands-on approach using Python. This bridges theoretical concepts with practical applications, covering algorithms, data preprocessing, and model evaluation. It includes topics such as supervised and unsupervised learning, feature selection, and deep learning techniques. Ideal for students, researchers, and professionals, the emphasizes real-world examples and Python implementations to enhance learning and skill development in data-driven problem-solving.
Representation Learning
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Author : Nada Lavrač
language : en
Publisher: Springer Nature
Release Date : 2021-07-10
Representation Learning written by Nada Lavrač and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-10 with Computers categories.
This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions.
Contemporary Machine Learning Methods Harnessing Scikit Learn And Tensorflow
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-03
Contemporary Machine Learning Methods Harnessing Scikit Learn And Tensorflow written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
"Contemporary Machine Learning Methods: Harnessing Scikit-Learn and TensorFlow" is an indispensable resource for data scientists and machine learning practitioners eager to sharpen their skills and stay at the forefront of technology. This book offers a comprehensive exploration of modern machine learning methodologies, encompassing innovative regression and classification techniques, along with complex neural network architectures using TensorFlow. Explore practical implementations and real-world examples that demystify intricate concepts like unsupervised learning, deep learning optimizations, natural language processing, and feature engineering with clarity. Each chapter serves as a step-by-step guide to applying these contemporary methods, complete with code samples and thorough explanations. Whether you're a professional aiming to deploy machine learning solutions at an enterprise level, an academic researcher investigating computational innovations, or a postgraduate student interested in cutting-edge AI, this book equips you with the insights, tools, and expertise needed to effectively leverage machine learning technologies. Master the nuances of machine learning with "Contemporary Machine Learning Methods: Harnessing Scikit-Learn and TensorFlow" and convert data into impactful knowledge.
Data Mining And Exploration
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Author : Chong Ho Alex Yu
language : en
Publisher: CRC Press
Release Date : 2022-10-27
Data Mining And Exploration written by Chong Ho Alex Yu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-27 with Business & Economics categories.
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.