Anaconda Distribution Essentials
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Anaconda Distribution Essentials
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-17
Anaconda Distribution Essentials written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.
"Anaconda Distribution Essentials" "Anaconda Distribution Essentials" is a comprehensive, authoritative guide devoted to mastering the Anaconda platform—the leading open-source distribution for scientific computing and data science. Delving deeply into the architecture and ecosystem, the book articulates the philosophical underpinnings and technical intricacies that have shaped Anaconda’s evolution, from its core components like Conda and Navigator to its seamless integration with foundational scientific libraries such as NumPy, SciPy, and Pandas. Readers will gain a thorough understanding of the distribution’s robust channel infrastructure, cross-language support, and its pivotal role in both open source communities and enterprise environments. The guide moves systematically from foundational concepts to advanced practices, empowering professionals to navigate complex installation strategies, manage scalable deployments, and craft reproducible, collaborative environments for both individual and large-scale organizational needs. Through expert treatment of Conda environment management, package customization, secure collaboration, and best-in-class approaches for data science workflow optimization, the book provides actionable techniques for addressing real-world challenges. Topics such as containerized deployments, integration with CI/CD pipelines, and performance tuning for computational scalability are presented with clarity, making the content accessible yet thorough for IT leaders, data scientists, and system administrators alike. With in-depth chapters on security, compliance, and environment isolation, "Anaconda Distribution Essentials" also addresses the increasing demands for vulnerability management and regulatory conformance in today's data-centric enterprises. The work culminates in insightful case studies, forward-looking analyses, and interviews with core Anaconda engineers—offering critical perspectives on large-scale adoption, innovation, and the evolving future of data science infrastructure. Both a reference and a roadmap, this book is an indispensable resource for organizations and practitioners seeking to maximize the impact and reliability of their analytics platforms.
Python Data Science Essentials
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Author : Alberto Boschetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-09-28
Python Data Science Essentials written by Alberto Boschetti 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 2018-09-28 with Computers categories.
Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
Machine Learning Essentials And Applications
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Author : Mrs. N. Jayasri
language : en
Publisher: RK Publication
Release Date : 2024-07-27
Machine Learning Essentials And Applications written by Mrs. N. Jayasri and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-27 with Computers categories.
Machine Learning Essentials and Applications a comprehensive of machine learning's core principles, methodologies, and real-world applications. This book is designed for both beginners and professionals, covering essential topics like supervised and unsupervised learning, neural networks, and deep learning. With clear explanations and practical examples, it connects theory to practice, showcasing machine learning’s impact across industries such as healthcare, finance, and technology. Ideal for readers seeking foundational knowledge and insights into the transformative potential of machine learning in various fields.
Learn Data Science From Scratch
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Author : Pratheerth Padman
language : en
Publisher: BPB Publications
Release Date : 2024-02-15
Learn Data Science From Scratch written by Pratheerth Padman and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-15 with Computers categories.
Turn raw data into meaningful solutions KEY FEATURES ● Complete guide to master data science basics. ● Practical and hands-on examples in ML, deep learning, and NLP. ● Drive innovation and improve decision making through the power of data. DESCRIPTION Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions. This book provides a thorough exploration of essential data science concepts, tools, and techniques. Starting with the fundamentals of data science, you will progress through data collection, web scraping, data exploration and visualization, and data cleaning and pre-processing. You will build the required foundation in statistics and probability before diving into machine learning algorithms, deep learning, natural language processing, recommender systems, and data storage systems. With hands-on examples and practical advice, each chapter offers valuable insights and key takeaways, empowering you to master the art of data-driven decision making. By the end of this book, you will be well-equipped with the essential skills and knowledge to navigate the exciting world of data science. You will be able to collect, analyze, and interpret data, build and evaluate machine learning models, and effectively communicate your findings, making you a valuable asset in any data-driven environment. WHAT YOU WILL LEARN ● Master key data science tools like Python, NumPy, Pandas, and more. ● Build a strong foundation in statistics and probability for data analysis. ● Learn and apply machine learning, from regression to deep learning. ● Expertise in NLP and recommender systems for advanced analytics. ● End-to-end data project from data collection to model deployment, with planning and execution. WHO THIS BOOK IS FOR This book is ideal for beginners with a basic understanding of programming, particularly in Python, and a foundational knowledge of mathematics. It is well-suited for aspiring data scientists and analysts. TABLE OF CONTENTS 1. Unraveling the Data Science Universe: An Introduction 2. Essential Python Libraries and Tools for Data Science 3. Statistics and Probability Essentials for Data Science 4. Data Mining Expedition: Web Scraping and Data Collection Techniques 5. Painting with Data: Exploration and Visualization 6. Data Alchemy: Cleaning and Preprocessing Raw Data 7. Machine Learning Magic: An Introduction to Predictive Modeling 8. Exploring Regression: Linear, Logistic, and Advanced Methods 9. Unveiling Patterns with k-Nearest Neighbors and Naïve Bayes 10. Exploring Tree-Based Models: Decision Trees to Gradient Boosting 11. Support Vector Machines: Simplifying Complexity 12. Dimensionality Reduction: From PCA to Advanced Methods 13. Unlocking Unsupervised Learning 14. The Essence of Neural Networks and Deep Learning 15. Word Play: Text Analytics and Natural Language Processing 16. Crafting Recommender Systems 17. Data Storage Mastery: Databases and Efficient Data Management 18. Data Science in Action: A Comprehensive End-to-end Project
Data Science Basics
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Author : Zoe Codewell
language : en
Publisher: Publifye AS
Release Date : 2025-01-13
Data Science Basics written by Zoe Codewell and has been published by Publifye AS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-13 with Computers categories.
Data Science Basics offers a comprehensive introduction to transforming raw data into actionable insights, structured around three fundamental pillars: exploratory data analysis, statistical visualization, and machine learning applications. This practical guide stands out for its problem-first approach, introducing technical concepts as solutions to real-world analytical challenges rather than abstract theories, making it particularly valuable for aspiring analysts and business professionals. The book's progression is thoughtfully organized across four main sections, beginning with essential data manipulation techniques and advancing through visualization methods, statistical analysis, and machine learning implementations. What sets this resource apart is its emphasis on combining technical proficiency with critical thinking and clear communication, illustrated through diverse case studies from business, healthcare, and scientific research. The content bridges theoretical understanding with practical application through hands-on exercises using Python and R programming languages. Throughout the text, readers encounter real-world datasets and practical examples that demonstrate the universal applicability of data science methods. The book maintains accessibility while covering complex topics, using clear explanations and relevant examples to build a solid foundation in data literacy. By incorporating interactive exercises and end-of-chapter projects, it enables readers to develop practical problem-solving skills while mastering essential concepts in statistical analysis, data visualization, and machine learning fundamentals.
Data Science Essentials In Python
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Author : Dmitry Zinoviev
language : en
Publisher: The Pragmatic Programmers LLC
Release Date : 2016-08-10
Data Science Essentials In Python written by Dmitry Zinoviev and has been published by The Pragmatic Programmers LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-10 with Business & Economics categories.
Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.
Python 3 Fundamentals
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2024-10-29
Python 3 Fundamentals written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-29 with Computers categories.
"Python 3 Fundamentals: A Complete Guide for Modern Programmers" is an authoritative resource designed to equip both novice and experienced developers with a thorough understanding of Python programming. Written by an expert in computer science and software engineering, this comprehensive guide navigates through essential Python topics, providing readers with a definitive pathway to mastery. From setting up the Python environment and understanding variables and data types, to exploring control flow, functions, and data structures, every chapter is meticulously crafted to offer clear, insightful explanations alongside practical examples. Delving deeper, the book expands on advanced concepts such as object-oriented programming, exception handling, and file management, ensuring readers gain a solid foundation in developing scalable, efficient Python applications. With sections dedicated to leveraging Python's expansive libraries and frameworks, as well as integrating best practices for testing and debugging, this guide is not only a learning tool but also a valuable reference for creating robust, high-quality software. Whether you're building web applications, automating tasks, or embarking on data analysis, this guide empowers you with the skills needed to harness the full potential of Python in any domain.
Numpy Essentials
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Author : Leo (Liang-Huan) Chin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-04-28
Numpy Essentials written by Leo (Liang-Huan) Chin 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 2016-04-28 with Computers categories.
Boost your scientific and analytic capabilities in no time at all by discovering how to build real-world applications with NumPy About This Book Optimize your Python scripts with powerful NumPy modules Explore the vast opportunities to build outstanding scientific/ analytical modules by yourself Packed with rich examples to help you master NumPy arrays and universal functions Who This Book Is For If you are an experienced Python developer who intends to drive your numerical and scientific applications with NumPy, this book is for you. Prior experience or knowledge of working with the Python language is required. What You Will Learn Manipulate the key attributes and universal functions of NumPy Utilize matrix and mathematical computation using linear algebra modules Implement regression and curve fitting for models Perform time frequency / spectral density analysis using the Fourier Transform modules Collate with the distutils and setuptools modules used by other Python libraries Establish Cython with NumPy arrays Write extension modules for NumPy code using the C API Build sophisticated data structures using NumPy array with libraries such as Panda and Scikits In Detail In today's world of science and technology, it's all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need. This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier Transform; solving linear systems of equations, interpolation, extrapolation, regression, and curve fitting; and evaluating integrals and derivatives. We will also introduce you to using Cython with NumPy arrays and writing extension modules for NumPy code using the C API. This book will give you exposure to the vast NumPy library and help you build efficient, high-speed programs using a wide range of mathematical features. Style and approach This quick guide will help you get to grips with the nitty-gritties of NumPy using with practical programming examples. Each topic is explained in both theoretical and practical ways with hands-on examples providing you efficient way of learning and adequate knowledge to support your professional work.
Ipython Notebook Essentials
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Author : L. Felipe Martins
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-11-21
Ipython Notebook Essentials written by L. Felipe Martins 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 2014-11-21 with Computers categories.
If you are a professional, student, or educator who wants to learn to use IPython Notebook as a tool for technical and scientific computing, visualization, and data analysis, this is the book for you. This book will prove valuable for anyone that needs to do computations in an agile environment.
Python Machine Learning
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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-12
Python Machine Learning written by Sebastian Raschka 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 2019-12-12 with Computers categories.
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Master the frameworks, models, and techniques that enable machines to 'learn' from data Use scikit-learn for machine learning and TensorFlow for deep learning Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more Build and train neural networks, GANs, and other models Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.