Mastering Large Datasets With Python

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Mastering Large Datasets with Python

John Wolohan
Mastering Large Datasets with Python Book PDF

Author : John Wolohan
Publisher : Simon and Schuster
Page : 451 pages
File Size : 50,7 Mb
Release : 2020-01-15
Category : Computers
ISBN : 9781638350361

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Mastering Large Datasets with Python by John Wolohan Pdf

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

Mastering Large Datasets

J. T. Wolohan
Mastering Large Datasets Book PDF

Author : J. T. Wolohan
Publisher : Manning Publications
Page : 350 pages
File Size : 45,6 Mb
Release : 2020-01-06
Category : Electronic
ISBN : 1617296236

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Mastering Large Datasets by J. T. Wolohan Pdf

With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the toolz library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Fast Python

Tiago Antao
Fast Python Book PDF

Author : Tiago Antao
Publisher : Simon and Schuster
Page : 302 pages
File Size : 41,6 Mb
Release : 2023-07-04
Category : Computers
ISBN : 9781638356868

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Fast Python by Tiago Antao Pdf

Master Python techniques and libraries to reduce run times, efficiently handle huge datasets, and optimize execution for complex machine learning applications. Fast Python is a toolbox of techniques for high performance Python including: Writing efficient pure-Python code Optimizing the NumPy and pandas libraries Rewriting critical code in Cython Designing persistent data structures Tailoring code for different architectures Implementing Python GPU computing Fast Python is your guide to optimizing every part of your Python-based data analysis process, from the pure Python code you write to managing the resources of modern hardware and GPUs. You'll learn to rewrite inefficient data structures, improve underperforming code with multithreading, and simplify your datasets without sacrificing accuracy. Written for experienced practitioners, this book dives right into practical solutions for improving computation and storage efficiency. You'll experiment with fun and interesting examples such as rewriting games in Cython and implementing a MapReduce framework from scratch. Finally, you'll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working. About the Technology Face it. Slow code will kill a big data project. Fast pure-Python code, optimized libraries, and fully utilized multiprocessor hardware are the price of entry for machine learning and large-scale data analysis. What you need are reliable solutions that respond faster to computing requirements while using less resources, and saving money. About the Book Fast Python is a toolbox of techniques for speeding up Python, with an emphasis on big data applications. Following the clear examples and precisely articulated details, you’ll learn how to use common libraries like NumPy and pandas in more performant ways and transform data for efficient storage and I/O. More importantly, Fast Python takes a holistic approach to performance, so you’ll see how to optimize the whole system, from code to architecture. What’s Inside Rewriting critical code in Cython Designing persistent data structures Tailoring code for different architectures Implementing Python GPU computing About the Reader For intermediate Python programmers familiar with the basics of concurrency. About the Author Tiago Antão is one of the co-authors of Biopython, a major bioinformatics package written in Python. Table of Contents: PART 1 - FOUNDATIONAL APPROACHES 1 An urgent need for efficiency in data processing 2 Extracting maximum performance from built-in features 3 Concurrency, parallelism, and asynchronous processing 4 High-performance NumPy PART 2 - HARDWARE 5 Re-implementing critical code with Cython 6 Memory hierarchy, storage, and networking PART 3 - APPLICATIONS AND LIBRARIES FOR MODERN DATA PROCESSING 7 High-performance pandas and Apache Arrow 8 Storing big data PART 4 - ADVANCED TOPICS 9 Data analysis using GPU computing 10 Analyzing big data with Dask

Mastering Large Language Models with Python

Raj Arun R
Mastering Large Language Models with Python Book PDF

Author : Raj Arun R
Publisher : Orange Education Pvt Ltd
Page : 547 pages
File Size : 53,7 Mb
Release : 2024-04-12
Category : Computers
ISBN : 9788197081828

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Mastering Large Language Models with Python by Raj Arun R Pdf

A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index

Data Engineering with Python

Paul Crickard
Data Engineering with Python Book PDF

Author : Paul Crickard
Publisher : Packt Publishing Ltd
Page : 357 pages
File Size : 54,9 Mb
Release : 2020-10-23
Category : Computers
ISBN : 9781839212307

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Data Engineering with Python by Paul Crickard Pdf

Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.

Extending Power BI with Python and R

Luca Zavarella,Francesca Lazzeri
Extending Power BI with Python and R Book PDF

Author : Luca Zavarella,Francesca Lazzeri
Publisher : Packt Publishing Ltd
Page : 559 pages
File Size : 51,7 Mb
Release : 2021-11-26
Category : Computers
ISBN : 9781801076678

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Extending Power BI with Python and R by Luca Zavarella,Francesca Lazzeri Pdf

Perform more advanced analysis and manipulation of your data beyond what Power BI can do to unlock valuable insights using Python and R Key FeaturesGet the most out of Python and R with Power BI by implementing non-trivial codeLeverage the toolset of Python and R chunks to inject scripts into your Power BI dashboardsImplement new techniques for ingesting, enriching, and visualizing data with Python and R in Power BIBook Description Python and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages. You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model. By the end of this book, you'll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R. What you will learnDiscover best practices for using Python and R in Power BI productsUse Python and R to perform complex data manipulations in Power BIApply data anonymization and data pseudonymization in Power BILog data and load large datasets in Power BI using Python and REnrich your Power BI dashboards using external APIs and machine learning modelsExtract insights from your data using linear optimization and other algorithmsHandle outliers and missing values for multivariate and time-series dataCreate any visualization, as complex as you want, using R scriptsWho this book is for This book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.

Spark in Action, Second Edition

Jean-Georges Perrin
Spark in Action Second Edition Book PDF

Author : Jean-Georges Perrin
Publisher : Manning Publications
Page : 574 pages
File Size : 46,5 Mb
Release : 2020-06-02
Category : Computers
ISBN : 9781617295522

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Spark in Action, Second Edition by Jean-Georges Perrin Pdf

Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Table of Contents PART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES 1 So, what is Spark, anyway? 2 Architecture and flow 3 The majestic role of the dataframe 4 Fundamentally lazy 5 Building a simple app for deployment 6 Deploying your simple app PART 2 - INGESTION 7 Ingestion from files 8 Ingestion from databases 9 Advanced ingestion: finding data sources and building your own 10 Ingestion through structured streaming PART 3 - TRANSFORMING YOUR DATA 11 Working with SQL 12 Transforming your data 13 Transforming entire documents 14 Extending transformations with user-defined functions 15 Aggregating your data PART 4 - GOING FURTHER 16 Cache and checkpoint: Enhancing Spark’s performances 17 Exporting data and building full data pipelines 18 Exploring deployment

Data Science with Python

Rohan Chopra,Aaron England,Mohamed Noordeen Alaudeen
Data Science with Python Book PDF

Author : Rohan Chopra,Aaron England,Mohamed Noordeen Alaudeen
Publisher : Packt Publishing Ltd
Page : 426 pages
File Size : 53,9 Mb
Release : 2019-07-19
Category : Computers
ISBN : 9781838552169

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Data Science with Python by Rohan Chopra,Aaron England,Mohamed Noordeen Alaudeen Pdf

Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event. Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasetsBook Description Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book. What you will learnPre-process data to make it ready to use for machine learningCreate data visualizations with MatplotlibUse scikit-learn to perform dimension reduction using principal component analysis (PCA)Solve classification and regression problemsGet predictions using the XGBoost libraryProcess images and create machine learning models to decode them Process human language for prediction and classificationUse TensorBoard to monitor training metrics in real timeFind the best hyperparameters for your model with AutoMLWho this book is for Data Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.

Mastering Large Language Models

Sanket Subhash Khandare
Mastering Large Language Models Book PDF

Author : Sanket Subhash Khandare
Publisher : BPB Publications
Page : 465 pages
File Size : 50,8 Mb
Release : 2024-03-12
Category : Computers
ISBN : 9789355519658

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Mastering Large Language Models by Sanket Subhash Khandare Pdf

Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact

Mastering Python

Rick van Hattem
Mastering Python Book PDF

Author : Rick van Hattem
Publisher : Packt Publishing Ltd
Page : 486 pages
File Size : 45,6 Mb
Release : 2016-04-29
Category : Computers
ISBN : 9781785289132

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Mastering Python by Rick van Hattem Pdf

Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers About This Book Become familiar with the most important and advanced parts of the Python code style Learn the trickier aspects of Python and put it in a structured context for deeper understanding of the language Offers an expert's-eye overview of how these advanced tasks fit together in Python as a whole along with practical examples Who This Book Is For Almost anyone can learn to write working script and create high quality code but they might lack a structured understanding of what it means to be 'Pythonic'. If you are a Python programmer who wants to code efficiently by getting the syntax and usage of a few intricate Python techniques exactly right, this book is for you. What You Will Learn Create a virtualenv and start a new project Understand how and when to use the functional programming paradigm Get familiar with the different ways the decorators can be written in Understand the power of generators and coroutines without digressing into lambda calculus Create metaclasses and how it makes working with Python far easier Generate HTML documentation out of documents and code using Sphinx Learn how to track and optimize application performance, both memory and cpu Use the multiprocessing library, not just locally but also across multiple machines Get a basic understanding of packaging and creating your own libraries/applications In Detail Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges. Style and Approach This book is a comprehensive guide that covers advanced features of the Python language, and communicate them with an authoritative understanding of the underlying rationale for how, when, and why to use them.

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

Megan Squire
Mastering Data Mining with Python Find Patterns Hidden in Your Data Book PDF

Author : Megan Squire
Publisher : Unknown
Page : 268 pages
File Size : 42,6 Mb
Release : 2016-08-29
Category : Electronic
ISBN : 1785889958

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Mastering Data Mining with Python - Find Patterns Hidden in Your Data by Megan Squire Pdf

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.

Mastering Quantitative Research

Cybellium Ltd
Mastering Quantitative Research Book PDF

Author : Cybellium Ltd
Publisher : Cybellium Ltd
Page : 318 pages
File Size : 48,8 Mb
Release : 2024-06-28
Category : Computers
ISBN : 9798867654009

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Mastering Quantitative Research by Cybellium Ltd Pdf

Unlock the Power of Data and Precision in Your Research with "Mastering Quantitative Research" In today's data-driven world, the ability to conduct rigorous and insightful quantitative research is a skill that can set you apart in academia, business, and beyond. "Mastering Quantitative Research" is your definitive guide to navigating the complex landscape of quantitative analysis, providing you with the tools and knowledge to unlock the full potential of your research. A Comprehensive Guide to Quantitative Research Quantitative research is the cornerstone of evidence-based decision-making. In "Mastering Quantitative Research," you will embark on a journey through the fundamental principles, methodologies, and advanced techniques that underpin quantitative analysis. This book is designed to demystify quantitative research and equip you with the skills necessary to design, execute, and interpret quantitative studies. What You Will Discover · Foundations of Quantitative Research: Explore the essential concepts and theories that form the basis of quantitative research, ensuring a strong understanding of the fundamentals. · Research Design and Data Collection: Learn how to design research studies, select appropriate data sources, and collect high-quality data. · Statistical Analysis: Master the use of statistical tools and techniques to analyze data, draw meaningful conclusions, and make data-driven decisions. · Data Visualization: Understand the art of presenting your findings effectively through compelling data visualization techniques. · Advanced Topics: Dive into advanced topics such as multivariate analysis, regression modeling, and hypothesis testing, taking your quantitative research skills to the next level. · Real-World Applications: Gain insights into how quantitative research is applied in various fields, including social sciences, business, healthcare, and more. Why "Mastering Quantitative Research" Is Essential · Comprehensive Coverage: This book provides a comprehensive and accessible introduction to quantitative research, making it suitable for both beginners and those looking to deepen their knowledge. · Practical Guidance: Loaded with real-world examples, case studies, and hands-on exercises, "Mastering Quantitative Research" offers practical advice that can be immediately applied to your research projects. · Expert Insights: Benefit from the expertise of experienced researchers who share their knowledge and best practices to help you succeed in your quantitative research endeavors. · Empower Your Decision-Making: Whether you are a student, researcher, or professional, the skills you gain from this book will empower you to make data-driven decisions that can have a profound impact on your field of study or work. · Stay Ahead: In a world where data is king, staying ahead in your field requires proficiency in quantitative research. This book ensures you are well-prepared for the challenges and opportunities of the data-driven future. Your Journey to Quantitative Research Mastery Begins Here "Mastering Quantitative Research" is your gateway to becoming a proficient and confident quantitative researcher. Whether you are conducting research for academia, business, policy, or personal interest, this book will equip you with the tools and knowledge you need to excel in the world of data and analysis. Don't miss out on the opportunity to enhance your research skills and make a significant impact in your chosen field. "Mastering Quantitative Research" is your key to unlocking the potential of data-driven decision-making. Start your journey to quantitative research mastery today! © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Parallel Python with Dask

Tim Peters
Parallel Python with Dask Book PDF

Author : Tim Peters
Publisher : GitforGits
Page : 172 pages
File Size : 48,9 Mb
Release : 2023-10-19
Category : Computers
ISBN : 9788119177462

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Parallel Python with Dask by Tim Peters Pdf

Unlock the Power of Parallel Python with Dask: A Perfect Learning Guide for Aspiring Data Scientists Dask has revolutionized parallel computing for Python, empowering data scientists to accelerate their workflows. This comprehensive guide unravels the intricacies of Dask to help you harness its capabilities for machine learning and data analysis. Across 10 chapters, you'll master Dask's fundamentals, architecture, and integration with Python's scientific computing ecosystem. Step-by-step tutorials demonstrate parallel mapping, task scheduling, and leveraging Dask arrays for NumPy workloads. You'll discover how Dask seamlessly scales Pandas, Scikit-Learn, PyTorch, and other libraries for large datasets. Dedicated chapters explore scaling regression, classification, hyperparameter tuning, feature engineering, and more with clear examples. You'll also learn to tap into the power of GPUs with Dask, RAPIDS, and Google JAX for orders of magnitude speedups. This book places special emphasis on practical use cases related to scalability and distributed computing. You'll learn Dask patterns for cluster computing, managing resources efficiently, and robust data pipelines. The advanced chapters on DaskML and deep learning showcase how to build scalable models with PyTorch and TensorFlow. With this book, you'll gain practical skills to: Accelerate Python workloads with parallel mapping and task scheduling Speed up NumPy, Pandas, Scikit-Learn, PyTorch, and other libraries Build scalable machine learning pipelines for large datasets Leverage GPUs efficiently via Dask, RAPIDS and JAX Manage Dask clusters and workflows for distributed computing Streamline deep learning models with DaskML and DL frameworks Packed with hands-on examples and expert insights, this book provides the complete toolkit to harness Dask's capabilities. It will empower Python programmers, data scientists, and machine learning engineers to achieve faster workflows and operationalize parallel computing. Table of Content Introduction to Dask Dask Fundamentals Batch Data Parallel Processing with Dask Distributed Systems and Dask Advanced Dask: APIs and Building Blocks Dask with Pandas Dask with Scikit-learn Dask and PyTorch Dask with GPUs Scaling Machine Learning Projects with Dask

Logging in Action

Phil Wilkins
Logging in Action Book PDF

Author : Phil Wilkins
Publisher : Simon and Schuster
Page : 390 pages
File Size : 54,6 Mb
Release : 2022-03-29
Category : Computers
ISBN : 9781617298356

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Logging in Action by Phil Wilkins Pdf

Logging in Action teaches you to record and analyze application and infrastructure data using Fluentd. Using clear, relevant examples, it shows you exactly how to transform raw system data into a unified stream of actionable information. You'll discover how logging configuration impacts the way your system functions and set up Fluentd to handle data from legacy IT environments, local data centers, and massive Kubernetes-driven distributed systems. You'll even learn how to implement complex log parsing with RegEx and output events to MongoDB and Slack.

Mastering Python

Rick van Hattem
Mastering Python Book PDF

Author : Rick van Hattem
Publisher : Packt Publishing Ltd
Page : 711 pages
File Size : 49,8 Mb
Release : 2022-05-20
Category : Computers
ISBN : 9781800202108

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Mastering Python by Rick van Hattem Pdf

Use advanced features of Python to write high-quality, readable code and packages Key Features Extensively updated for Python 3.10 with new chapters on design patterns, scientific programming, machine learning, and interactive Python Shape your scripts using key concepts like concurrency, performance optimization, asyncio, and multiprocessing Learn how advanced Python features fit together to produce maintainable code Book Description Even if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python's capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code's performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challenges What you will learn Write beautiful Pythonic code and avoid common Python coding mistakes Apply the power of decorators, generators, coroutines, and metaclasses Use different testing systems like pytest, unittest, and doctest Track and optimize application performance for both memory and CPU usage Debug your applications with PDB, Werkzeug, and faulthandler Improve your performance through asyncio, multiprocessing, and distributed computing Explore popular libraries like Dask, NumPy, SciPy, pandas, TensorFlow, and scikit-learn Extend Python's capabilities with C/C++ libraries and system calls Who this book is for This book will benefit more experienced Python programmers who wish to upskill, serving as a reference for best practices and some of the more intricate Python techniques. Even if you have been using Python for years, chances are that you haven't yet encountered every topic discussed in this book. A good understanding of Python programming is necessary