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Python Machine Learning By Example
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Python Machine Learning By Example
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Jul 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (25)
eBook 526 pages
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Unlock machine learning best practices with real-world use cases
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  • 9781835082225 eBook $33.29
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Python Machine Learning By Example
Unlock machine learning best practices with real-world use cases
Description
The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide. *Email sign-up and proof of purchase required
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Jul 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
526 pages
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Author Yuxi (Hayden) Liu
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Generative AI with Python and PyTorch
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Generative AI with Python and PyTorch
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Mar 2025
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1)
eBook 454 pages
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Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications
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  • 9781835884454 eBook $39.59
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Generative AI with Python and PyTorch
Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications
Description
Become an expert in Generative AI through hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. This book is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable. From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence. You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models. Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI. *Email sign-up and proof of purchase required
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Mar 2025
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454 pages
AI Assistant AI Assistant
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Author Joseph Babcock
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Python for Algorithmic Trading Cookbook
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Aug 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (22)
eBook 406 pages
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Recipes for designing, building, and deploying algorithmic trading strategies with Python
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  • 9781835087763 eBook $43.19
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Python for Algorithmic Trading Cookbook
Recipes for designing, building, and deploying algorithmic trading strategies with Python
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Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading. Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details. By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.
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Aug 2024 406 pages
AI Assistant AI Assistant
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Author Jason Strimpel
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Causal Inference and Discovery in Python
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May 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (48)
eBook 466 pages
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Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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  • 9781804611739 eBook $39.59
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Causal Inference and Discovery in Python
Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
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Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.
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May 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
466 pages
AI Assistant AI Assistant
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Author Aleksander Molak
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Modern Time Series Forecasting with Python
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Oct 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1)
eBook 660 pages
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Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
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  • 9781835883198 eBook $42.29
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Modern Time Series Forecasting with Python
Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
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Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both. Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques. This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills. *Email sign-up and proof of purchase required
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Oct 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
660 pages
AI Assistant AI Assistant
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Author Manu Joseph
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Learn Python Programming
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Learn Python Programming
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Nov 2024
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (6)
eBook 620 pages
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A comprehensive, up-to-date, and definitive guide to learning Python
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  • 9781835882955 eBook $28.79
  • 9781835882948 Paperback $39.99
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Learn Python Programming
A comprehensive, up-to-date, and definitive guide to learning Python
Description
Learn Python Programming, Fourth Edition, provides a comprehensive, up-to-date introduction to Python programming, covering fundamental concepts and practical applications. This edition has been meticulously updated to include the latest features from Python versions 3.9 to 3.12, new chapters on type hinting and CLI applications, and updated examples reflecting modern Python web development practices. This Python book empowers you to take ownership of writing your software and become independent in fetching the resources you need. By the end of this book, you will have a clear idea of where to go and how to build on what you have learned from the book. Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned. This Python book offers a clear and practical guide to mastering Python and applying it effectively in various domains, such as data science, web development, and automation.
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Nov 2024
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
620 pages
AI Assistant AI Assistant
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Author Fabrizio Romano
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Bayesian Analysis with Python
Bayesian Analysis with Python
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Jan 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (19)
eBook 358 pages
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A practical guide to probabilistic modeling
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Hardcover eBook
  • 9781805125419 eBook $35.99
  • 9781805127161 Paperback $49.99
  • 9781836644835 Hardcover $69.99
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Bayesian Analysis with Python
A practical guide to probabilistic modeling
Description
The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection. In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets. By the end of this book, you’ll understand probabilistic modeling and be able to design and implement Bayesian models for data science, with a strong foundation for more advanced study. *Email sign-up and proof of purchase required
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Jan 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
358 pages
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
Authors
Author Osvaldo Martin
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Hardcover $69.99
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Python for Finance Cookbook
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Python for Finance Cookbook
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Dec 2022
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (38)
eBook 740 pages
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Over 80 powerful recipes for effective financial data analysis
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  • 9781803238838 eBook $35.99
  • 9781803243191 Paperback $49.99
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Python for Finance Cookbook
Over 80 powerful recipes for effective financial data analysis
Description
Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions. You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses. Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.
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Dec 2022
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
740 pages
AI Assistant AI Assistant
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Author Eryk Lewinson
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Python Object-Oriented Programming
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Jul 2021
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9 (35)
eBook 714 pages
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Build robust and maintainable object-oriented Python applications and libraries
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  • 9781801075237 eBook $57.59
  • 9781801077262 Paperback $79.99
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Python Object-Oriented Programming
Build robust and maintainable object-oriented Python applications and libraries
Description
Python Object-Oriented Programming, Fourth Edition is a practical guide to advancing your OOP skills with modern Python. Going beyond the fundamentals, it helps you work with Python as an OOP language, explore both common and advanced design patterns, and apply these concepts to data manipulation and testing of complex OOP systems. Each chapter features newly written open-ended exercises as well as a real-world case study, aligned with the improvements in Python 3.11—bringing faster execution and memory efficiency to your applications. Authors Steven F. Lott and Dusty Phillips provide a comprehensive, illustrative tour of important OOP concepts, such as inheritance, composition, and polymorphism, showing how they integrate with Python’s classes and data structures to facilitate good design. The book also introduces two powerful automated testing systems, unittest and pytest, and explores Python's concurrent programming ecosystem in depth. By the end of the book, you’ll have a thorough understanding of how to think about and apply object-oriented principles using Python syntax to create robust and reliable programs.
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Jul 2021
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9
714 pages
AI Assistant AI Assistant
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Author Steven F. Lott
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Python Machine Learning
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Python Machine Learning
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Dec 2019
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (40)
eBook 772 pages
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Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2
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  • 9781789958294 eBook $39.59
  • 9781789955750 Paperback $54.99
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Python Machine Learning
Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2
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.
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Dec 2019
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
772 pages
AI Assistant AI Assistant
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Author Sebastian Raschka
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Hands-On Data Structures and Algorithms with Python
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Jul 2022
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (26)
eBook 496 pages
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Store, manipulate, and access data effectively and boost the performance of your applications
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  • 9781801076814 eBook $34.19
  • 9781801073448 Paperback $49.99
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Hands-On Data Structures and Algorithms with Python
Store, manipulate, and access data effectively and boost the performance of your applications
Description
Choosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You’ll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer. By the end of this Python book, you’ll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.
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Jul 2022
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
496 pages
AI Assistant AI Assistant
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Author Dr. Basant Agarwal
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Expert Python Programming
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May 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (24)
eBook 630 pages
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Master Python by learning the best coding practices and advanced programming concepts
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  • 9781801076197 eBook $37.79
  • 9781801071109 Paperback $50.99
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Expert Python Programming
Master Python by learning the best coding practices and advanced programming concepts
Description
This new edition of Expert Python Programming provides you with a thorough understanding of the process of building and maintaining Python apps. Complete with best practices, useful tools, and standards implemented by professional Python developers, this fourth edition has been extensively updated. Throughout this book, you’ll get acquainted with the latest Python improvements, syntax elements, and interesting tools to boost your development efficiency. The initial few chapters will allow experienced programmers coming from different languages to transition to the Python ecosystem. You will explore common software design patterns and various programming methodologies, such as event-driven programming, concurrency, and metaprogramming. You will also go through complex code examples and try to solve meaningful problems by bridging Python with C and C++, writing extensions that benefit from the strengths of multiple languages. Finally, you will understand the complete lifetime of any application after it goes live, including packaging and testing automation. By the end of this book, you will have gained actionable Python programming insights that will help you effectively solve challenging problems.
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May 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
630 pages
AI Assistant AI Assistant
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Author Michał Jaworski
Author Tarek Ziade
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Python GUI Programming with Tkinter, 2nd edition
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Oct 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (31)
eBook 664 pages
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Design and build functional and user-friendly GUI applications
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  • 9781801818865 eBook $35.99
  • 9781801815925 Paperback $49.99
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Python GUI Programming with Tkinter, 2nd edition
Design and build functional and user-friendly GUI applications
Description
Tkinter is widely used to build GUIs in Python due to its simplicity. In this book, you’ll discover Tkinter’s strengths and overcome its challenges as you learn to develop fully featured GUI applications. Python GUI Programming with Tkinter, Second Edition, will not only provide you with a working knowledge of the Tkinter GUI library, but also a valuable set of skills that will enable you to plan, implement, and maintain larger applications. You’ll build a full-blown data entry application from scratch, learning how to grow and improve your code in response to continually changing user and business needs. You’ll develop a practical understanding of tools and techniques used to manage this evolving codebase and go beyond the default Tkinter widget capabilities. You’ll implement version control and unit testing, separation of concerns through the MVC design pattern, and object-oriented programming to organize your code more cleanly. You’ll also gain experience with technologies often used in workplace applications, such as SQL databases, network services, and data visualization libraries. Finally, you’ll package your application for wider distribution and tackle the challenge of maintaining cross-platform compatibility.
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Oct 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
664 pages
AI Assistant AI Assistant
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Author D. Moore
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Mastering Python Networking
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Mastering Python Networking
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Jan 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (89)
eBook 594 pages
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Utilize Python packages and frameworks for network automation, monitoring, cloud, and management
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Mastering Python Networking
Utilize Python packages and frameworks for network automation, monitoring, cloud, and management
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Networks in your infrastructure set the foundation for deploying, maintaining, and servicing applications. Python is the ideal language for network engineers to explore tools that were previously available to systems engineers and application developers. Mastering Python Networking, Fourth edition, guides you on a Python-driven journey from traditional network engineering to modern network development. This new edition incorporates the latest Python features and DevOps frameworks. In addition to new chapters on introducing Docker containers and Python 3 Async IO for network engineers, each chapter is updated with the latest libraries and working examples to ensure compatibility and clarity of the concepts. Starting with a basic overview of Python, the book teaches you how it can interact with both legacy and API-enabled network devices. You will learn to leverage high-level Python packages and frameworks to perform network automation tasks, monitoring, management, and enhanced network security, followed by AWS and Azure cloud networking. You will use Git for code management, GitLab for continuous integration, and Python-based testing tools to verify your network. By the end of this book, you'll be a confident network developer capable of automating modern infrastructure using Python, DevOps practices, and cloud technologies.
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Jan 2023
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594 pages
AI Assistant AI Assistant
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Author Eric Chou
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Modern Python Cookbook
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Jul 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (18)
eBook 818 pages
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130+ updated recipes for modern Python 3.12 with new techniques and tools
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Modern Python Cookbook
130+ updated recipes for modern Python 3.12 with new techniques and tools
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Python is the go-to language for developers, engineers, data scientists, and hobbyists worldwide. Known for its versatility, Python can efficiently power applications, offering remarkable speed, safety, and scalability. This book distills Python into a collection of straightforward recipes, providing insights into specific language features within various contexts, making it an indispensable resource for mastering Python and using it to handle real-world use cases. The third edition of Modern Python Cookbook provides an in-depth look into Python 3.12, offering more than 140 new and updated recipes that cater to both beginners and experienced developers. This edition introduces new chapters on documentation and style, data visualization with Matplotlib and Pyplot, and advanced dependency management techniques using tools like Poetry and Anaconda. With practical examples and detailed explanations, this cookbook helps developers solve real-world problems, optimize their code, and get up to date with the latest Python features.
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Jul 2024 818 pages
AI Assistant AI Assistant
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Author Steven F. Lott
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Hands-On Financial Trading with Python
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Apr 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (14)
eBook 360 pages
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A practical guide to using Zipline and other Python libraries for backtesting trading strategies
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Hands-On Financial Trading with Python
A practical guide to using Zipline and other Python libraries for backtesting trading strategies
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Creating an effective system to automate your trading can help you achieve two of every trader’s key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage. This practical Python book will introduce you to Python and tell you exactly why it’s the best platform for developing trading strategies. You’ll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. As you progress, you’ll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get —and stay—ahead of the markets.
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Apr 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
360 pages
AI Assistant AI Assistant
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Author Jiri Pik
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Hands-On Graph Neural Networks Using Python
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Apr 2023
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eBook 354 pages
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Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
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Hands-On Graph Neural Networks Using Python
Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
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Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps. By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.
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Apr 2023
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354 pages
AI Assistant AI Assistant
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Author Maxime Labonne
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Clean Code in Python
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Jan 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (34)
eBook 422 pages
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Write Scalable, Readable, and Maintainable Code with Modern Python
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  • 9781800562097 eBook $37.79
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Clean Code in Python
Write Scalable, Readable, and Maintainable Code with Modern Python
Description
Experienced professionals in every field face several instances of disorganization, poor readability, and testability due to unstructured code. This second edition has been updated to reflect new best practices in Python 3.9, helping developers write professional, scalable, and maintainable code. It introduces advanced object-oriented design, decorators, descriptors, and refactoring strategies using SOLID principles. Whether modernizing legacy projects or building new applications, this book provides practical techniques to write cleaner and more efficient Python code using the Python standard library and manage your projects successfully. The book begins by describing the basic elements of writing clean code and how it plays a key role in Python programming. The book discusses object-oriented programming in Python and show you the design principles of software testing and how to resolve problems by implementing software design patterns in your code. In the concluding chapter, we break down a monolithic application into a microservices-based one starting from the code as the basis for a solid platform. By the end of this clean code book, you will be proficient in applying industry-approved coding practices to design clean, sustainable, and readable real-world Python code.
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Jan 2021
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
422 pages
AI Assistant AI Assistant
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Author Mariano Anaya
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Building AI Intensive Python Applications
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Building AI Intensive Python Applications
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Sep 2024
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eBook 298 pages
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Create intelligent apps with LLMs and vector databases
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Building AI Intensive Python Applications
Create intelligent apps with LLMs and vector databases
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The era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.
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Sep 2024 298 pages
AI Assistant AI Assistant
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Author Rachelle Palmer
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Learn Python Programming
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Learn Python Programming
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Jun 2018
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (28)
eBook 508 pages
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The no-nonsense, beginner's guide to programming, data science, and web development with Python 3.7
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  • 9781788991650 eBook $33.29
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Learn Python Programming
The no-nonsense, beginner's guide to programming, data science, and web development with Python 3.7
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Learn Python Programming is a quick, thorough, and practical introduction to Python - an extremely flexible and powerful programming language that can be applied to many disciplines. Unlike other books, it doesn't bore you with elaborate explanations of the basics but gets you up-and-running, using the language. You will begin by learning the fundamentals of Python so that you have a rock-solid foundation to build upon. You will explore the foundations of Python programming and learn how Python can be manipulated to achieve results. Explore different programming paradigms and find the best approach to a situation; understand how to carry out performance optimization and effective debugging; control the flow of a program; and utilize an interchange format to exchange data. You'll also walk through cryptographic services in Python and understand secure tokens. Learn Python Programming will give you a thorough understanding of the Python language. You'll learn how to write programs, build websites, and work with data by harnessing Python's renowned data science libraries. Filled with real-world examples and projects, the book covers various types of applications, and concludes by building real-world projects based on the concepts you have learned.
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Jun 2018 508 pages
AI Assistant AI Assistant
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Author Romano
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Python for Security and Networking
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Python for Security and Networking
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Jun 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (33)
eBook 586 pages
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Leverage Python modules and tools in securing your network and applications
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Python for Security and Networking
Leverage Python modules and tools in securing your network and applications
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Python’s latest updates add numerous libraries that can be used to perform critical security-related missions, including detecting vulnerabilities in web applications, taking care of attacks, and helping to build secure and robust networks that are resilient to them. This fully updated third edition will show you how to make the most of them and improve your security posture. The first part of this book will walk you through Python scripts and libraries that you’ll use throughout the book. Next, you’ll dive deep into the core networking tasks where you will learn how to check a network’s vulnerability using Python security scripting and understand how to check for vulnerabilities in your network – including tasks related to packet sniffing. You’ll also learn how to achieve endpoint protection by leveraging Python packages along with writing forensics scripts. The next part of the book will show you a variety of modern techniques, libraries, and frameworks from the Python ecosystem that will help you extract data from servers and analyze the security in web applications. You’ll take your first steps in extracting data from a domain using OSINT tools and using Python tools to perform forensics tasks. By the end of this book, you will be able to make the most of Python to test the security of your network and applications.
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Jun 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
586 pages
AI Assistant AI Assistant
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Author José Manuel Ortega
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Artificial Intelligence with Python
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Jan 2020
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eBook 618 pages
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Your complete guide to building intelligent apps using Python 3.x
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Artificial Intelligence with Python
Your complete guide to building intelligent apps using Python 3.x
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Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
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Jan 2020
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9
618 pages
AI Assistant AI Assistant
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Author Alberto Artasanchez
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Data Engineering with Python
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Data Engineering with Python
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Oct 2020
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.6 (24)
eBook 356 pages
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Work with massive datasets to design data models and automate data pipelines using Python
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Data Engineering with Python
Work with massive datasets to design data models and automate data pipelines using Python
Description
Data 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.
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Oct 2020
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.6
356 pages
AI Assistant AI Assistant
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Authors
Author Paul Crickard
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Machine Learning Engineering with Python
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Aug 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (36)
eBook 462 pages
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Manage the lifecycle of machine learning models using MLOps with practical examples
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Machine Learning Engineering with Python
Manage the lifecycle of machine learning models using MLOps with practical examples
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The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
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Aug 2023
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
462 pages
AI Assistant AI Assistant
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Author Andrew P. McMahon
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Generative AI Foundations in Python
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Jul 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (5)
eBook 190 pages
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Discover key techniques and navigate modern challenges in LLMs
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  • 9781835464915 eBook $28.79
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Generative AI Foundations in Python
Discover key techniques and navigate modern challenges in LLMs
Description
The intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.
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Jul 2024 190 pages
AI Assistant AI Assistant
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Authors
Author Carlos Rodriguez
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Hands-On GPU Programming with Python and CUDA
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Hands-On GPU Programming with Python and CUDA
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Nov 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (7)
eBook 310 pages
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Explore high-performance parallel computing with CUDA
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  • 9781788995221 eBook $35.99
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Hands-On GPU Programming with Python and CUDA
Explore high-performance parallel computing with CUDA
Description
Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory. As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS. With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain. By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.
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Nov 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
310 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author Dr. Brian Tuomanen
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Extending Power BI with Python and R
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Extending Power BI with Python and R
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Mar 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (30)
eBook 814 pages
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Perform advanced analysis using the power of analytical languages
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  • 9781837635863 eBook $41.39
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Extending Power BI with Python and R
Perform advanced analysis using the power of analytical languages
Description
The latest edition of this book delves deep into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond available RAM, employing the Parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server Language Extensions to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the Grammar of Graphics in both R and Python. This Power BI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. You'll learn how to safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of datasets by plotting multiple visual graphs in the process of building a machine learning model. The book will guide you on utilizing external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis. You'll reinforce your learning with questions at the end of each chapter.
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Mar 2024
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814 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author Luca Zavarella
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Offensive Security Using Python
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Offensive Security Using Python
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Sep 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (5)
eBook 248 pages
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A hands-on guide to offensive tactics and threat mitigation using practical strategies
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  • 9781835460634 eBook $30.59
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Offensive Security Using Python
A hands-on guide to offensive tactics and threat mitigation using practical strategies
Description
Offensive Security Using Python is your go-to manual for mastering the quick-paced field of offensive security. This book is packed with valuable insights, real-world examples, and hands-on activities to help you leverage Python to navigate the complicated world of web security, exploit vulnerabilities, and automate challenging security tasks. From detecting vulnerabilities to exploiting them with cutting-edge Python techniques, you’ll gain practical insights into web security, along with guidance on how to use automation to improve the accuracy and effectiveness of your security activities. You’ll also learn how to design personalized security automation tools. While offensive security is a great way to stay ahead of emerging threats, defensive security plays an equal role in protecting organizations from cyberattacks. In this book, you’ll get to grips with Python secure coding techniques to improve your ability to recognize dangers quickly and take appropriate action. As you progress, you’ll be well on your way to handling the contemporary challenges in the field of cybersecurity using Python, as well as protecting your digital environment from growing attacks. By the end of this book, you’ll have a solid understanding of sophisticated offensive security methods and be able to stay ahead in the constantly evolving cybersecurity space.
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Sep 2024
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248 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author Rejah Rehim
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Mastering Python Design Patterns
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Mastering Python Design Patterns
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May 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (8)
eBook 296 pages
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Craft essential Python patterns by following core design principles
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Mastering Python Design Patterns
Craft essential Python patterns by following core design principles
Description
As software systems become increasingly complex, maintaining code quality, scalability, and efficiency can be a daunting challenge. Mastering Python Design Patterns is an essential resource that equips you with the tools you need to overcome these hurdles and create robust, scalable applications. The book delves into design principles and patterns in Python, covering both classic and modern patterns, and apply them to solve daily challenges as a Python developer or architect. Co-authored by two Python experts with a combined experience of three decades, this new edition covers creational, structural, behavioral, and architectural patterns, including concurrency, asynchronous, and performance patterns. You'll find out how these patterns are relevant to various domains, such as event handling, concurrency, distributed systems, and testing. Whether you're working on user interfaces (UIs), web apps, APIs, data pipelines, or AI models, this book equips you with the knowledge to build robust and maintainable software. The book also presents Python anti-patterns, helping you avoid common pitfalls and ensuring your code remains clean and efficient. By the end of this book, you'll be able to confidently apply classic and modern Python design patterns to build robust, scalable applications.
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May 2024 296 pages
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Author Kamon Ayeva
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Mastering Python Design Patterns
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Mastering Python Design Patterns
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May 2024
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (8)
eBook 296 pages
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Craft essential Python patterns by following core design principles
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  • 9781837637652 eBook $28.79
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Mastering Python Design Patterns
Craft essential Python patterns by following core design principles
Description
As software systems become increasingly complex, maintaining code quality, scalability, and efficiency can be a daunting challenge. Mastering Python Design Patterns is an essential resource that equips you with the tools you need to overcome these hurdles and create robust, scalable applications. The book delves into design principles and patterns in Python, covering both classic and modern patterns, and apply them to solve daily challenges as a Python developer or architect. Co-authored by two Python experts with a combined experience of three decades, this new edition covers creational, structural, behavioral, and architectural patterns, including concurrency, asynchronous, and performance patterns. You'll find out how these patterns are relevant to various domains, such as event handling, concurrency, distributed systems, and testing. Whether you're working on user interfaces (UIs), web apps, APIs, data pipelines, or AI models, this book equips you with the knowledge to build robust and maintainable software. The book also presents Python anti-patterns, helping you avoid common pitfalls and ensuring your code remains clean and efficient. By the end of this book, you'll be able to confidently apply classic and modern Python design patterns to build robust, scalable applications.
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May 2024 296 pages
AI Assistant AI Assistant
AI Assistant: Features our revolutionary AI Assistant technology which lets you interact with our Books to enhance your learning
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Author Kamon Ayeva
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Practical Guide to Applied Conformal Prediction in Python
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Practical Guide to Applied Conformal Prediction in Python
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Dec 2023
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (28)
eBook 240 pages
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Learn and apply the best uncertainty frameworks to your industry applications
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  • 9781805120919 eBook $37.79
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Practical Guide to Applied Conformal Prediction in Python
Learn and apply the best uncertainty frameworks to your industry applications
Description
In the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.
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Dec 2023
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
240 pages
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Author Valery Manokhin
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Practical Python Programming for IoT
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Practical Python Programming for IoT
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Nov 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (6)
eBook 516 pages
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Build advanced IoT projects using a Raspberry Pi 4, MQTT, RESTful APIs, WebSockets, and Python 3
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  • 9781838982836 eBook $37.79
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Practical Python Programming for IoT
Build advanced IoT projects using a Raspberry Pi 4, MQTT, RESTful APIs, WebSockets, and Python 3
Description
The age of connected devices is here, be it fitness bands or smart homes. It's now more important than ever to understand how hardware components interact with the internet to collect and analyze user data. The Internet of Things (IoT), combined with the popular open source language Python, can be used to build powerful and intelligent IoT systems with intuitive interfaces. This book consists of three parts, with the first focusing on the "Internet" component of IoT. You'll get to grips with end-to-end IoT app development to control an LED over the internet, before learning how to build RESTful APIs, WebSocket APIs, and MQTT services in Python. The second part delves into the fundamentals behind electronics and GPIO interfacing. As you progress to the last part, you'll focus on the "Things" aspect of IoT, where you will learn how to connect and control a range of electronic sensors and actuators using Python. You'll also explore a variety of topics, such as motor control, ultrasonic sensors, and temperature measurement. Finally, you'll get up to speed with advanced IoT programming techniques in Python, integrate with IoT visualization and automation platforms, and build a comprehensive IoT project. By the end of this book, you'll be well-versed with IoT development and have the knowledge you need to build sophisticated IoT systems using Python.
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Nov 2020
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516 pages
AI Assistant AI Assistant
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Author Gary Smart
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Mastering Reinforcement Learning with Python
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Mastering Reinforcement Learning with Python
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Dec 2020
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (12)
eBook 544 pages
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Build next-generation, self-learning models using reinforcement learning techniques and best practices
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  • 9781838648497 eBook $34.19
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Mastering Reinforcement Learning with Python
Build next-generation, self-learning models using reinforcement learning techniques and best practices
Description
Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.
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Dec 2020
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
544 pages
AI Assistant AI Assistant
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Author Enes Bilgin
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Learning OpenCV 4 Computer Vision with Python 3
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Learning OpenCV 4 Computer Vision with Python 3
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Feb 2020
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (14)
eBook 372 pages
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Get to grips with tools, techniques, and algorithms for computer vision and machine learning
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  • 9781789530643 eBook $37.79
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Learning OpenCV 4 Computer Vision with Python 3
Get to grips with tools, techniques, and algorithms for computer vision and machine learning
Description
Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
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Feb 2020
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1
372 pages
AI Assistant AI Assistant
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Author Joseph Howse
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Modern Time Series Forecasting with Python
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Modern Time Series Forecasting with Python
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Nov 2022
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (29)
eBook 552 pages
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Explore industry-ready time series forecasting using modern machine learning and deep learning
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  • 9781803232041 eBook $40.49
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Modern Time Series Forecasting with Python
Explore industry-ready time series forecasting using modern machine learning and deep learning
Description
We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.
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Nov 2022
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
552 pages
AI Assistant AI Assistant
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Author Manu Joseph
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Python Feature Engineering Cookbook
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Python Feature Engineering Cookbook
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Aug 2024
eBook 396 pages
A complete guide to crafting powerful features for your machine learning models
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  • 9781835883594 eBook $32.39
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Python Feature Engineering Cookbook
A complete guide to crafting powerful features for your machine learning models
Description
Streamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient. This guide addresses common challenges, such as imputing missing values and encoding categorical variables using practical solutions and open source Python libraries. You’ll learn advanced techniques for transforming numerical variables, discretizing variables, and dealing with outliers. Each chapter offers step-by-step instructions and real-world examples, helping you understand when and how to apply various transformations for well-prepared data. The book explores feature extraction from complex data types such as dates, times, and text. You’ll see how to create new features through mathematical operations and decision trees and use advanced tools like Featuretools and tsfresh to extract features from relational data and time series. By the end, you’ll be ready to build reproducible feature engineering pipelines that can be easily deployed into production, optimizing data preprocessing workflows and enhancing machine learning model performance.
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Aug 2024 396 pages
AI Assistant AI Assistant
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Author Soledad Galli
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