Engineering With Python And Ai
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Engineering With Python And Ai
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Author : Ahmed Abdulla
language : en
Publisher: Independently Published
Release Date : 2024-06-23
Engineering With Python And Ai written by Ahmed Abdulla and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-23 with Computers categories.
Unlock the power of Python and revolutionize your engineering projects with this comprehensive guide. "Engineering with Python: A Comprehensive Guide for Modern Engineers" is an essential resource for engineers and students looking to harness the versatility and efficiency of Python in their work. This book provides a practical, hands-on approach to using Python in various engineering disciplines, from data analysis and modeling to automation and machine learning. Whether you are a mechanical, electrical, civil, or chemical engineer, this guide covers the essential tools and techniques you need to tackle complex engineering problems and innovate solutions. Inside this book, you will find: Fundamentals of Python Programming: Learn the basics of Python, including variables, control flow, data structures, and functions. Set up your Python development environment with ease. Engineering Libraries and Tools: Master essential libraries like NumPy, SciPy, Pandas, Matplotlib, and Seaborn for numerical computation, data manipulation, and visualization. Explore advanced libraries for machine learning and deep learning. Data Analysis and Visualization: Import, process, and analyze engineering data from Excel and CSV files. Create professional graphs and visualizations to communicate your findings effectively. Modeling and Simulation: Develop mathematical models and simulate engineering systems using differential equations and linear algebra. Apply these techniques to real-world problems in mechanical, electrical, and civil engineering. Statistical Analysis and Optimization: Perform descriptive and inferential statistical analyses on engineering data. Use optimization techniques to enhance engineering designs and processes. Machine Learning and Artificial Intelligence: Implement machine learning algorithms to optimize engineering processes and predict outcomes. Dive into neural networks and deep learning to solve complex engineering problems. Automation and Scripting: Write Python scripts to automate repetitive engineering tasks and improve efficiency. Develop automated solutions for data analysis, modeling, and report generation. Advanced Projects and Case Studies: Explore integrated projects that demonstrate the application of Python in smart manufacturing, smart city traffic management, renewable energy, and structural health monitoring. Learn from detailed case studies based on real-world engineering challenges. Who is this book for? Engineering students seeking to enhance their programming skills. Professional engineers looking to incorporate Python into their workflows. Researchers and academics aiming to leverage Python for data analysis and modeling. Technical managers overseeing engineering projects and innovations. Why choose this book? "Engineering with Python" offers a unique blend of theoretical knowledge and practical applications, making it an invaluable resource for engineers at all levels. With clear explanations, step-by-step instructions, and real-world examples, this book empowers you to solve engineering problems efficiently and innovate in your field. Transform your engineering projects with the power of Python. Get your copy of "Engineering with Python: A Comprehensive Guide for Modern Engineers" today and take the first step towards mastering Python in engineering!
Machine Learning Engineering With Python
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Author : Andrew P. McMahon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-11-05
Machine Learning Engineering With Python written by Andrew P. McMahon and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-05 with Computers categories.
Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments Key Features Explore hyperparameter optimization and model management tools Learn object-oriented programming and functional programming in Python to build your own ML libraries and packages Explore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use cases Book DescriptionMachine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.What you will learn Find out what an effective ML engineering process looks like Uncover options for automating training and deployment and learn how to use them Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions Understand what aspects of software engineering you can bring to machine learning Gain insights into adapting software engineering for machine learning using appropriate cloud technologies Perform hyperparameter tuning in a relatively automated way Who this book is for This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediate-level knowledge of Python is necessary.
Python Ai Programming
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Author : Patrick J
language : en
Publisher: GitforGits
Release Date : 2024-01-03
Python Ai Programming written by Patrick J and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-03 with Computers categories.
This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models. The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding. We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered. The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner. Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable. Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence. Key Learnings Explore Python basics and AI integration for real-world application and career advancement. Experience the power of Python in AI with practical machine learning techniques. Practice Python's deep learning tools for innovative AI solution development. Dive into NLP with Python to revolutionize data interpretation and communication strategies. Simple yet practical understanding of reinforcement learning for strategic AI decision making. Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI. Harness Python's capabilities for creating AI applications with a focus on fairness and bias. Table of Content Introduction to Artificial Intelligence Python for AI Data as Fuel for AI Machine Learning Foundation Essentials of Deep Learning NLP and Computer Vision Hands-on Reinforcement Learning Ethics to AI
Ai In Structural Engineering
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Author : DR. G. Chennakesava Reddy
language : en
Publisher: AQUA PUBLICATIONS
Release Date :
Ai In Structural Engineering written by DR. G. Chennakesava Reddy and has been published by AQUA PUBLICATIONS this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
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What Every Engineer Should Know About Python
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Author : Raymond J. Madachy
language : en
Publisher: CRC Press
Release Date : 2025-05-27
What Every Engineer Should Know About Python written by Raymond J. Madachy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-27 with Computers categories.
Engineers across all disciplines can benefit from learning Python. This powerful programming language enables engineers to enhance their skill sets and perform more sophisticated work in less time, whether in engineering analysis, system design and development, integration and testing, machine learning and other artificial intelligence applications, project management, or other areas. What Every Engineer Should Know About Python offers students and practicing engineers a straightforward and practical introduction to Python for technical programming and broader uses to enhance productivity. It focuses on the core features of Python most relevant to engineering tasks, avoids computer science jargon, and emphasizes writing useful software while effectively leveraging generative AI. Features examples tied to real-world engineering scenarios that are easily adapted Explains how to leverage the vast ecosystem of open-source Python packages for scientific applications, rather than developing new software from scratch Covers the incorporation of Python into engineering designs and systems, whether web-based, desktop, or embedded Provides guidance on optimizing generative AI with Python, including case study examples Describes software tool environments and development practices for the rapid creation of high-quality software Demonstrates how Python can improve personal and organizational productivity through workflow automation Directs readers to further resources for exploring advanced Python features This practical and concise book serves as a self-contained introduction for engineers and readers from scientific disciplines who are new to programming or to Python.
Master Python Data Engineering With Virtual Ai Tutoring
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Author : Diego Rodrigues
language : en
Publisher: Diego Rodrigues
Release Date : 2024-11-19
Master Python Data Engineering With Virtual Ai Tutoring written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Business & Economics categories.
Imagine acquiring a book and, as a bonus, gaining access to a 24/7 AI-assisted Virtual Tutoring to personalize your learning journey, reinforce knowledge, and receive mentorship for developing and implementing real projects... ...Welcome to the Revolution of Personalized Learning with AI-Assisted Virtual Tutoring! Discover " MASTER PYTHON DATA ENGINEERING: From Fundamentals to Advanced Applications with Virtual AI Tutoring," the essential guide for professionals and enthusiasts who want to master data engineering with Python. This innovative manual, written by Diego Rodrigues, an author with over 140 titles published in six languages, combines high-quality content with the advanced technology of IAGO, a virtual tutor developed and hosted on the OpenAI platform. Innovative Features: Personalized Learning: IAGO adapts the content to your knowledge level, offering detailed explanations and personalized exercises. Immediate Feedback: Receive corrections and suggestions in real time, speeding up your learning process. Interactivity and Engagement: Interact with the tutor via text or voice, making learning more dynamic and motivating. Project Development Mentorship: Get practical guidance to develop and implement real projects, applying the knowledge gained. Total Flexibility: Access the tutor anywhere, anytime, whether on a desktop, notebook, or smartphone with web access. Take advantage of the Limited-Time Launch Promotional Price! Don't miss the opportunity to transform your learning journey with an innovative and effective method. This book has been carefully structured to meet your needs and exceed your expectations, ensuring you are prepared to face challenges and seize opportunities in the field of data engineering. Open the book sample and discover how to access the select club of cutting-edge technology professionals. Take advantage of this unique opportunity and achieve your goals! TAGS: data engineering automation science big Pandas NumPy Dask SQLAlchemy web scraping BeautifulSoup Scrapy APIs ETL DataOps Data Lakes Data Warehouses AWS Google Cloud Microsoft Azure Hadoop Spark machine learning artificial intelligence data pipelines data visualization Matplotlib Seaborn data analysis relational databases NoSQL MongoDB Apache Airflow Kafka real-time data governance data security compliance mentorship Diego Rodrigues Tableau Power BI Snowflake Informatica Alation Talend Apache Flink Jupyter Notebooks DevOps Databricks Cloudera Hortonworks Teradata IBM Cloud Oracle Cloud Salesforce SAP HANA ElasticSearch Redis Kubernetes Docker Jenkins GitHub GitLab Continuous Integration Continuous Deployment CI/CD digital transformation predictive analysis business intelligence IoT Internet of Things smart cities connected health Industry 4.0 fintechs retail education marketing competitive intelligence data science automated testing custom reports operational efficiency Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques skills cybersecurity industry global cybersecurity trends Kali Linux tools education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR
Data Centric Machine Learning With Python
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Author : Jonas Christensen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-02-29
Data Centric Machine Learning With Python written by Jonas Christensen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Computers categories.
Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synthetic data creation using Python Develop essential skills for building reliable, responsible, and ethical machine learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.What you will learn Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI Who this book is for This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.
Python For Ai
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Author : Matthew J. Taylor
language : en
Publisher:
Release Date : 2025-12
Python For Ai written by Matthew J. Taylor and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12 with Computers categories.
A practical, example¿driven guide to building and scaling AI workloads with Python-on laptops, workstations, GPUs, TPUs, edge devices, and clusters. Most AI books focus on models. Python For AI focuses on how to make those models actually run well on real hardware: faster, cheaper, and more reproducibly. Through concise, hands¿on chapters, you'll learn how to set up reliable environments, profile and optimize performance, and scale training and inference across modern compute platforms. The tone is professional but approachable, with runnable examples and concrete recipes you can adapt to your own projects. Who this book is for Software engineers and ML/AI practitioners who already use Python and want to make better use of CPUs, GPUs, and clusters. Data scientists and ML engineers who can train models on a single machine but struggle with slow runs, scaling, or reproducibility. Students and self¿taught developers comfortable with basic Python who want a guided path from "runs on my laptop" to "runs efficiently on serious hardware." What you'll learn Setting up reproducible Python environments using tools like virtual environments, Conda, and containers. Profiling and benchmarking AI workloads to find real CPU, GPU, memory, and I/O bottlenecks. Optimizing Python code with vectorization, multi¿processing, and async patterns so CPUs aren't the bottleneck. Using GPUs and other accelerators effectively with frameworks such as PyTorch, TensorFlow, and JAX-without wasting device time. Scaling training and inference across multiple GPUs and nodes, including data parallelism, model parallelism, and basic cluster orchestration. Deploying models to constrained or edge environments and making informed trade¿offs between speed, cost, and accuracy. Improving reliability with checkpointing, monitoring, and simple, repeatable workflows that others can run. What this book is not Not a first¿time programming book: you should already be comfortable reading and writing basic Python and using the command line. Not a deep math or theory text: the focus is on systems, performance, and engineering trade¿offs rather than proofs and derivations. Not a catalog of model architectures: examples use common models, but the emphasis is on running, scaling, and deploying them efficiently.
Machine Learning Engineering With Python
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Author : Andrew P. McMahon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-08-31
Machine Learning Engineering With Python written by Andrew P. McMahon and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-31 with Computers categories.
Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain Key Features This second edition delves deeper into key machine learning topics, CI/CD, and system design Explore core MLOps practices, such as model management and performance monitoring Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools Book DescriptionThe 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.What you will learn Plan and manage end-to-end ML development projects Explore deep learning, LLMs, and LLMOps to leverage generative AI Use Python to package your ML tools and scale up your solutions Get to grips with Apache Spark, Kubernetes, and Ray Build and run ML pipelines with Apache Airflow, ZenML, and Kubeflow Detect drift and build retraining mechanisms into your solutions Improve error handling with control flows and vulnerability scanning Host and build ML microservices and batch processes running on AWS Who this book is for This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you’re not a developer but want to manage or understand the product lifecycle of these systems, you’ll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.
Machine Learning Engineering With Python Second Edition
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Author : Andrew McMahon
language : en
Publisher:
Release Date : 2023-06
Machine Learning Engineering With Python Second Edition written by Andrew McMahon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06 with categories.
This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems.