دانلود کتاب برنامه‌نویسی به کمک هوش مصنوعی برای وب و یادگیری ماشین

Software development is entering a new era. What was once the sole domain of human coders is now a collaborative space where artificial intelligence (AI) works alongside us, suggesting improvements, generating code, catching errors, and even optimizing solutions before we run them. When we first explored AI-assisted coding, each of us approached it with a healthy mix of curiosity and skepticism. Could an AI truly understand the complexities and nuances of modern development workflows? We put it to the test, and within days, our initial doubts gave way to excitement. Tools like GitHub Copilot and ChatGPT were not only automating repetitive coding tasks but also suggesting elegant solutions and introducing innovative approaches none of us had anticipated. Experiencing this collectively changed the way we thought about programming. We realized that AI is not here to replace a developer’s creativity or expertise — it is here to amplify them. This book was born from that shared discovery, and we aim to help you experience AI as a trusted partner in your development journey. AI-assisted Programming for Web and Machine Learning is your complete, hands-on guide to integrating AI into your daily coding practice. We will start with the foundations — understanding AI’s role in programming, setting up an AIready environment, and mastering the art of prompt engineering. Then we will move into practical applications: using AI to accelerate front end and back end development, enhance debugging and optimization, and streamline machine learning pipelines from preprocessing to deployment. You will also find real-world case studies, best practices, and ethical considerations to keep your work responsible and future-ready. Whether you are a student exploring AI-assisted coding for the first time, a developer looking to shorten delivery timelines, or a machine learning practitioner aiming to automate complex workflows, this book will give you both the skills and the confidence to work with AI, not as a gimmick, but as an essential part of your toolkit. By the final chapter, AI will not feel like an extra you occasionally try — it will feel like a trusted teammate you cannot imagine working without. Chapter 1: AI in Programming – Trace AI’s journey from research labs to everyday coding desks. Explore transformative milestones, from the first code-assist experiments to today’s advanced tools, and see how GitHub Copilot and ChatGPT are reshaping developer workflows. Learn why adoption is growing, what benefits early adopters report, and where the limitations still lie so you can set realistic expectations for AI in your work. Chapter 2: Setting up Your AI Environment – Great results start with the right environment. Learn how to configure Visual Studio Code for AI integration, use Jupyter Notebook for data-driven projects, and manage collaborative coding with GitHub. Discover how Docker supports containerized workflows and how AI agents can automate routine tasks like testing, deployment, and code refactoring, leaving you free to focus on problem-solving. Chapter 3: Prompt Engineering – The difference between mediocre and outstanding AI results often comes down to the prompt. This chapter shows you how to craft clear, context-rich prompts for specific outcomes—whether generating full features, diagnosing errors, or building complex ML workflows. Real-world examples demonstrate how subtle changes in phrasing can produce dramatically different results, and case studies reveal prompt strategies used in successful projects. Chapter 4: AI in Front end Development – Experience the speed boost of letting AI generate clean, responsive HTML/CSS layouts, streamline JavaScript functions, and prototype UI/UX concepts in minutes. See how to combine AI’s rapid prototyping with your design expertise to finetune results and integrate these capabilities with React to deliver dynamic, data-driven, and accessible front end applications. Chapter 5: AI for Back end Development – Learn how AI can accelerate server-side coding by generating API endpoints, suggesting optimized database queries, and even writing authentication logic. Explore examples using Node.js and Django, with guidance on ensuring security, scalability, and maintainability. You will also see how AI can help with documentation and automated testing to support long-term back end health. Chapter 6: Debugging and Optimization with AI – Transform debugging from a time-consuming chore into an efficient, collaborative process. Learn how to feed AI error messages and receive actionable suggestions, detect hidden performance bottlenecks, and optimize code for speed and scalability. This chapter also covers integrating AI with profiling tools to monitor performance in real time. Chapter 7: Data Preprocessing with AI – Machine learning depends on high-quality data. Here, you will learn how AI can clean datasets, handle missing values, normalize formats, and extract key features automatically. Explore techniques for visualizing complex data relationships and preparing both structured and unstructured data for analysis, saving hours of manual preprocessing. Chapter 8: Building and Training Machine Learning Models – Use AI to assist in selecting the right algorithms, setting up your ML pipeline, and training models efficiently. Build classification, regression, CNN, and MLP models while learning how to fine-tune hyperparameters for maximum performance. Understand evaluation metrics in depth so you can measure success beyond just accuracy. Chapter 9: Deploying Optimized ML Models – A trained model is only valuable when it is in use. This chapter shows you AI-assisted approaches for fine-tuning, versioning, and deploying models to production. Learn scalable deployment strategies, from containerized services to cloud-based hosting, and see how to automate updates and monitor model performance post-deployment. Chapter 10: Real-world Applications – Go behind the scenes of AI-assisted projects in full-stack web development and machine learning. Learn how teams cut development time, improve code quality, and deliver innovative solutions using AI tools. Each case study includes takeaways you can apply to your work, plus cautions to help you avoid common pitfalls. Chapter 11: Future Innovations and Ethics in AI – Look beyond current capabilities to emerging trends like autonomous coding agents, multimodal AI assistants, and integrated AI project management. At the same time, address ethical challenges: mitigating bias, safeguarding user privacy, and ensuring that automation supports — rather than replaces — human creativity.