Python first principles data par nayak ravindra (11 résultats)

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Etat : Neu. Neuware - Volume 1 helped you understand Python thinking.Volume 2 helped you build practical programs.Volume 3 helped you organize information with data structures.Volume 4 helped you work with real data using NumPy and pandas.Volume 5 helped you visualize data and think statistically.Volume 6 helped you enter machine learning responsibly.Volume 7 helped you build professional Python tools.Volume 8 brings Python into the modern AI and automation world.AI can feel mysterious. Prompts, models, tokens, embeddings, APIs, RAG, agents, and automation workflows can sound overwhelming to beginners. But behind the noise, the first principles are simple: input, context, reasoning support, output, evaluation, and human judgment.Python First Principles for Data Scientists and Developers - Volume 8: The AI and Automation Workshop explains AI-assisted building step by step.This volume teaches readers how to use Python with large language models, design better prompts, call APIs, structure AI outputs, search meaning with embeddings, build beginner-friendly RAG systems, understand agentic workflows, evaluate AI responses, protect privacy, and use AI responsibly in data science and software development.Inside this volume, readers will learn: How LLMs fit into the Python developer's workflowHow prompts work as instructions, context, and constraintsHow to use APIs safely and practicallyHow structured outputs make AI results more reliableHow embeddings represent meaningHow semantic search finds ideas, not just keywordsHow RAG connects documents with AI responsesHow agents use tools, steps, and feedback loopsHow to evaluate AI outputs instead of trusting them blindlyHow to use AI for data science assistanceHow to use AI for developer productivityHow to think about safety, privacy, bias, and responsible automationHow to build practical AI-assisted capstone projectsThis is not a hype-driven AI book.It is a practical workshop for building with AI calmly and responsibly.By the end of Volume 8, readers will understand how Python, AI models, automation, and human judgment work together.AI is not a replacement for thinking.AI is a tool that becomes powerful when guided by clear thinking.…

Paperback. Etat : new. Paperback. Volume 1 helped you understand Python thinking.Volume 2 helped you build practical programs.Volume 3 helped you organize information with data structures.Volume 4 helped you work with real data using NumPy and pandas.Volume 5 helped you visualize data and think statistically.Volume 6 helped you enter machine learning responsibly.Volume 7 helped you build professional Python tools.Volume 8 brings Python into the modern AI and automation world.AI can feel mysterious. Prompts, models, tokens, embeddings, APIs, RAG, agents, and automation workflows can sound overwhelming to beginners. But behind the noise, the first principles are simple: input, context, reasoning support, output, evaluation, and human judgment.Python First Principles for Data Scientists and Developers - Volume 8: The AI and Automation Workshop explains AI-assisted building step by step.This volume teaches readers how to use Python with large language models, design better prompts, call APIs, structure AI outputs, search meaning with embeddings, build beginner-friendly RAG systems, understand agentic workflows, evaluate AI responses, protect privacy, and use AI responsibly in data science and software development.Inside this volume, readers will learn: How LLMs fit into the Python developer's workflowHow prompts work as instructions, context, and constraintsHow to use APIs safely and practicallyHow structured outputs make AI results more reliableHow embeddings represent meaningHow semantic search finds ideas, not just keywordsHow RAG connects documents with AI responsesHow agents use tools, steps, and feedback loopsHow to evaluate AI outputs instead of trusting them blindlyHow to use AI for data science assistanceHow to use AI for developer productivityHow to think about safety, privacy, bias, and responsible automationHow to build practical AI-assisted capstone projectsThis is not a hype-driven AI book.It is a practical workshop for building with AI calmly and responsibly.By the end of Volume 8, readers will understand how Python, AI models, automation, and human judgment work together.AI is not a replacement for thinking.AI is a tool that becomes powerful when guided by clear thinking This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Etat : new. Paperback. Volume 1 helped you understand Python thinking.Volume 2 helped you build practical programs.Volume 3 teaches you how to organize information so your programs become clearer, smarter, and more useful.Many beginners learn lists, dictionaries, tuples, and sets as separate syntax topics. But real confidence begins when you understand them as shapes of thought.A list is for order.A dictionary is for labels and lookup.A set is for uniqueness.A tuple is for fixed facts.Nested data is for real-world records.Python First Principles for Data Scientists and Developers - Volume 3: The Data Structures Lab breaks these ideas down slowly and practically. Through friendly explanations, interactive dialogues, guided examples, exercises, checklists, mini-projects, and a capstone learning tracker, readers learn how to choose the right structure before writing code.Inside this volume, readers will learn: How lists help store ordered informationHow dictionaries connect keys to meaningHow tuples protect fixed factsHow sets remove duplicates and compare groupsHow strings behave like structured textHow nested data represents real-world recordsHow comprehensions make repeated transformations cleanerHow searching, sorting, counting, and grouping workHow simple algorithm patterns prepare the mind for deeper problem solvingHow data scientists and developers think differently about the same structuresThis is not a rushed syntax reference.It is a practical lab for building structured Python thinking.By the end of Volume 3, readers will understand how information moves through Python programs, how patterns appear in data, and how small structures become the foundation for data science, automation, software tools, and machine learning.Data enters.Structure forms.Insight begins. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Etat : New. Print on Demand.

Etat : New. Print on Demand.

Paperback. Etat : new. Paperback. Volume 1 helped you understand Python thinking.Volume 2 helped you build practical programs.Volume 3 helped you organize information with data structures.Volume 4 helped you work with real data using NumPy and pandas.Volume 5 helped you visualize data and think statistically.Volume 6 helped you enter machine learning responsibly.Volume 7 helped you build professional Python tools.Volume 8 brings Python into the modern AI and automation world.AI can feel mysterious. Prompts, models, tokens, embeddings, APIs, RAG, agents, and automation workflows can sound overwhelming to beginners. But behind the noise, the first principles are simple: input, context, reasoning support, output, evaluation, and human judgment.Python First Principles for Data Scientists and Developers - Volume 8: The AI and Automation Workshop explains AI-assisted building step by step.This volume teaches readers how to use Python with large language models, design better prompts, call APIs, structure AI outputs, search meaning with embeddings, build beginner-friendly RAG systems, understand agentic workflows, evaluate AI responses, protect privacy, and use AI responsibly in data science and software development.Inside this volume, readers will learn: How LLMs fit into the Python developer's workflowHow prompts work as instructions, context, and constraintsHow to use APIs safely and practicallyHow structured outputs make AI results more reliableHow embeddings represent meaningHow semantic search finds ideas, not just keywordsHow RAG connects documents with AI responsesHow agents use tools, steps, and feedback loopsHow to evaluate AI outputs instead of trusting them blindlyHow to use AI for data science assistanceHow to use AI for developer productivityHow to think about safety, privacy, bias, and responsible automationHow to build practical AI-assisted capstone projectsThis is not a hype-driven AI book.It is a practical workshop for building with AI calmly and responsibly.By the end of Volume 8, readers will understand how Python, AI models, automation, and human judgment work together.AI is not a replacement for thinking.AI is a tool that becomes powerful when guided by clear thinking This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Etat : new. Paperback. Volume 1 helped you understand Python thinking.Volume 2 helped you build practical programs.Volume 3 teaches you how to organize information so your programs become clearer, smarter, and more useful.Many beginners learn lists, dictionaries, tuples, and sets as separate syntax topics. But real confidence begins when you understand them as shapes of thought.A list is for order.A dictionary is for labels and lookup.A set is for uniqueness.A tuple is for fixed facts.Nested data is for real-world records.Python First Principles for Data Scientists and Developers - Volume 3: The Data Structures Lab breaks these ideas down slowly and practically. Through friendly explanations, interactive dialogues, guided examples, exercises, checklists, mini-projects, and a capstone learning tracker, readers learn how to choose the right structure before writing code.Inside this volume, readers will learn: How lists help store ordered informationHow dictionaries connect keys to meaningHow tuples protect fixed factsHow sets remove duplicates and compare groupsHow strings behave like structured textHow nested data represents real-world recordsHow comprehensions make repeated transformations cleanerHow searching, sorting, counting, and grouping workHow simple algorithm patterns prepare the mind for deeper problem solvingHow data scientists and developers think differently about the same structuresThis is not a rushed syntax reference.It is a practical lab for building structured Python thinking.By the end of Volume 3, readers will understand how information moves through Python programs, how patterns appear in data, and how small structures become the foundation for data science, automation, software tools, and machine learning.Data enters.Structure forms.Insight begins. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…