ISBN: 9781837636303 - principles of data science: a beginner's guide to essential math and coding skills for data fluency and machine learning (19 risultati)

Paperback. Condizione: Very Good. 3. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Paperback. Condizione: Good. Dieser Bildband ist eine Liebeserklarung an Basel: Peter Blattner, geburtiger Basler, hat die Schonheit der Stadt in vielfaltigen Ansichten eingefangen. Nicht nur die mit viel Traditionsbewusstsein erhaltene und gepflegte Altstadt mit ihrer alten Bausubstanz ist in reizvollen Motiven vertreten, sondern auch Blicke auf Hafen und Messe. Auch unauffallige, aber beachtenswerte Details, die zuweilen Humor bergen, kommen ins Bild und damit in den Blick. Ein Stadtspaziergang, der zur Nachahmung animiert.…

Paperback. Condizione: Good. Dieser Bildband ist eine Liebeserklarung an Basel: Peter Blattner, geburtiger Basler, hat die Schonheit der Stadt in vielfaltigen Ansichten eingefangen. Nicht nur die mit viel Traditionsbewusstsein erhaltene und gepflegte Altstadt mit ihrer alten Bausubstanz ist in reizvollen Motiven vertreten, sondern auch Blicke auf Hafen und Messe. Auch unauffallige, aber beachtenswerte Details, die zuweilen Humor bergen, kommen ins Bild und damit in den Blick. Ein Stadtspaziergang, der zur Nachahmung animiert.…

Condizione: New.

Paperback or Softback. Condizione: New. Principles of Data Science - Third Edition: A beginner's guide to essential math and coding skills for data fluency and machine learning. Book.

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Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. Principles of Data Science provides an end-to-end framework for cultivating critical thinking about data, performing practical data science, building performant machine learning models, and mitigating bias in AI pipelines. Learn the fundamentals of computational math and stats while exploring modern machine learning and large pre-trained models.…

Condizione: New. In English.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. 3rd ed. edition NO-PA16APR2015-KAP.

Paperback. Condizione: New. Principles of Data Science provides an end-to-end framework for cultivating critical thinking about data, performing practical data science, building performant machine learning models, and mitigating bias in AI pipelines. Learn the fundamentals of computational math and stats while exploring modern machine learning and large pre-trained models.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Print on Demand.

Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Transform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your dataKey Features: Learn practical data science combined with data theory to gain maximum insights from data Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models Explore actionable case studies to put your new skills to use immediately Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you'll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you'll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.What You Will Learn: Master the fundamentals steps of data science through practical examples Bridge the gap between math and programming using advanced statistics and ML Harness probability, calculus, and models for effective data control Explore transformative modern ML with large language models Evaluate ML success with impactful metrics and MLOps Create compelling visuals that convey actionable insights Quantify and mitigate biases in data and ML modelsWho this book is for:If you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you'll find this book useful. Familiarity with Python programming will further enhance your learning experience.Table of Contents Data Science Terminology Types of Data The Five Steps of Data Science Basic Mathematics Impossible or Improbable - A Gentle Introduction to Probability Advanced Probability What are the Chances An Introduction to Statistics Advanced Statistics Communicating Data How to Tell if Your Toaster is Learning - Machine Learning Essentials Predictions Don't Grow on Trees, or Do They Introduction to Transfer Learning and Pre-trained Models Mitigating Algorithmic Bias and Tackling Model and Data Drift AI Governance Navigating Real-World Data Science Case Studies in Action.…