دانلود کتاب کگل (Kaggle) – مسابقات کارشناسی ارشد علوم داده با یادگیری ماشین، GenAI و LLM، ویرایش دوم

Having competed on Kaggle for so many years, we have experienced highs and lows in many competitions. While on this long journey, we often found ourselves refocusing our efforts on different activities related to Kaggle. Over time, we devoted ourselves not only to competitions but also to creating content and code based on the demands of the data science market and our own professional aspirations. After a certain point in our journey, we started to feel that our combined experience and still-burning passion for competitions could really help other participants who have just begun or who would like to get inspired and make the decision to start, by giving them access to the essential expertise they need to begin their own journey in data science competitions. We then decided to work on a book on Kaggle with a purpose: To offer, in a single place, the best tips for being competitive and approaching most of the problems you may find when participating in Kaggle as well as other data science competitions To offer enough suggestions to allow anyone to reach at least the Expert level in any Kaggle discipline: Competitions, Datasets, Notebooks, or Discussions To provide tips on how to get the most out of Kaggle and leverage this experience for professional growth in data science To gather the most significant perspectives on the experience of participating in competitions in a single source by interviewing Kaggle Masters and Grandmasters and listening to their stories and suggestions In short, we have wrien a book that demonstrates how to participate in competitions successfully and take advantage of all the opportunities Kaggle offers. We present here the second edition of this book, with updated chapters and content. It aims to provide even more help in an evolving landscape where, alongside the established tabular data, time series, computer vision, and NLP competitions, there is a growing number of competitions revolving around AutoML and powerful Large Language Models (LLMs). These newer LLM competitions require skills in fine-tuning models like Llama, Mistral, Gemma, Qwen, and DeepSeek for specific tasks. As in the previous edition, this book is also intended as a practical reference to save you time and effort, thanks to its selection of many competition tips and tricks that are hard to learn about and find on the internet or on Kaggle forums. Nevertheless, the book doesn’t limit itself to providing practical help; it also aspires to help you figure out how to boost your career in data science by participating in competitions. Please note that this book does not teach data science from the ground up. We do not provide detailed explanations of linear regression, random forests, or gradient-boosting functions. Instead, we focus on how to use these methods effectively to achieve the best results in data-related problems. We expect our readers to have a solid foundation in data science topics and at least a basic proficiency in Python usage. If you are still a data science beginner, you must supplement this book with other data science, machine learning, and deep learning textbooks and train on online courses, such as those offered by Kaggle itself or by Massive Open Online Courses (MOOCs) such as edX or Coursera. If you want to practically strengthen your data science knowledge through hands-on experience, challenge yourself with intriguing data problems, and simultaneously build a network of fellow data scientists as passionate as you are, then this is definitely the book for you.