Machine Learning Contests A Guidebook

This book systematically introduces the competitions in the field of algorithm and machine learning. The first author of the book has won 5 championships and 5 runner-ups in domestic and international algorithm competitions. Firstly, it takes common competition scenarios as a guide by giving the main processes of using machine learning to solve real-world problems, namely problem modelling, data exploration, feature engineering, model training. And then lists the main points of difficulties, general ideas with solutions in the whole process. Moreover, this book comprehensively covers several common problems in the field of machine learning competitions such as recommendation, temporal prediction, advertising, text computing, etc. The authors, also knew as "competition professionals”, will explain the actual cases in detail and teach you various processes, routines, techniques and strategies, which is a rare treasure book for all competition enthusiasts. It is very suitable for readers who are interested in algorithm competitions and deep learning algorithms in practice, or computer-related majors.

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Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn how Kaggle works and how to make the most of competitions from over 30 expert Kagglers Sharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoML A concise collection of smart data handling techniques for modeling and parameter tuning Book DescriptionMillions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the knowledge they’ve accumulated along the way. As well as Kaggle-specific tips, you’ll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You’ll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!What you will learn Get acquainted with Kaggle as a competition platform Make the most of Kaggle Notebooks, Datasets, and Discussion forums Create a portfolio of projects and ideas to get further in your career Design k-fold and probabilistic validation schemes Get to grips with common and never-before-seen evaluation metrics Understand binary and multi-class classification and object detection Approach NLP and time series tasks more effectively Handle simulation and optimization competitions on Kaggle Who this book is for This book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful. A basic understanding of machine learning concepts will help you make the most of this book.

Building on , this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.

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Are you interested in seeing what deep learning, machine learning, and data analysis are all about and how they are going to be able to help you to get more out of your business and make good decisions about the future of your company? Would you like to see how all of this is going to come together and make you more profitable than ever? This guidebook is going to be the perfect companion and tool for your needs. You will find that we will talk about all of the topics that you need to know when it comes to working with data analysis and data science in no time. And it will not take long before we actually use some of these projects and processes on our own as well. Many companies want to find ways to get ahead of their competition and provide the best options to their customers all at the same time. And they want to make sure that they are making some of the best decisions that you need in order to get ahead in your competition. Some of the highlights of the book include: What is deep learning How to conduct a data analysis The different Python libraries that you are able to use for deep learning. Understanding some of the math behind neural networks. The basics of working with the TensorFlow library that can help you with your deep learning project. How to handle the Keras library for your needs. The PyTorch library and how this library is going to be able to help us out with machine learning and deep learning. Looking more at machine learning and how we are able to fit this into some of the data analysis that we are talking about. How deep learning is going to be helpful when it is time to handle your own predictive analysis. Deep learning, machine learning, and data analysis are important parts of many business today. These topics and processes are going to help us to really explore the industry, the customers, the competition and more that are going to come out when we want to help our business succeed and when we want to figure out what steps we need to take in order to get ahead of the competition. Are you ready to want to master this? Scroll up and click on the BUY NOW button to get your copy now!

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This book summarizes the organized competitions held during the first NIPS competition track. It provides both theory and applications of hot topics in machine learning, such as adversarial learning, conversational intelligence, and deep reinforcement learning. Rigorous competition evaluation was based on the quality of data, problem interest and impact, promoting the design of new models, and a proper schedule and management procedure. This book contains the chapters from organizers on competition design and from top-ranked participants on their proposed solutions for the five accepted competitions: The Conversational Intelligence Challenge, Classifying Clinically Actionable Genetic Mutations, Learning to Run, Human-Computer Question Answering Competition, and Adversarial Attacks and Defenses.