Machine Learning Contests: A Guidebook [electronic resource] /

Chapter 1 First Sight -- Chapter 2 Problem Modeling -- Chapter 3 Data Exploration -- Chapter 4 Characteristic Engineering -- Chapter 5 Model Training -- Chapter 6 Model Fusion -- Chapter 7 User Portrait -- Chapter 8 Actual Combat Case: Elo Merchant -- Chapter 9 time sequence -- Chapter 10 Practical Cases: Global Urban -- Chapter 11 Practical Case: Corporaci .-Corporación Favorita Grocery Sales Forecasting -- Chapter 12 Computing Advertising -- Chapter 13 Practical Cases: Tencent 2018 Advertising Algorithm Contest-Similarity Crowd Expansion -- Chapter 14: TalkingData AdTracking Fraud Detection Challenge -- Chapter 15 Natural Language Processing -- Chapter 16 Practical Case: Quora Question Pairs.

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 suitablefor readers who are interested in algorithm competitions and deep learning algorithms in practice, or computer-related majors.