Explaining the practical know-how required for data utilization in the accelerating DX (Digital Transformation) and IoT implementation!
★【The Key to Project Success!】 Practical Techniques for Data Preprocessing Explained ⇒ Methods for feature generation according to data types, techniques for data augmentation and transfer learning, considerations for handling missing values and their imputation, removal, and replacement methods, which preprocessing techniques to use and how to determine that, measures against leakage (the inclusion of unnecessary data), a list of convenient tools for processing, and more... explained by active data scientists! ★ How to Approach Data Analysis and Examples by Field! ⇒ Mentioning practical techniques that engineers on the ground will definitely relate to, including common challenges and their solutions! ★ How to correctly handle "technologies and means," including AI (artificial intelligence), by working backward from customer and company objectives? ★ Steps and considerations for efficiently acquiring high-quality data ★ Which metrics should be used to evaluate machine learning models? What is the recently highlighted explainable AI (XAI)? ★ What are the factors that lead many companies to fail in implementing AI and machine learning? What are the commonalities of successful projects? ★ How should organizations be structured for AI implementation, what know-how is needed for the operation of developed products, and how should business profitability be evaluated?
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"Guidelines for Data Analysis and the Introduction of AI and Machine Learning ~Practical Level Correspondence for Data Collection, Preprocessing, Analysis, and Evaluation Results~" Published: July 8, 2020 Price: 65,000 yen + tax Format: B5 size, 390 pages ISBN: 978-4-86502-191-2
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List price: 65,000 yen + tax
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Table of Contents *For details, please check the website* Chapter 1: Considerations Before Introducing Data Science - To Avoid Making Means the Goal Chapter 2: How to Collect Data and the Thought Process Behind It Chapter 3: Data Preprocessing - From Basics to Practical Processing Chapter 4: Methods for Evaluating Analysis Results Chapter 5: How to Proceed with Data Analysis, Examples of Implementation in the Field, and Proposals Chapter 6: Application to Business
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