Explaining the practical know-how for utilizing data required for 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 methods for imputation, removal, and replacement, which preprocessing techniques to use and how to determine that, strategies to prevent leakage (unwanted data contamination), a list of convenient tools for processing, and more... explained by active data scientists! ★ How to Approach Data Analysis and Examples by Field! ⇒ Techniques useful in practice, including common issues that engineers on the ground can relate to and how to address them, are mentioned throughout! ★ How to correctly handle "technologies and methods," 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 recent focus on explainable AI (XAI)? ★ What are the factors behind the failure of many companies 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 operating 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 Approaches to 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 *Please check the website for details 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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