Machine learning is closely related to computational statistics, which focuses on making predictions using computers. Mathematical optimization research provides methods, theories, and applications in the field of machine learning. Data mining is a research field of machine learning, focusing on exploratory data analysis by unsupervised learning. In the application of business problems, machine learning is also called predictive analysis.
This time, I recommend a book about the introduction to machine learning in 2020.
This book explains natural language processing from the basics so that even people who have never learned it can learn it. What kind of processing should be done in advance to process natural language on a computer, how to analyze words and sentences, what kind of processing should be done to execute tasks such as automatic translation , Etc. will be explained gently. This is the best book for those who want to learn natural language processing together with the implementation in the program from now on.
Risks are “uncertain” and “want to avoid”. We will summarize the basic method of how such risks should be treated statistically in the modern age of data science.
Image recognition AI that anyone can make Machine learning model construction made with the latest technology of Google! !! From learning data creation to predictive model construction, tuning, model evaluation, web and application integration. Simple image recognition AI creation using the theory of deep learning, neural models, and machine learning.
Apply machine learning! Open software "Weka" automatically analyzes a large amount of data. Determine which information is most relevant. Crystallized information is automatically predicted. Judgment "quickly" and "accurately" from human decision-making.
At the heart of data analysis technology are analysis algorithms and modeling techniques. However, in the field of practice, we face the importance of "pretreatment". The method depends on the "analysis goal" and "data format", and the success or failure of machine learning depends on how the features are created from them. This book introduces the procedure of preprocessing in machine learning for structured data, image data, time series data, and natural language with "prediction" as the analysis target. After going through the exercises, you will experience the implementation by Python. As the implementation proceeds according to the data analysis framework CRISP-DM, you will acquire preprocessing techniques in a form close to practical use.
A super introductory book that explains the mechanism of AI from the basics! You can understand while moving concretely using Excel!
This book is a book that explains the basics and practical methods of machine learning. We also follow up on the preparation of the machine learning development environment, how to use it in the actual field, and the theoretical part that tends to be black-boxed. It also explains how to use a machine learning model in combination with data aggregation and shaping. The target audience is developers and researchers involved in artificial intelligence-related development. In Chapter 1, this book explains the environment construction required for machine learning and the basics of Python required for machine learning. In Chapter 2, we will explain supervised learning and unsupervised learning based on samples. Chapter 3 describes machine learning models related to supervised and unsupervised learning. The theory of major machine learning models is explained in connection with mathematical formulas, and the coding method in Python based on that theory is explained. Chapter 4 explains how to aggregate and format data and how to use it in an actual machine learning model.
Thorough explanation of new common sense of business that machine learning changes! Explains from basic knowledge and terms to business utilization. You can understand the "now" of machine learning with abundant examples. Full of tips for business introduction. List of companies to watch for machine learning. This is a machine learning manual, a series that is very popular as the latest IT keyword manual. It's often thought of as a esoteric technology, but now machine learning is essential to understanding the efficiency of all businesses. This book provides a wide range of explanations from the basics to the latest knowledge. We will explain in an easy-to-understand manner the history of machine learning, application examples, technologies that support machine learning, and tips for business utilization that can be realized by SMEs and individuals. This book will give you everything business people need to know about machine learning!
A large amount of data is analyzed quickly and deeply, and the Jubatus developer thoroughly explains it! Explained the introduction of Jubatus, the basic concept, and the distributed learning mechanism "MIX". It also explains how to execute in distributed mode, which processes a large amount of data quickly. Detailed explanation of the analysis functions installed in Jubatus, such as classification and regression, with codes. Introducing tips for improving analysis accuracy and pitfalls during analysis!
An unprecedented introductory book that can be easily understood by the shortest route! Explains how to make an algorithm with a consistent procedure of "building a model-> deriving inference".
This book is an introductory book on machine learning written with an emphasis on practice rather than theory so that readers with a programming background can read it without the need for mathematical or theoretical knowledge. It is intended for programmers from the programmer's point of view, avoiding difficult theoretical explanations as much as possible and detailing actual techniques. This book is ideal for programmers who want to acquire practical knowledge and techniques of machine learning that are effective in processing large-scale data.
Practice analysis of actual data with free software. It also covers applied methods such as reinforcement learning, deep learning, and etc. An introductory book useful for analyzing big data that you can experience and understand.
The purpose is to convey the basic idea of using machine learning in natural language processing. It describes essential knowledge carefully selected from the vast field, and is a book that you should definitely read before picking up a dissertation or commentary.
Basic statistical methods for organizing data from the introduction to R, how to measure errors for predictions, machine learning with statistical models, major algorithms in machine learning, sparse learning, etc. Carefully explained To master machine learning, probability A basic theory of data analysis rooted in statistics is indispensable. Therefore, this book explains the basics of probability / statistics and machine learning using statistical models from the beginning of R using the statistical analysis free software "R", which is increasingly used in business. Machine learning is effective for analyzing large amounts of complex data, and is also called a flower-shaped technology for big data processing.
Careful commentary that even beginners of programming can feel at ease. A curriculum where you can enjoy learning by accumulating "done!" Enjoy the sense of accomplishment of "made!" By playing games and machine learning.
Learn how machine learning works Understand the essence of data science.
Underpinning today's artificial intelligence are data, computing environments, algorithms, and programs. Artificial intelligence cannot be created without a huge amount of data. And without a computational environment, algorithms, and programs that process huge amounts of data, artificial intelligence cannot be created. In this book, we will explain from machine learning theory to execution environment, Python programming, and deep learning with concrete data analysis examples.
Presenting a brief explanation of the 50 important basic concepts of statistics and machine learning required for data science and related terms, the minimum mathematical formulas to support them, clear visualization, and the R code to be realized. Promote understanding from many directions. Shows which items of statistics are needed and which are not needed in a series of data science processes of data classification, analysis, modeling, and forecasting, and for important items, their concepts, mathematical support, and programming. Approach from each side. It is possible to efficiently learn and deeply understand the items required for data science.
It systematically explains how to tackle the difficulties encountered in solving actual problems, rather than just listing the fragments of technology related to anomaly detection. Anomaly detection is an important technology that is the first step in catching signs, making quick decisions, and taking the next step.
I have focused on the basics so that beginners can learn the basics of pattern recognition and statistical learning. We have released a C language program on the Web so that we can implement the pattern recognition method, analyze the actual data, and improve the method.
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