컴퓨터/IT 개발/프로그래밍 , 컴퓨터/IT IT 해외원서
[체험판] Hands-On Automated Machine Learning
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- 출간 정보
- 2018.04.26. 전자책 출간
- 파일 정보
- 9.6MB
- 22쪽
- ISBN
- 9781788622288
- ECN
- -
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컴퓨터/IT 개발/프로그래밍 , 컴퓨터/IT IT 해외원서
판매가 | 무료 |
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<[체험판] Hands-On Automated Machine Learning> ▶Book Description
AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners' work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible.
In this book, you'll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning.
By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you'll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.
▶What You Will Learn
⦁ Understand the fundamentals of Automated Machine Learning systems
⦁ Explore auto-sklearn and MLBox for AutoML tasks
⦁ Automate your preprocessing methods along with feature transformation
⦁ Enhance feature selection and generation using the Python stack
⦁ Assemble individual components of ML into a complete AutoML framework
⦁ Demystify hyperparameter tuning to optimize your ML models
⦁ Dive into Machine Learning concepts such as neural networks and autoencoders
⦁ Understand the information costs and trade-offs associated with AutoML
▶Key Features
⦁ Build automated modules for different machine learning components Understand each component of a machine learning pipeline in depth Learn to use different open source AutoML and feature engineering platforms
▶Who This Book Is For
If you're a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You'll also find this book useful if you're an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book.
▶What this book covers
⦁ Chapter 1, Introduction to AutoML, creates a foundation for you to dive into AutoML. We also introduce you to various AutoML libraries.
⦁ Chapter 2, Introduction to Machine Learning Using Python, introduces some machine learning concepts so that you can follow the AutoML approaches easily.
⦁ Chapter 3, Data Preprocessing, provides an in-depth understanding of different data preprocessing methods, what can be automated, and how to automate it. Feature tools and auto-sklearn preprocessing methods will be introduced here.
⦁ Chapter 4, Automated Algorithm Selection, provides guidance on which algorithm works best on which kind of dataset. We learn about the computational complexity and scalability of different algorithms, along with methods to decide the algorithm to use based on training and scoring time. We demonstrate auto-sklearn and how to extend it to include new algorithms.
⦁ Chapter 5, Hyperparameter Optimization, provides you with the required fundamentals on automating hyperparameter tuning a for variety of variables.
⦁ Chapter 6, Creating AutoML Pipelines, explains stitching together various components to create an end-to-end AutoML pipeline.
⦁ Chapter 7, Dive into Deep Learning, introduces you to various deep learning concepts and how they contribute to AutoML.
⦁ Chapter 8, Critical Aspects of ML and Data Science Projects, concludes the discussion and provides information on various trade-offs on the complexity and cost of AutoML projects.
▶Editorial Review
Dear reader, welcome to the world of automated machine learning (ML). Automated ML (AutoML) is designed to automate parts of ML. The readily available AutoML tools make the tasks of data science practitioners easier and are being well received in the advanced analytics community. This book covers the foundations you need to create AutoML modules, and shows how you can get up to speed with them in the most practical way possible.
You will learn to automate different tasks in the ML pipeline, such as data preprocessing, feature selection, model training, model optimization, and much more. The book also demonstrates how to use already available automation libraries, such as auto-sklearn and MLBox, and how to create and extend your own custom AutoML components for ML.
By the end of this book, you will have a clearer understanding of what the different aspects of AutoML are, and will be able to incorporate the automation tasks using practical datasets. The knowledge you get from this book can be leveraged to implement ML in your projects, or to get a step closer to winning an ML competition. We hope that everyone who buys this book finds it worthy and informative.
⦁ Sibanjan Das
Sibanjan Das is a Business Analytics and Data Science consultant. He has extensive experience in IT industry working on ERP systems, implementing predictive analytics solutions in business systems and Internet of Things. An enthusiastic and passionate professional about technology & innovation, he has the passion for wrangling with data from early days of his career. His writings have appeared in various Analytics Magazines and have previously authored a book "Data Science using Oracle Data Miner and Oracle R Enterprise."
Sibanjan holds a Master of IT degree with a major in Business Analytics from Singapore Management University, Singapore and is a Computer Science Engineering graduate from Institute of Technical Education and Research, India. He is a Six Sigma Green Belt from Institute Of Industrial Engineers and also holds several industry certifications such as OCA, OCP, CSCMS, and ITIL V3.
⦁ Umit Mert Cakmak
Umit Cakmak is a Data Scientist at IBM, extensively focusing on IBM Data Science Experience and IBM Watson Machine Learning to solve complex business problems. His research spans across many areas from statistical modeling of financial asset prices to using evolutionary algorithms to improve the performance of machine learning models. Before joining to IBM, he worked on various domains such as high-frequency trading, supply chain management and consulting. He likes to learn from others and also share his insights at universities, conferences and local meet-ups.
▶TABLE of CONTENTS
1: INTRODUCTION TO AUTOML
2: INTRODUCTION TO MACHINE LEARNING USING PYTHON
3: DATA PREPROCESSING
4: AUTOMATED ALGORITHM SELECTION
5: HYPERPARAMETER OPTIMIZATION
6: CREATING AUTOML PIPELINES
7: DIVE INTO DEEP LEARNING
8: CRITICAL ASPECTS OF ML AND DATA SCIENCE PROJECTS
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