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Machine Learning for Auditors - Maris Sekar - Bog - APress - Booktok.dk

Machine Learning for Auditors - Maris Sekar - Bog - APress - Booktok.dk

Use artificial intelligence (AI) techniques to build tools for auditing your organization. This is a practical book with implementation recipes that demystify AI, ML, and data science and their roles as applied to auditing. You will learn about data analysis techniques that will help you gain insights into your data and become a better data storyteller. The guidance in this book around applying artificial intelligence in support of audit investigations helps you gain credibility and trust with your internal and external clients. A systematic process to verify your findings is also discussed to ensure the accuracy of your findings. Machine Learning for Auditors provides an emphasis on domain knowledge over complex data science know how that enables you to think like a data scientist. The book helps you achieve the objectives of safeguarding the confidentiality, integrity, and availability of your organizational assets. Data science does not need to be an intimidating concept for audit managers and directors. With the knowledge in this book, you can leverage simple concepts that are beyond mere buzz words to practice innovation in your team. You can build your credibility and trust with your internal and external clients by understanding the data that drives your organization. What You Will LearnUnderstand the role of auditors as trusted advisorsPerform exploratory data analysis to gain a deeper understanding of your organizationBuild machine learning predictive models that detect fraudulent vendor payments and expensesIntegrate data analytics with existing and new technologiesLeverage storytelling to communicate and validate your findings effectivelyApply practical implementation use cases within your organizationWho This Book Is ForAI Auditing is for internal auditors who are looking to use data analytics and data science to better understand their organizational data. It is for auditors interested in implementing predictive and prescriptive analytics in support of better decision making and risk-based testing of your organizational processes.

DKK 429.00
4

Beginning Machine Learning in iOS - Mohit Thakkar - Bog - APress - Booktok.dk

Beginning Machine Learning in iOS - Mohit Thakkar - Bog - APress - Booktok.dk

Implement machine learning models in your iOS applications. This short work begins by reviewing the primary principals of machine learning and then moves on to discussing more advanced topics, such as CoreML, the framework used to enable machine learning tasks in Apple products. Many applications on iPhone use machine learning: Siri to serve voice-based requests, the Photos app for facial recognition, and Facebook to suggest which people that might be in a photo. You''ll review how these types of machine learning tasks are implemented and performed so that you can use them in your own apps. Beginning Machine Learning in iOS is your guide to putting machine learning to work in your iOS applications. What You''ll Learn - Understand the CoreML components - Train custom models - Implement GPU processing for better computation efficiency - Enable machine learning in your application Who This Book Is For Novice developers and programmers who wish to implement machine learning in their iOS applications and those who want to learn the fundamentals about machine learning.

DKK 263.00
4

Machine Learning Applications Using Python - Puneet Mathur - Bog - APress - Booktok.dk

Machine Learning Applications Using Python - Puneet Mathur - Bog - APress - Booktok.dk

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you''ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You''ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will Learn - Discover applied machine learning processes and principles - Implement machine learning in areas of healthcare, finance, and retail - Avoid the pitfalls of implementing applied machine learning - Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.

DKK 596.00
4

Machine Learning for Decision Makers - Bog af Patanjali Kashyap - Paperback

Machine Learning for Decision Makers - Bog af Patanjali Kashyap - Paperback

This new and updated edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry problems.This second edition builds upon the foundation of the first book, revises all of the chapters, and updates the research, case studies, and practical examples to bring the book up to date with changes..

DKK 467.95
4

MATLAB Machine Learning Recipes - Stephanie Thomas - Bog - APress - Booktok.dk

MATLAB Machine Learning Recipes - Stephanie Thomas - Bog - APress - Booktok.dk

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution. This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What You Will LearnWrite code for machine learning, adaptive control, and estimation using MATLABUse MATLAB graphics and visualization tools for machine learningBecome familiar with neural netsBuild expert systemsUnderstand adaptive controlGain knowledge of Kalman FiltersWho This Book Is ForSoftware engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists.

DKK 471.00
4

MATLAB Machine Learning Recipes - Michael Paluszek - Bog - APress - Plusbog.dk

MATLAB Machine Learning Recipes - Michael Paluszek - Bog - APress - Plusbog.dk

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What you''ll learn: - How to write code for machine learning, adaptive control and estimation using MATLAB - How these three areas complement each other - How these three areas are needed for robust machine learning applications - How to use MATLAB graphics and visualization tools for machine learning - How to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.

DKK 375.00
1

Adaptive Machine Learning Algorithms with Python - Chanchal Chatterjee - Bog - APress - Booktok.dk

Adaptive Machine Learning Algorithms with Python - Chanchal Chatterjee - Bog - APress - Booktok.dk

Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment. What You Will LearnApply adaptive algorithms to practical applications and examplesUnderstand the relevant data representation features and computational models for time-varying multi-dimensional dataDerive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real dataSpeed up your algorithms and put them to use on real-world stationary and non-stationary dataMaster the applications of adaptive algorithms on critical edge device computation applicationsWho This Book Is ForMachine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.

DKK 388.00
4

Practical Machine Learning with Rust - Joydeep Bhattacharjee - Bog - APress - Plusbog.dk

Practical Machine Learning with Rust - Joydeep Bhattacharjee - Bog - APress - Plusbog.dk

Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will LearnWrite machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.

DKK 385.00
3

Machine Learning with PySpark - Pramod Singh - Bog - APress - Plusbog.dk

Machine Learning with PySpark - Pramod Singh - Bog - APress - Plusbog.dk

Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You''ll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You''ll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You''ll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark''s latest ML library. After completing this book, you will understand how to use PySpark''s machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications What you will learn: - Build a spectrum of supervised and unsupervised machine learning algorithms - Use PySpark''s machine learning library to implement machine learning and recommender systems - Leverage the new features in PySpark''s machine learning library - Understand data processing using Koalas in Spark - Handle issues around feature engineering, class balance, bias and variance, and cross validation to build optimally fit models Who This Book Is For Data science and machine learning professionals.

DKK 509.00
4

Machine Learning Using R - Abhishek Singh - Bog - APress - Booktok.dk

Machine Learning Using R - Abhishek Singh - Bog - APress - Booktok.dk

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. What You''ll Learn - Understand machine learning algorithms using R - Master the process of building machine-learning models - Cover the theoretical foundations of machine-learning algorithms - See industry focused real-world use cases - Tackle time series modeling in R - Apply deep learning using Keras and TensorFlow in R Who This Book is For Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.

DKK 512.00
4

Machine Learning with Microsoft Technologies - Leila Etaati - Bog - APress - Booktok.dk

Machine Learning with Microsoft Technologies - Leila Etaati - Bog - APress - Booktok.dk

Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today''s game changer and should be a key building block in every company''s strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set. Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. What You''ll Learn - - Choose the right Microsoft product for your machine learning solution - Create and manage Microsoft''s tool environments for development, testing, and production of a machine learning project - Implement and deploy supervised and unsupervised learning in Microsoft products - - Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning - - Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more - - Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing Who This Book Is For Data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set.

DKK 116.00
4