Secrets Of Machine Learning How It Works And What It Means For You
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Secrets Of Machine Learning How It Works And What It Means For You
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Author : Tom Kohn
language : en
Publisher: World Scientific
Release Date : 2024-03-14
Secrets Of Machine Learning How It Works And What It Means For You written by Tom Kohn and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-14 with Computers categories.
Cutting through the mass of technical literature on machine learning and AI and the plethora of fear-mongering books on the rise of killer robots, Secrets of Machine Learning offers a clear-sighted explanation for the informed reader of what this new technology is, what it does, how it works, and why it's so important.The surge in computer processing power along with the sheer quantities of training data available, means machine learning is now possible in ways wholly unthinkable just five years ago. Computers can recognize potential lung cancer better than doctors, detect fraud better than bankers, and create fake video almost impossible to tell from the real thing. And next, they are likely to drive our cars.Journalist and news product manager Tom Kohn gets to the heart of the revolutionary new technology that is developing all around us, explaining with precision how the different facets of machine learning work, how companies are using it, and why it is permeating all parts of society right now. The book guides readers through the arcane science and jargon in a clear and understandable way, but is detailed enough that it doesn't gloss over the hard technical concepts.If you want to know why Siri sometimes misunderstands you, how Netflix recommends your movies, and how machine learning will affect your job — read this book.
Practical Machine Learning With R
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Author : Brindha Priyadarshini Jeyaraman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-08-30
Practical Machine Learning With R written by Brindha Priyadarshini Jeyaraman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-30 with Computers categories.
Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems Key FeaturesGain a comprehensive overview of different machine learning techniquesExplore various methods for selecting a particular algorithmImplement a machine learning project from problem definition through to the final modelBook Description With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it. What you will learnDefine a problem that can be solved by training a machine learning modelObtain, verify and clean data before transforming it into the correct format for usePerform exploratory analysis and extract features from dataBuild models for neural net, linear and non-linear regression, classification, and clusteringEvaluate the performance of a model with the right metricsImplement a classification problem using the neural net packageEmploy a decision tree using the random forest libraryWho this book is for If you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this book very useful. The book will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Proceedings Of The International Conference On Machine Learning
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Author : John Anderson
language : en
Publisher:
Release Date : 19??
Proceedings Of The International Conference On Machine Learning written by John Anderson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 19?? with categories.
Machine Learning For Dummies
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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2021-02-09
Machine Learning For Dummies written by John Paul Mueller and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-09 with Computers categories.
One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.
Databricks Ml In Action
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Author : Stephanie Rivera
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-17
Databricks Ml In Action written by Stephanie Rivera and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-17 with Computers categories.
Get to grips with autogenerating code, deploying ML algorithms, and leveraging various ML lifecycle features on the Databricks Platform, guided by best practices and reusable code for you to try, alter, and build on Key Features Build machine learning solutions faster than peers only using documentation Enhance or refine your expertise with tribal knowledge and concise explanations Follow along with code projects provided in GitHub to accelerate your projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover what makes the Databricks Data Intelligence Platform the go-to choice for top-tier machine learning solutions. Written by a team of industry experts at Databricks with decades of combined experience in big data, machine learning, and data science, Databricks ML in Action presents cloud-agnostic, end-to-end examples with hands-on illustrations of executing data science, machine learning, and generative AI projects on the Databricks Platform. You’ll develop expertise in Databricks' managed MLflow, Vector Search, AutoML, Unity Catalog, and Model Serving as you learn to apply them practically in everyday workflows. This Databricks book not only offers detailed code explanations but also facilitates seamless code importation for practical use. You’ll discover how to leverage the open-source Databricks platform to enhance learning, boost skills, and elevate productivity with supplemental resources. By the end of this book, you'll have mastered the use of Databricks for data science, machine learning, and generative AI, enabling you to deliver outstanding data products.What you will learn Set up a workspace for a data team planning to perform data science Monitor data quality and detect drift Use autogenerated code for ML modeling and data exploration Operationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and Workflows Integrate open-source and third-party applications, such as OpenAI's ChatGPT, into your AI projects Communicate insights through Databricks SQL dashboards and Delta Sharing Explore data and models through the Databricks marketplace Who this book is for This book is for machine learning engineers, data scientists, and technical managers seeking hands-on expertise in implementing and leveraging the Databricks Data Intelligence Platform and its Lakehouse architecture to create data products.
Machine Learning Security With Azure
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Author : Georgia Kalyva
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-28
Machine Learning Security With Azure written by Georgia Kalyva and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-28 with Computers categories.
Implement industry best practices to identify vulnerabilities and protect your data, models, environment, and applications while learning how to recover from a security breach Key Features Learn about machine learning attacks and assess your workloads for vulnerabilities Gain insights into securing data, infrastructure, and workloads effectively Discover how to set and maintain a better security posture with the Azure Machine Learning platform Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWith AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure. This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture. By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.What you will learn Explore the Azure Machine Learning project life cycle and services Assess the vulnerability of your ML assets using the Zero Trust model Explore essential controls to ensure data governance and compliance in Azure Understand different methods to secure your data, models, and infrastructure against attacks Find out how to detect and remediate past or ongoing attacks Explore methods to recover from a security breach Monitor and maintain your security posture with the right tools and best practices Who this book is for This book is for anyone looking to learn how to assess, secure, and monitor every aspect of AI or machine learning projects running on the Microsoft Azure platform using the latest security and compliance, industry best practices, and standards. This is a must-have resource for machine learning developers and data scientists working on ML projects. IT administrators, DevOps, and security engineers required to secure and monitor Azure workloads will also benefit from this book, as the chapters cover everything from implementation to deployment, AI attack prevention, and recovery.
The Secrets Of Success At Work Epub Ebook 2e
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Author : Richard Hall
language : en
Publisher: Pearson UK
Release Date : 2012-09-07
The Secrets Of Success At Work Epub Ebook 2e written by Richard Hall and has been published by Pearson UK this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-07 with Self-Help categories.
These are real secrets. Pearls of wisdom learnt through years of experience in some of the most competitive companies around. They are the secrets that seriously successful people use to get ahead, even when times are tough. Here are ten core principles that you can use to be the very best at what you do. They’re shameless crutches on which to leverage your talent, powerful ways to develop a reputation for excellence and winning strategies that will help ensure your survival in any economic climate. Very few can build a career that gives them what they deserve – but now you can. Success can be yours, once you know the secrets. You need this book to keep sane and get ahead in business today. James Arnold-Baker - founding chairman, Doctors.net.uk and Hothouse Fiction; ex CEO Oxford University Press.
Mastering Azure Machine Learning
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Author : Christoph Körner
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-30
Mastering Azure Machine Learning written by Christoph Körner and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-30 with Computers categories.
Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes Key FeaturesMake sense of data on the cloud by implementing advanced analyticsTrain and optimize advanced deep learning models efficiently on Spark using Azure DatabricksDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)Book Description The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure. What you will learnSetup your Azure Machine Learning workspace for data experimentation and visualizationPerform ETL, data preparation, and feature extraction using Azure best practicesImplement advanced feature extraction using NLP and word embeddingsTrain gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine LearningUse hyperparameter tuning and Azure Automated Machine Learning to optimize your ML modelsEmploy distributed ML on GPU clusters using Horovod in Azure Machine LearningDeploy, operate and manage your ML models at scaleAutomated your end-to-end ML process as CI/CD pipelines for MLOpsWho this book is for This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.
Machine Learning In Production
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Author : Suhas Pote
language : en
Publisher: BPB Publications
Release Date : 2023-04-29
Machine Learning In Production written by Suhas Pote and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-29 with Computers categories.
Deploy, manage, and scale Machine Learning models with MLOps effortlessly KEY FEATURES ● Explore several ways to build and deploy ML models in production using an automated CI/CD pipeline. ● Develop and convert ML apps into Android and Windows apps. ● Learn how to implement ML model deployment on popular cloud platforms, including Azure, GCP, and AWS. DESCRIPTION ‘Machine Learning in Production’ is an attempt to decipher the path to a remarkable career in the field of MLOps. It is a comprehensive guide to managing the machine learning lifecycle from development to deployment, outlining ways in which you can deploy ML models in production. It starts off with fundamental concepts, an introduction to the ML lifecycle and MLOps, followed by comprehensive step-by-step instructions on how to develop a package for ML code from scratch that can be installed using pip. It then covers MLflow for ML life cycle management, CI/CD pipelines, and shows how to deploy ML applications on Azure, GCP, and AWS. Furthermore, it provides guidance on how to convert Python applications into Android and Windows apps, as well as how to develop ML web apps. Finally, it covers monitoring, the critical topic of machine learning attacks, and A/B testing. With this book, you can easily build and deploy machine learning solutions in production. WHAT YOU WILL LEARN ● Master the Machine Learning lifecycle with MLOps. ● Learn best practices for managing ML models at scale. ● Streamline your ML workflow with MLFlow. ● Implement monitoring solutions using whylogs, WhyLabs, Grafana, and Prometheus. ● Use Docker and Kubernetes for ML deployment. WHO THIS BOOK IS FOR Whether you are a Data scientist, ML engineer, DevOps professional, Software engineer, or Cloud architect, this book will help you get your machine learning models into production quickly and efficiently. TABLE OF CONTENTS 1. Python 101 2. Git and GitHub Fundamentals 3. Challenges in ML Model Deployment 4. Packaging ML Models 5. MLflow-Platform to Manage the ML Life Cycle 6. Docker for ML 7. Build ML Web Apps Using API 8. Build Native ML Apps 9. CI/CD for ML 10. Deploying ML Models on Heroku 11. Deploying ML Models on Microsoft Azure 12. Deploying ML Models on Google Cloud Platform 13. Deploying ML Models on Amazon Web Services 14. Monitoring and Debugging 15. Post-Productionizing ML Models