Описание: The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system.
Описание: The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system.
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems
Key Features
Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python
Master the art of data-driven problem-solving with hands-on examples
Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms
Book Description
Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.
The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it. As you advance, you'll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.
By the end of this machine learning book, you'll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You'll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
What you will learn
Understand when to use supervised, unsupervised, or reinforcement learning algorithms
Find out how to collect and prepare your data for machine learning tasks
Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff
Apply supervised and unsupervised algorithms to overcome various machine learning challenges
Employ best practices for tuning your algorithm's hyper parameters
Discover how to use neural networks for classification and regression
Build, evaluate, and deploy your machine learning solutions to production
Who this book is for
This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
Автор: Kolosova, Tatiana , Berestizhevsky, Samuel Название: Supervised Machine Learning ISBN: 0367277328 ISBN-13(EAN): 9780367277321 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises of bootstrapping to create multiple training and testing data sets, design and analysis of statistical experiments and optimal hyper-parameters for ML methods.
Автор: Dr. Dhoot, Dhoot Название: Supervised machine learning for kids (tinker toddlers) ISBN: 1950491072 ISBN-13(EAN): 9781950491070 Издательство: Неизвестно Рейтинг: Цена: 3492.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Tinker Toddlers is a series designed to introduce first nonfiction emerging STEM concepts to babies, toddlers, and preschoolers. Dion is not an ordinary machine. He has a superpower - he can learn. And Aria knows exactly what to teach him. Follow along as she teaches Dion all about cats and dogs.
Автор: Ramasubramanian Karthik, Moolayil Jojo Название: Applied Supervised Learning with R ISBN: 1838556338 ISBN-13(EAN): 9781838556334 Издательство: Неизвестно Рейтинг: Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Applied Supervised Learning with R will make you a pro at identifying your business problem, selecting the best supervised machine learning algorithm to solve it, and fine-tuning your model to exactly deliver your needs without overfitting itself.
Описание: Chapter 1: Introduction to Supervised LearningChapter Goal: Start the journey of the readers on supervised learning No of pages: 30-40 Sub -Topics 1. Machine learning and how is it different from software engineering? 2. Discuss reasons for machine learning being popular 3. Compare between supervised, semi-supervised and unsupervised algorithms 4. Statistical methods to get significant variables 5. The use cases of machine learning and respective use cases for each of supervised, semi-supervised and unsupervised algorithms Chapter 2: Supervised Learning for Regression AnalysisChapter Goal: Embrace the core concepts of supervised learning to predict continuous variables No of pages: 40-50 Sub - Topics 1. Supervised learning algorithms for predicting continuous variables 2. Explain mathematics behind the algorithms 3. Develop Python solution using linear regression, decision tree, random forest, SVM and neural network 4. Measure the performance of the algorithms using r square, RMSE etc. 5. Compare and contrast the performance of all the algorithms 6. Discuss the best practices and the common issues faced like data cleaning, null values etc. Chapter 3: Supervised Learning for Classification ProblemsChapter Goal: Discuss the concepts of supervised learning for solving classification problems No of pages: 30-40 Sub - Topics: 1. Discuss classification problems for supervised learning 2. Examine logistic regression, decision tree, random forest, knn and naпve Bayes. Understand the statistics and mathematics behind each 3. Discuss ROC curve, akike value, confusion matrix, precision/recall etc 4. Compare the performance of all the algorithms 5. Discuss the tips and tricks, best practices and common pitfalls like a bias-variance tradeoff, data imbalance etc. Chapter 4: Supervised Learning for Classification Problems-Advanced Chapter Goal: cover advanced classification algorithms for supervised learning algorithms No of pages:30-40 Sub - Topics: 1. Refresh classification problems for supervised learning 2. Examine gradient boosting and extreme gradient boosting, support vector machine and neural network 3. Compare the performance of all the algorithms 4. Discuss the best practices and common pitfalls, tips and tricks Chapter 5: End-to-End Model DeploymentChapter Goal: guide the reader on the end-to-end process of deploying a supervised learning model in production No of pages:25-30 1. Meaning of model deployment 2. Various steps in the model deployment process 3. Preparations to be made like settings, environment etc. 4. Various use cases in the deployment 5. Practical tips in model deployment
Описание: With the help of advanced machine learning techniques, engaging activities, and detailed code examples, this book will train you to find solutions for challenging data science problems and help you develop the skills needed for feature selection and feature engineering.
Автор: Brandon C. Gegg; C. Steve Suh; Albert C. J. Luo Название: Machine Tool Vibrations and Cutting Dynamics ISBN: 1489997539 ISBN-13(EAN): 9781489997531 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Exploring the fundamentals of cutting dynamics from the perspective of discontinuous systems theory, this volume shows how to use coupling, interaction, and different cutting states to mitigate machining instability and enable better machine tool design.
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