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Machine Learning Concepts Techniques and Applications

Machine Learning Concepts Techniques and Applications

Machine Learning: Concepts Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases self-assessments exercises activities numerical problems and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare Education Business Engineering Applications Ethics of machine learning including Bias Fairness Trust Responsibility Basics of Deep learning important deep learning models and applications Plenty of objective questions Use Cases Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students researchers and professionals so that they can formulate the problems prepare data decide features select appropriate machine learning algorithms and do appropriate performance evaluation. | Machine Learning Concepts Techniques and Applications

GBP 140.00
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Applied Machine Learning for Smart Data Analysis

Applied Machine Learning for Smart Data Analysis

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation context awareness analyzing various data sources machine learning algorithms and intelligent services and applications. Further it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning security IoT and data mining the concepts are explained with practical implementation including results. Key Features Follows an algorithmic approach for data analysis in machine learning Introduces machine learning methods in applications Address the emerging issues in computing such as deep learning machine learning Internet of Things and data analytics Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health transportation and Internet applications | Applied Machine Learning for Smart Data Analysis

GBP 125.00
1

Machine Learning Theory and Practice

Machine Learning Theory and Practice

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization tree-based methods including Random Forests and Boosted Trees Artificial Neural Networks including Convolutional Neural Networks (CNNs) reinforcement learning and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid illustrated with figures and examples. For each machine learning method discussed the book presents appropriate libraries in the R programming language along with programming examples. Features: Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding enabling further exploration Presents worked out suitable programming examples thus ensuring conceptual theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth within limits of what can be taught in a short period of time. Thus the book can provide foundations that will empower a student to read advanced books and research papers. | Machine Learning Theory and Practice

GBP 110.00
1

Dualit Espresso Coffee Machine

Dualit Espresso Coffee Machine

Dualit's espresso coffee machine is fast, easy to use, makes consistently good coffee and is compact enough to not take too much space on your worktop. If you need your morning coffee to be coffee shop standard but without the fussm this is the machine for you. With one cup and two cup filters, Dualit's automatic espresso coffee machine is ready to use in just 90 seconds and can produce a single espresso, double espresso or two single espressos at the turn of a dial. The 20 Bar pump ensures a perfect crema every time. An easy to use built-in milk frother makes it possible to create indulgent drinks with lashings of froth such as cappuccinos, lattes, hot chocolates or even babycinos. Dualit's espresso coffee machine can also simply steam milk, enough to liven up a classic Americano. Compatible with both ground coffee and mess-free ESE pods for choice and convenience. The coffee machine also uses manual dosing for ultimate control over your coffee extraction. When your cup has been filled to the desired level, simply stop dispensing.

GBP 119.99
1

Machine Washable Bed Cot Bumpers

Machine Washable Bed Cot Bumpers

These Machine Washable Bed Cot Bumpers are designed specifically for use on Profiling Beds and are machine-washable for Infection Control. To promote maximum levels of comfort, the Bed Cot Bumpers are made from exceptionally soft quilted polyester. The Machine Washable Bed Cot Bumpers have been specially designed to ensure safety by providing padding on either side of the bed, thus reducing the danger of the user becoming injured or trapped. Available in a variety of sizes these Bed Cot Bumpers can be fitted to bed rails of all lengths and heights. These bumpers are machine washable up to 71°C with foam inners and easily attach to the rails thanks to the split base. Features and Benefits: Offers maximum safety Machine Washable for Infection Control Manufactured using soft quilted polyester for maximum comfort Reduces the risk of being injured or trapped Easily attached by hook or loop fixings Specifications: Supplied in pairs Available in 3 different sizes depending on the rails attached to the bed Machine Washable up to 71°C

GBP 101.94
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Stochastic Optimization for Large-scale Machine Learning

Stochastic Optimization for Large-scale Machine Learning

Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms i. e. optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.

GBP 150.00
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