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  • Machine Learning
    Machine Learning
    Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research.This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics.It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning.Each chapter includes exercises and further reading, so that readers can explore areas of interest.The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors.It is also a useful reference resource for researchers and practitioners of machine learning.
    Price: 54.99 £ | Shipping*: 0.00 £
  • Machine Learning Interviews : Kickstart Your Machine Learning Career
    Machine Learning Interviews : Kickstart Your Machine Learning Career
    As tech products become more prevalent today, the demand for machine learning professionals continues to grow.But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict.In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process. Having served as principal data scientist in several companies, Chang has considerable experience as both ML interviewer and interviewee.She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way.You'll quickly understand how to successfully navigate your way through typical ML interviews. This guide shows you how to:Explore various machine learning roles, including ML engineer, applied scientist, data scientist, and other positionsAssess your interests and skills before deciding which ML role(s) to pursueEvaluate your current skills and close any gaps that may prevent you from succeeding in the interview processAcquire the skill set necessary for each machine learning roleAce ML interview topics, including coding assessments, statistics and machine learning theory, and behavioral questionsPrepare for interviews in statistics and machine learning theory by studying common interview questions
    Price: 63.99 £ | Shipping*: 0.00 £
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    gashapon machine play house candy game machine learning machine candy learning play house learning
    gashapon machine play house candy game machine learning machine candy learning play house learning
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  • Grokking Machine Learning
    Grokking Machine Learning
    It's time to dispel the myth that machine learning is difficult.Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math.No specialist knowledge is required to tackle the hands-on exercises using readily available machine learning tools! In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you.Practical examples illustrate each new concept to ensure you’re grokking as you go.You’ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill.Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Key Features · Different types of machine learning, including supervised and unsupervised learning · Algorithms for simplifying, classifying, and splitting data · Machine learning packages and tools · Hands-on exercises with fully-explained Python code samples For readers with intermediate programming knowledge in Python or a similar language. About the technology Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data.This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art. Luis G. Serrano has worked as the Head of Content for Artificial Intelligence at Udacity and as a Machine Learning Engineer at Google, where he worked on the YouTube recommendations system.He holds a PhD in mathematics from the University of Michigan, a Bachelor and Masters from the University of Waterloo, and worked as a postdoctoral researcher at the University of Quebec at Montreal.He shares his machine learning expertise on a YouTube channel with over 2 million views and 35 thousand subscribers, and is a frequent speaker at artificial intelligence and data science conferences.
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  • Why deep learning compared to machine learning?

    Deep learning is a subset of machine learning that uses neural networks to learn from data. It is more powerful than traditional machine learning techniques because it can automatically discover and learn from complex patterns and features in the data without the need for explicit feature engineering. Deep learning can handle large amounts of data and is capable of learning from unstructured data such as images, audio, and text, making it more versatile and effective for a wide range of applications. Additionally, deep learning models can continuously improve their performance with more data, making them more adaptable and scalable compared to traditional machine learning models.

  • What is the difference between Deep Learning and Machine Learning?

    Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. It involves training these neural networks with large amounts of labeled data to make predictions or decisions. Machine learning, on the other hand, is a broader field that encompasses various techniques and algorithms for computers to learn from data and make predictions without being explicitly programmed. While machine learning can involve simpler algorithms like decision trees or support vector machines, deep learning typically involves more complex neural network architectures and requires a large amount of data for training.

  • Is machine learning just a hype?

    Machine learning is not just a hype, but a rapidly advancing field with real-world applications across various industries. It has proven to be a valuable tool for solving complex problems, making predictions, and automating tasks. The increasing availability of data and computing power has further accelerated the development and adoption of machine learning techniques. As a result, it is becoming an integral part of many businesses and technologies, demonstrating its practical significance beyond just being a passing trend.

  • Does anyone know about machine learning?

    Yes, machine learning is a rapidly growing field in computer science that focuses on developing algorithms and techniques that allow computers to learn from and make predictions or decisions based on data. It has applications in a wide range of industries, including healthcare, finance, and technology. Many companies and researchers are actively working on advancing machine learning techniques and applying them to real-world problems.

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  • Machine Learning Methods
    Machine Learning Methods
    This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods.It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields.In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining.In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics.The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.
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  • A.I. Machine Learning
    A.I. Machine Learning
    Essential exploration of artificial intelligence and its key roles, components, uses, benefits and challenges within a wide range of real-world applications.Dr. Kyle Allison, senior executive, professor, speaker and author who is focused on all things digital uses his multi-faceted knowledge and experience from teaching and consulting to offer valuable and succinct need-to-know facts in 6 laminated pages.Working on digital strategies of all sizes including retailers like Best Buy, Dick’s Sporting Goods, VF Corporation and more, Dr. Allison focuses on the most important functions of AI for business strategy in our famous QuickStudy format that gives more answers per page than any other source.AI has the power to improve performance, ensure quality standards and boost the output of resources.At this price do not miss this eye-opening tool that can offer you direction for the use of AI for everything from data management and finance to customer service, human resources, and more.
    Price: 7.99 £ | Shipping*: 3.99 £
  • Introducing Machine Learning
    Introducing Machine Learning
    Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them.Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project.Next, they introduce Microsoft’s powerful ML.NET library, including capabilities for data processing, training, and evaluation.They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks.The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. · 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you · Explore what’s known about how humans learn and how intelligent software is built · Discover which problems machine learning can address · Understand the machine learning pipeline: the steps leading to a deliverable model · Use AutoML to automatically select the best pipeline for any problem and dataset · Master ML.NET, implement its pipeline, and apply its tasks and algorithms · Explore the mathematical foundations of machine learning · Make predictions, improve decision-making, and apply probabilistic methods · Group data via classification and clustering · Learn the fundamentals of deep learning, including neural network design · Leverage AI cloud services to build better real-world solutions faster About This Book · For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills · Includes examples of machine learning coding scenarios built using the ML.NET library
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  • Python Machine Learning
    Python Machine Learning
    Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This Book• Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization• Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms• Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn• Explore how to use different machine learning models to ask different questions of your data• Learn how to build neural networks using Keras and Theano• Find out how to write clean and elegant Python code that will optimize the strength of your algorithms• Discover how to embed your machine learning model in a web application for increased accessibility• Predict continuous target outcomes using regression analysis• Uncover hidden patterns and structures in data with clustering• Organize data using effective pre-processing techniques• Get to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate.Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace.Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages.If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable.Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions.It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
    Price: 35.99 £ | Shipping*: 0.00 £

  • Is machine learning already artificial intelligence?

    Machine learning is a subset of artificial intelligence. It involves training a machine to learn from data and make predictions or decisions without being explicitly programmed to do so. Artificial intelligence, on the other hand, encompasses a broader range of technologies and applications that enable machines to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, and solving problems. While machine learning is an important component of artificial intelligence, AI also includes other techniques such as natural language processing, computer vision, and robotics.

  • Is a Machine Learning Engineer an engineer?

    Yes, a Machine Learning Engineer is considered an engineer. They apply engineering principles and techniques to design, develop, and deploy machine learning models and systems. Machine Learning Engineers typically have a background in computer science, mathematics, and engineering, and they use their expertise to create innovative solutions using machine learning algorithms and technologies. Their role involves solving complex problems, optimizing algorithms, and building scalable systems, which aligns with the responsibilities of an engineer.

  • Is AWS the standard in machine learning?

    AWS is a major player in the machine learning space, offering a wide range of tools and services for building, training, and deploying machine learning models. However, it is not the only standard in the industry. Other cloud providers such as Google Cloud and Microsoft Azure also offer robust machine learning platforms, and there are open-source tools and frameworks like TensorFlow and PyTorch that are widely used in the machine learning community. Ultimately, the choice of platform depends on the specific needs and preferences of the user or organization.

  • How can AI and machine learning improve videos?

    AI and machine learning can improve videos in several ways. They can enhance video quality by upscaling resolution, reducing noise, and improving color grading. AI can also be used for content analysis, enabling automatic tagging, categorization, and recommendation of videos based on user preferences. Additionally, machine learning algorithms can be used for video editing, such as automated scene detection, object tracking, and even generating personalized video summaries. Overall, AI and machine learning can significantly improve the overall viewing experience and efficiency of video production and distribution.

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