Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Machine Learning Made Easy PDF full book. Access full book title Machine Learning Made Easy by Timeo Williams. Download full books in PDF and EPUB format.
Author: Timeo Williams Publisher: Panel PR ISBN: Category : Computers Languages : en Pages : 11
Book Description
Discover the power of machine learning with ease! Whether you're a beginner or seasoned pro, "Machine Learning Made Easy" is your go-to guide. From basics to real-world applications, this book breaks down complex concepts into simple, actionable steps. Learn core principles, practical techniques, and apply them to diverse fields like healthcare and finance. With clear explanations and hands-on examples, you'll master machine learning effortlessly. Don't miss out—unlock the potential of machine learning today!
Author: Timeo Williams Publisher: Panel PR ISBN: Category : Computers Languages : en Pages : 11
Book Description
Discover the power of machine learning with ease! Whether you're a beginner or seasoned pro, "Machine Learning Made Easy" is your go-to guide. From basics to real-world applications, this book breaks down complex concepts into simple, actionable steps. Learn core principles, practical techniques, and apply them to diverse fields like healthcare and finance. With clear explanations and hands-on examples, you'll master machine learning effortlessly. Don't miss out—unlock the potential of machine learning today!
Author: John Paul Mueller Publisher: John Wiley & Sons ISBN: 1119724015 Category : Computers Languages : en Pages : 471
Book Description
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.
Author: Dale Lane Publisher: No Starch Press ISBN: 1718500572 Category : Computers Languages : en Pages : 290
Book Description
A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+
Author: M.B. Chatfield Publisher: M.B. Chatfield ISBN: Category : Computers Languages : en Pages : 258
Book Description
Unleash the power of machine learning to automate tasks, make predictions, and solve complex problems. Machine learning is a powerful tool that can be used to automate tasks, make predictions, and solve complex problems. It is used in a wide variety of industries, including healthcare, finance, and manufacturing. Machine Learning Made Easy is the perfect resource for anyone who wants to learn the basics of machine learning. This comprehensive guide covers everything you need to know, from the basics of machine learning algorithms to advanced topics such as deep learning. Whether you're a student, a business professional, or a data enthusiast, Machine Learning Made Easy is the essential resource for learning about machine learning. Here are some of the key topics covered in the book: Introduction to machine learning Types of machine learning algorithms Choosing the right machine learning algorithm Training a machine learning model Evaluating a machine learning model Using machine learning to automate tasks Using machine learning to make predictions If you are a beginner who wants to learn about machine learning, Machine Learning Made Easy is a great place to start.
Author: N. Lewis Publisher: ISBN: 9781546483755 Category : Languages : en Pages : 356
Book Description
Finally, A Blueprint for Machine Learning with R! Machine Learning Made Easy with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating machine learning models with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R. NO EXPERIENCE REQUIRED: This book uses plain language rather than a ton of equations; I'm assuming you never did like linear algebra, don't want to see things derived, dislike complicated computer code, and you're here because you want to try successful machine learning algorithms for yourself. YOUR PERSONAL BLUE PRINT: Through a simple to follow intuitive step by step process, you will learn how to use the most popular machine learning algorithms using R. Once you have mastered the process, it will be easy for you to translate your knowledge to assess your own data. THIS BOOK IS FOR YOU IF YOU WANT: Focus on explanations rather than mathematical derivation Practical illustrations that use real data. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use and try on your own data. TAKE THE SHORTCUT: This guide was written for people just like you. Individuals who want to get up to speed as quickly as possible. to: YOU'LL LEARN HOW TO: Unleash the power of Decision Trees. Develop hands on skills using k-Nearest Neighbors. Design successful applications with Naive Bayes. Deploy Linear Discriminant Analysis. Explore Support Vector Machines. Master Linear and logistic regression. Create solutions with Random Forests. Solve complex problems with Boosting. Gain deep insights via K-Means clustering. Acquire tips to enhance model performance. For each machine learning algorithm, every step in the process is detailed, from preparing the data for analysis, to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R. Everything you need to get started is contained within this book. Machine Learning Made Easy with R is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress!
Author: John Paul Mueller Publisher: John Wiley & Sons ISBN: 1119467586 Category : Computers Languages : en Pages : 60
Book Description
Step into the future with AI The term "Artificial Intelligence" has been around since the 1950s, but a lot has changed since then. Today, AI is referenced in the news, books, movies, and TV shows, and the exact definition is often misinterpreted. Artificial Intelligence For Dummies provides a clear introduction to AI and how it’s being used today. Inside, you’ll get a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field. Learn about what AI has contributed to society Explore uses for AI in computer applications Discover the limits of what AI can do Find out about the history of AI The world of AI is fascinating—and this hands-on guide makes it more accessible than ever!
Author: Sinan Ozdemir Publisher: Packt Publishing Ltd ISBN: 1787286479 Category : Computers Languages : en Pages : 310
Book Description
A perfect guide to speed up the predicting power of machine learning algorithms Key Features Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Book Description Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. What you will learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study Who this book is for If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.
Author: N. D. Lewis Publisher: ISBN: 9781533013620 Category : Languages : en Pages : 218
Book Description
Who Else Wants to Master Deep Learning in Half the Time? Start building smarter models today using R ! Build Deep Learning Models Faster Then You Imagined Possible! This book provides an accessible, hands on, easy to follow guide to building deep learning models in R. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is for you. It is designed for anyone who wishes to gain a practical understanding of the important modeling and prediction techniques that make up the increasingly lucrative discipline of deep learning. NO EXPERIENCE REQUIRED: - Bestselling data scientist Dr. N. D Lewis cuts a clear path through the jargon, opening the way for you to discover, understand, apply and exploit the potential of deep learning in your own research. Following on from the success of the first book in the Deep Learning Made Easy Series, it gives you new deep learning tools to use in your very own research. . YOU'LL LEARN HOW TO: Unleash the power of Kernel Deep Convex Neural Networks. Develop winning solutions with Deep Boosting. Explore, evaluate and exploit Monotone Neural Networks. Design successful solutions with Extreme Learning Machines. Master Deep Autoencoders. Ignite your use of Self-Organizing Polynomial Neural Networks. This hands on text is for individuals who want to master the subject in the minimum amount of time. It leverages the power of the FREE predictive analytic package R to provide you with the necessary tools to maximize your understanding, deepen your knowledge and unleash ideas to enhance your data science projects. THIS BOOK IS FOR YOU IF YOU WANT:: Real world applications that make sense. Examples to stimulate your thinking. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use. Deep Learning Made Easy with R:Volume II is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and join the data science revolution!
Author: John Paul Mueller Publisher: John Wiley & Sons ISBN: 1119543045 Category : Computers Languages : en Pages : 370
Book Description
Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.
Author: Stephen Marsland Publisher: CRC Press ISBN: 1420067192 Category : Business & Economics Languages : en Pages : 407
Book Description
Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but
Author: Timeo Williams
Author: Timeo Williams
Author: John Paul Mueller
Author: Dale Lane
Author: M.B. Chatfield
Author: N. Lewis
Author: John Paul Mueller
Author: Sinan Ozdemir
Author: N. D. Lewis
Author: John Paul Mueller
Author: Stephen Marsland