Machine Learning Made Easy
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Machine Learning Made Easy
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Author : Timeo Williams
language : en
Publisher: Panel PR
Release Date : 2024-02-21
Machine Learning Made Easy written by Timeo Williams and has been published by Panel PR this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-21 with Computers categories.
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!
Machine Learning Made Easy With R
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Author : N. Lewis
language : en
Publisher:
Release Date : 2017-05-07
Machine Learning Made Easy With R written by N. Lewis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-07 with categories.
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!
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Deep Learning Made Easy With R
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Author : N. D. Lewis
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-01-10
Deep Learning Made Easy With R written by N. D. Lewis and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-10 with categories.
Master Deep Learning with this fun, practical, hands on guide. With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations are quickly building out their own teams. If you want to join the ranks of today's top data scientists take advantage of this valuable book. It will help you get started. It reveals how deep learning models work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Bestselling decision scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build the most successful deep learning models used for learning from data. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is the place to get started. YOU'LL LEARN HOW TO: Understand Deep Neural Networks Use Autoencoders Unleash the power of Stacked Autoencoders Leverage the Restricted Boltzmann Machine Develop Recurrent Neural Networks Master Deep Belief Networks Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide - the ultimate cheat sheet for deep learning mastery. A book for everyone interested in machine learning, predictive analytic techniques, neural networks and decision science. Start building smarter models today using R! Buy the book today. Your next big breakthrough using deep learning is only a page away!
Machine Learning Made Easy A Beginner S Guide For All
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Author : M.B. Chatfield
language : en
Publisher: M.B. Chatfield
Release Date :
Machine Learning Made Easy A Beginner S Guide For All written by M.B. Chatfield and has been published by M.B. Chatfield this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
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. #datascience #machinelearning #analyticsforeveryone #dataanalysisforbeginners #data #datavisualization #machinelearning #beginnersguide #learndata #GoogleAnalytics #Google #mobileapp #datavisualization #madeeasy #madesimple
Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10
Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19
Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Machine Learning For Kids
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Author : Dale Lane
language : en
Publisher: No Starch Press
Release Date : 2021-02-09
Machine Learning For Kids written by Dale Lane and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-09 with Computers categories.
A hands-on, application-based introduction to machine learning and artificial intelligence (AI). Create compelling AI-powered games and applications using the Scratch programming language. AI Made Easy with 13 Projects 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 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! Step by easy step, 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 them. You’ll turn your models into 13 fun computer games and apps, including: A Rock, Paper, Scissors game that recognizes your hand shapes A computer character that reacts to insults and compliments An interactive virtual assistant (like Siri or Alexa) A movie recommendation app An AI version of Pac-Man There’s no experience required and step-by-step instructions make sure that anyone can follow along! No Experience Necessary! Ages 12+
Machine Learning Made Simple
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Author : Daniel Lee
language : en
Publisher: Daniel Lee
Release Date : 2025-01-22
Machine Learning Made Simple written by Daniel Lee and has been published by Daniel Lee this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-22 with Computers categories.
"Machine Learning Made Simple: A Practical Introduction: Building Intelligent Algorithms from Scratch" is your go-to resource for comprehending and using machine learning without becoming bogged down in overwhelming complexity or technical jargon. This book, which simplifies the principles of machine learning and provides practical possibilities to develop clever algorithms step-by-step, is ideal for novices and inquisitive learners. The book begins by outlining the fundamental ideas, including supervised and unsupervised learning, and then it progressively guides you through the crucial steps involved in managing data, including cleaning, preprocessing, and visualizing it in order to derive insightful information. You will gain a comprehension of the theory and the ability to apply it on your own by learning how to build fundamental algorithms like linear regression from scratch with an emphasis on practicality. The book provides real-world examples and a case study where you will construct and assess a basic prediction model to further humanize the concepts. As you advance, you'll also discover how to enhance model performance and switch to specialized tools like Scikit-learn, which will allow you to expand your knowledge and skills. This book will enable you to understand the principles and begin developing clever solutions, regardless of whether you're a professional, tech enthusiast, or student interested in machine learning. Take the first step toward becoming an expert in machine learning by diving right in!