Machine Learning And Its Implementation PDF Download
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 And Its Implementation PDF full book. Access full book title Machine Learning And Its Implementation.
Author | : Dr. M. Shanmukhi Marella |
Publisher | : Blue Rose Publishers |
Total Pages | : 116 |
Release | : 2022-09-16 |
Genre | : Computers |
ISBN | : |
Download Machine Learning and Its Implementation Book in PDF, ePub and Kindle
This book will help the students and experienced professionals in understanding the concepts in a much easier way. As the book is more of a practical guide for implementing machine learning rather than just covering the concepts in a theoretical way. In this book, you’ll learn about how to create algorithms in machine learning. It teaches readers how to write code for algorithms. Thanks & Regards
Author | : John Anderson |
Publisher | : |
Total Pages | : |
Release | : 19?? |
Genre | : |
ISBN | : |
Download Proceedings of the international conference on Machine Learning Book in PDF, ePub and Kindle
Author | : Shai Shalev-Shwartz |
Publisher | : Cambridge University Press |
Total Pages | : 415 |
Release | : 2014-05-19 |
Genre | : Computers |
ISBN | : 1107057132 |
Download Understanding Machine Learning Book in PDF, ePub and Kindle
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author | : Shan Suthaharan |
Publisher | : Springer |
Total Pages | : 359 |
Release | : 2015-10-20 |
Genre | : Business & Economics |
ISBN | : 1489976418 |
Download Machine Learning Models and Algorithms for Big Data Classification Book in PDF, ePub and Kindle
This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Author | : Dale Lane |
Publisher | : No Starch Press |
Total Pages | : 290 |
Release | : 2021-01-19 |
Genre | : Computers |
ISBN | : 1718500572 |
Download Machine Learning for Kids Book in PDF, ePub and Kindle
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 | : Jason Brownlee |
Publisher | : Machine Learning Mastery |
Total Pages | : 237 |
Release | : 2016-11-16 |
Genre | : Computers |
ISBN | : |
Download Machine Learning Algorithms From Scratch with Python Book in PDF, ePub and Kindle
You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. No longer. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.
Author | : Jeremy Howard |
Publisher | : O'Reilly Media |
Total Pages | : 624 |
Release | : 2020-06-29 |
Genre | : Computers |
ISBN | : 1492045497 |
Download Deep Learning for Coders with fastai and PyTorch Book in PDF, ePub and Kindle
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
Author | : Stanley Cohen |
Publisher | : Elsevier Health Sciences |
Total Pages | : 290 |
Release | : 2020-06-02 |
Genre | : Medical |
ISBN | : 0323675379 |
Download Artificial Intelligence and Deep Learning in Pathology Book in PDF, ePub and Kindle
Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.
Author | : Rudolph Russell |
Publisher | : Createspace Independent Publishing Platform |
Total Pages | : 106 |
Release | : 2018-05-22 |
Genre | : |
ISBN | : 9781719528405 |
Download Machine Learning Book in PDF, ePub and Kindle
MACHINE LEARNING - PYTHON Buy the Paperback version of this book, and get the Kindle eBook version included for FREE! Do You Want to Become An Expert Of Machine Learning?? Start Getting this Book and Follow My Step by Step Explanations! Click Add To Cart Now! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a practical way, and we'll implement many machine-learning algorithms using the Scikit-learn library in the Python programming language. In the first chapter, you'll learn the most important concepts of machine learning, and, in the next chapter, you'll work mainly with the classification. In the last chapter you'll learn how to train your model. I assume that you've knowledge of the basics of programming This book contains illustrations and step-by-step explanations with bullet points and exercises for easy and enjoyable learning. Benefits of reading this book that you're not going to find anywhere else: Introduction to Machine Learning Classification How to train a Model Different Models Combinations Don't miss out on this new step by step guide to Machine Learning. All you need to do is scroll up and click on the BUY NOW button to learn all about it!
Author | : Gyanendra K. Verma |
Publisher | : Springer Nature |
Total Pages | : 444 |
Release | : 2021-08-19 |
Genre | : Computers |
ISBN | : 9811616817 |
Download Data Science Book in PDF, ePub and Kindle
This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.