Exploring Machine Learning A Beginners Perspective
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Exploring Machine Learning A Beginners Perspective
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Author : Dr. Raghuram Bhukya
language : en
Publisher: Horizon Books ( A Division of Ignited Minds Edutech P Ltd)
Release Date : 2021-04-20
Exploring Machine Learning A Beginners Perspective written by Dr. Raghuram Bhukya and has been published by Horizon Books ( A Division of Ignited Minds Edutech P Ltd) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-20 with Computers categories.
Machine learning is a field of Artificial intelligence that provides algorithms those can learn and improve from experiences. Machine learning algorithms are turned as integral parts of today’s digital life. Its applications include recommender systems, targeted campaigns, text categorization, computer vision and auto security systems etc. Machine learning also considered as essential part of data science due to its capability of providing predictive analytics, capability in handling variety of data and suitability for big data applications. Its capability for predictive analytics resulted of its general structure that is building statistical models out of training data. In other hand easy scalability advantage of machine learning algorithms is making them to be suitable for big data applications. The different types of learning algorithms includes supervised learning, unsupervised learning, reinforcement learning, feature learning, rule based learning, Robot or expert system learning, sparse dictionary and anomaly detection. These learning algorithms can be realized by computing models artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, Genetic algorithms and soft computing. The familiar tools to implement machine learning algorithms include Python, R, Matlab, Scala, Clojure and Ruby. Involving of such open source programming languages, tools and social network communities makes the machine learning most progressing filed of computer science. The machine learning life cycle includes defining project objectives, explore the types and format, modeling data to fit for machine learning algorithms, deciding suitable machine learning model and implement and decide best result from data for decision making. These days, machine learning is observing great interest by the society and it has turned as one of the significant responsibility of top level managers to transform their business in the profitable means by exploring its basic functionalities. The world is at the sheer of realizing a situation where machines will work in agreement with human being to work together, operation, and advertise their services in a novel way which is targeted, valuable, and well-versed. In order to achieve this, they can influence machine learning distinctiveness. Dr. Raghuram Bhukya
Machine Learning For Finance
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Author : Saurav Singla
language : en
Publisher: BPB Publications
Release Date : 2021-01-05
Machine Learning For Finance written by Saurav Singla and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-05 with Computers categories.
Understand the essentials of Machine Learning and its impact in financial sector KEY FEATURESÊ _Explore the spectrum of machine learning and its usage. _Understand the NLP and Computer Vision and their use cases. _Understand the Neural Network, CNN, RNN and their applications. _ÊUnderstand the Reinforcement Learning and their applications. _Learn the rising application of Machine Learning in the Finance sector. Ê_Exposure to data mining, data visualization and data analytics. DESCRIPTION The fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this review. Now and again, the advancement has been emotional, opening new ways to deal with long-standing innovation challenges, for example, progresses in PC vision and picture investigation.Ê Ê The book demonstrates how to solve some of the most common issues in the financial industry.Ê The book addresses real-life problems faced by practitioners on a daily basis. The book explains how machine learning works on structured data, text, and images. You will cover the exploration of Nave Bayes, Normal Distribution, Clustering with Gaussian process, advanced neural network, sequence modeling, and reinforcement learning. Later chapters will discuss machine learning use cases in the finance sector and the implications of deep learning. The book ends with traditional machine learning algorithms. Ê Machine Learning has become very important in the finance industry, which is mostly used for better risk management and risk analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions. Machine Learning to empower fintech to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability. WHAT WILL YOU LEARN _ Ê Ê Ê You will grasp the most relevant techniques of Machine Learning for everyday use. _ Ê Ê Ê You will be confident in building and implementing ML algorithms. _ Ê Ê Ê Familiarize the adoption of Machine Learning for your business need. _ Ê Ê Ê Discover more advanced concepts applied in banking and other sectors today. _ Ê Ê Ê Build mastery skillset in designing smart AI applications including NLP, Computer Vision and Deep Learning. WHO THIS BOOK IS FORÊ Data Scientist, Machine Learning Engineers and Individuals who want to adopt machine learning in the financial domain. Practitioners are working in banks, asset management, hedge funds or working the first time in the finance domain. Individuals who want to learn about applications of machine learning in finance or individuals entering the fintech domain. TABLE OF CONTENTS 1.Introduction 2.Naive Bayes, Normal Distribution and Automatic Clustering Processes 3.Machine Learning for Data Structuring 4.Parsing Data Using NLP 5.Computer Vision 6.Neural Network, GBM and Gradient Descent 7.Sequence Modeling 8.Reinforcement Learning For Financial Markets 9.Finance Use Cases 10.Impact of Machine Learning on Fintech 11.Machine Learning in Finance 12.eKYC and Anti-Fraud Policy 13.Uses of Data Mining and Data Visualization 14.Advantages and Disadvantages of Machine Learning 15.Applications of Machine Learning in Other Industries 16.Ethical considerations in Artificial Intelligence 17.Artificial Intelligence in Banking 18.Common Machine Learning Algorithms 19.Frequently Asked Questions
Machine Learning
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Author : Steven Alex
language : en
Publisher:
Release Date : 2019-11-06
Machine Learning written by Steven Alex and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-06 with categories.
★ ★ Buy the Paperback Version of this Book and Get the Kindle Book version for FREE ★ ★ Machine Learning (Update Edition 2019-2020) this Guide is a branch of artificial intelligence, This Machine Learning Series idea is relatively new. A science that researches machines to acquire new knowledge and new skills and to identify existing knowledge. The best way to understand the potential of machine learning is to explore how people and companies are currently taking advantage of it.If you are one of the almost 400 million people with machine learning worldwide, This book offers a method to Techniques! Not every machine learning model uses the same techniques, so training will depend on your approach. Let's consider a few examples: Psychology of learning Machine learning in practice Reinforcement learning Types of machine learning Learning by reinforcement Types of reinforcement The different types of learning This guidebook is going to take some time to explore machine learning, and what it is all about. There are so many different aspects of machine learning and how to make it work for your needs, and all of it is found in this guidebook. Some of the different topics that you will be able to learn about inside include: Neural networks Historical background Why use neural networks? Tasks of neural networks Deep learning Algorithms Starting with python Basic types of data Get access to free software and data sets so you can try out your very own machine learning software. See how advanced machine learning will impact our world in the future! Scroll Up and Click the Buy Now Button!
Machine Learning For Beginners
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Author : James Deep
language : en
Publisher:
Release Date : 2019-11-18
Machine Learning For Beginners written by James Deep and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-18 with categories.
Curious about the world of Machine Learning? Would you like to explore its features? If yes, then keep reading. Machine Learning is defined as the ability of algorithms to work efficiently without explicit programming. This book dives deep in analyzing the types, models, and applications of Machine Learning algorithms, using a tremendous comparative analysis approach. It sets the tone on pointing out the close relationship between the subsects of Artificial Intelligence and Machine Learning. The book also bears in mind the importance of data in Machine Learning by explaining the terminologies used about and the process of Data Mining. There is no Machine Learning without Data Mining. But that's not all, because "Machine Learning for Beginners" takes a trip on the current upsurge of intelligent machines and their application in various sectors of the economy. The book covers the field extensively offering recommendations on the suitable road to take for organizations and the general public. It outlines a different model for predictions in a step-by-step slant of the model, of the algorithmic process, of decision making, and solution. Inside this book you will find: The importance of Artificial intelligence Types of ML Neural & Bayesian networks Machine Learning Models Support Vector Machine Components of Soft Computing Decision Trees classifiers ML Datasets Applications of Data Mining ...and many more amazing and interesting topics! In general terms, this guide is an artifact for people looking to understand the basics of ML, not only from the beginners' viewpoint. If you have not joined the Machine Learning world yet, this is the best moment to do that. Want to know more? Scroll to the top of the page and click the "buy now" button!
Machine Learning
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Author : Ryan Turner
language : en
Publisher: Publishing Factory
Release Date : 2020-04-19
Machine Learning written by Ryan Turner and has been published by Publishing Factory this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-19 with Computers categories.
Are you someone who is interested in how the next generation of machines can help you? Is Artificial Intelligence something to be feared, or do you imagine it that it will change our lives for the better? This book will provide the answers you need. Life is becoming ever more complex as we struggle to keep up with technology and use it to our best advantage. It is also more hectic and less certain, even in some of the mundane aspects of our lives, so that we are constantly trying to keep pace. New advancements in technology are paving the way to making life easier for billions and now things like Machine Learning and AI are changing the way we live. In this book, Machine Learning: The Ultimate Beginner’s Guide to Learn Machine Learning, Artificial Intelligence & Neural Networks Step by Step, you will see how this new technology continuously improves itself, can identify trends and patterns with ease and handles a wide variety of data, with chapters that explore: • Teaching the basic principles of Machine Learning • Why it is important and the many benefits that it provides • How Machine Learning differs from conventional programming • The fundamentals of algorithms • Challenges with Machine Learning and how you can easily overcome them • How it is going to change the future and make life easier • And much more… Machine Learning and AI are more than just science fiction. They are here now and undoubtedly will remain, improving and enhancing our lives in many ways, from the everyday to the vitally important. This book provides a platform that will give you a comprehensive understanding, that is second to none, of machine learning and its place in the world today. Get a copy now and see how Machine Learning will change your life!
Advanced Analytics And Deep Learning Models
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Author : Archana Mire
language : en
Publisher: John Wiley & Sons
Release Date : 2022-05-03
Advanced Analytics And Deep Learning Models written by Archana Mire and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-03 with Computers categories.
Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.
The Applied Tensorflow And Keras Workshop
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Author : Harveen Singh Chadha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-30
The Applied Tensorflow And Keras Workshop written by Harveen Singh Chadha and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-30 with Computers categories.
Cut through the noise and get real results with this workshop for beginners. Use a project-based approach to exploring machine learning with TensorFlow and Keras. Key FeaturesUnderstand the nuances of setting up a deep learning programming environmentGain insights into the common components of a neural network and its essential operationsGet to grips with deploying a machine learning model as an interactive web application with FlaskBook Description Machine learning gives computers the ability to learn like humans. It is becoming increasingly transformational to businesses in many forms, and a key skill to learn to prepare for the future digital economy. As a beginner, you'll unlock a world of opportunities by learning the techniques you need to contribute to the domains of machine learning, deep learning, and modern data analysis using the latest cutting-edge tools. The Applied TensorFlow and Keras Workshop begins by showing you how neural networks work. After you've understood the basics, you will train a few networks by altering their hyperparameters. To build on your skills, you'll learn how to select the most appropriate model to solve the problem in hand. While tackling advanced concepts, you'll discover how to assemble a deep learning system by bringing together all the essential elements necessary for building a basic deep learning system - data, model, and prediction. Finally, you'll explore ways to evaluate the performance of your model, and improve it using techniques such as model evaluation and hyperparameter optimization. By the end of this book, you'll have learned how to build a Bitcoin app that predicts future prices, and be able to build your own models for other projects. What you will learnFamiliarize yourself with the components of a neural networkUnderstand the different types of problems that can be solved using neural networksExplore different ways to select the right architecture for your modelMake predictions with a trained model using TensorBoardDiscover the components of Keras and ways to leverage its features in your modelExplore how you can deal with new data by learning ways to retrain your modelWho this book is for If you are a data scientist or a machine learning and deep learning enthusiast, who is looking to design, train, and deploy TensorFlow and Keras models into real-world applications, then this workshop is for you. Knowledge of computer science and machine learning concepts and experience in analyzing data will help you to understand the topics explained in this book with ease.
Deep Learning With R For Beginners
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Author : Mark Hodnett
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-20
Deep Learning With R For Beginners written by Mark Hodnett and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-20 with Computers categories.
Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key FeaturesGet to grips with the fundamentals of deep learning and neural networksUse R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processingImplement effective deep learning systems in R with the help of end-to-end projectsBook Description Deep learning finds practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The book will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects. This Learning Path includes content from the following Packt products: R Deep Learning Essentials - Second Edition by Joshua F. Wiley and Mark HodnettR Deep Learning Projects by Yuxi (Hayden) Liu and Pablo MaldonadoWhat you will learnImplement credit card fraud detection with autoencodersTrain neural networks to perform handwritten digit recognition using MXNetReconstruct images using variational autoencodersExplore the applications of autoencoder neural networks in clustering and dimensionality reductionCreate natural language processing (NLP) models using Keras and TensorFlow in RPrevent models from overfitting the data to improve generalizabilityBuild shallow neural network prediction modelsWho this book is for This Learning Path is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. A fundamental understanding of R programming and familiarity with the basic concepts of deep learning are necessary to get the most out of this Learning Path.
Machine Learning For Beginners
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Author : Jason Knox
language : en
Publisher:
Release Date : 2020-11-06
Machine Learning For Beginners written by Jason Knox and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-06 with categories.
Thinking about beginning a career in the field of Data Science? Do you want to understand more in depth everything that concerns Machine Learning? Or maybe you're a total newbie eager to start learning this topic from zero or so. Machine Learning is one of the most exciting developments to come out of computer science since its founding. It's dramatically changing society all around us and the new occupation of Data Science which has arisen as a result of the development of Machine Learning has opened up a new career path that guarantees employment that is exciting, at the cutting edge, and guaranteed to be challenging. Maybe you're aware of all the hype but you are quite sure what Machine Learning is. If that's the case you've come to the right place. This book is designed to be a beginner's introduction to the exciting world of Machine Learning and Data Science. In this book we are going to pull the curtain back and reveal the secrets and tools used in these exciting fields. We'll begin by recounting a history of machines and how they are an extension of the human mind and also an extension of human labor. Then we will introduce you to the concept of Machine Learning and explore how it relates to Artificial Intelligence into Deep Learning. You will learn all the different ways that Machine Learning can be applied in the real world in practical circumstances. After this, we will reveal the different types of learning and training that is used in order to get computers to learn how to deal with the real world and become autonomous agents. We will teach you all about Supervised and Unsupervised Learning. You're also going to learn the concepts behind all the major algorithms that are used in Data Science and Machine Learning. Inside you'll discover: What Linear Regression is, and the concept of least squares; Types of learning used to train machines to think and act autonomously; Avoid getting lost in Decision Trees and Random Forests; Understand Logistic Regression; Learn how tools like Clustering are used; Find out some of the recent applications of Machine Learning to the real world; See how Machine Learning is being used in Social Media, Analysis, by Government and by companies like Amazon, Netflix and Google; And much more... So, don't waste anymore time and let's start your journey !!
Deep Learning With Python
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Author : Brian Walker
language : en
Publisher:
Release Date : 2019-06-20
Deep Learning With Python written by Brian Walker and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-20 with categories.
Artificial intelligence takes many shapes and forms. At this point in its evolution, machine learning and deep learning are two of the most common shapes it takes. This is primarily because we are at a point where we have discovered how to create networks of information that can actually be filtered and processed just as a normal human cognitive process would be. Beyond all those shapes and forms of AI, though, this entire concept is built based on a few basic ideas: Information is power Neural networks can imitate the human brain Programmers can create machine programs that enable them to filter information in a specific way, that allows them to draw conclusions and grow their learning based on that Hopefully, the book at hand will help you gain a better understanding of the grand scheme of deep learning - and even more specifically, how deep learning connects to Python, one of the single most popular programming languages of the moment. I have structured the book in a way that allows you to make sense of everything in the most logical way possible. What are you waiting for?.. Grab a copy of this book and start to explore the world of Deep Learning with Python!