Markov Models

Author: Robert Tier
Publisher: Createspace Independent Publishing Platform
ISBN: 9781544657288
Format: PDF, ePub, Docs
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Discover How to Master Unsupervised Machine Learning and Crack Some of the Greatest Data Enigmas With Markov Models! Would you like to unlock the mysteries of Data Science? Are you yearning to understand how to make educated predictions on the weather, horse races, your unborn baby's facial features, or your boss's next black mood? Would you like a guide to explain these and many other "phenomenons" in clear, easy-to-understand language? If the answer is 'yes' then you'll want to Download this book today! It's never been easier to make predictions and smart analysis with the use of Markov Models. You don't need a crystal ball or any wizardry. The only thing you need is science, some average high-school math skills and a decent knowledge of Python programming in order to solve the most perplexing problems. And if you're unfamiliar with Python programming or Machine learning, don't worry, it'll all be explained in this book. Inside this book I'm going to show you how to be a data master. You'll discover how to solve almost-unsolvable machine learning problems in no time. I'm going to show you the tools, code, and methods needed to effectively use Markov Models for any event or situation you come across. Download This Book Today and Discover: How to program with Python The secrets behind unsupervised machine learning How to use Markov Models to master machine learning How to make predictions with Markov Models How to use Markov Chains How to use Hidden Markov Models The 3 main problems of Markov Models and how to overcome them How to use Python to find the probability of longer and more complex problems What packages to get for using Python for Markov Models How to implement HMM algorithms How to build a speech recognizer A code that will turn gibberish into understandable text How to forecast the weather The secrets behind Queueing Theory The Markov Mutation Model The Secret Structure of Google's PageRank Algorithm How to perform Google PageRank in PythonAnd much, much more! So save yourself some time and frustration trying to learning these intricate algorithms on your own. Let me help you get started quickly and easily. Download Markov Models today and Enjoy Mastering Data Science!

Markov Models

Author: Duo Code
Publisher: Createspace Independent Publishing Platform
ISBN: 9781546999799
Format: PDF
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Do you want to MASTER data science? Learn how MACHINE LEARNING systems can carry out multifaceted processes by learning from data? Understand MARKOV MODELS and how they can help your correctly forecast future events? Want to explore practical implementations of Markov models in PYTHON PROGRAMMING environment? Then you should DOWNLOAD your copy today The aim of machine learning is to train the computers or machine to learn on its own and make informed decisions in a relatively shorter time than what human beings can do. The primary objective of this book is to provide you with all the ins and outs of Markov models and unsupervised machine learning over a range of multi-faceted applications. Specifically, the book will explore practical implementations of Markov models in Python programming environment. You'll discover: - Types of machine learning algorithms - The mathematics behind markov algorithms - Application of markov models in python programming - Application of markov models in - gaming - Speech recognition - Weather reporting and much much more! DOWNLOAD YOUR COPY TODAY TO GAIN A HUGE ADVANTAGE OVER YOUR COMPETITORS

Markov Models Supervised and Unsupervised Machine Learning

Author: William Sullivan
Publisher: Createspace Independent Publishing Platform
ISBN: 9781976050008
Format: PDF, Docs
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Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python BONUS Buy a paperback copy of this book NOW and you will get the Kindle version Absolutely FREE via Kindle Matchbook Do you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events Extend your knowledge of machine learning, python programming & algorithms What you'll Learn Mathematics Behind Markov Algorithms 3 Main Problems Of Markov Models And How To Overcome Them Uses And Applications For Machine Learning Python Programming Speech Recognition Weather Reporting The Markov Rule And Markov's Model Fundamental Axioms Of Statistics And Probability Solutions Theories Artificial Intelligence Bayesian Inference Important Tools Used With HMM And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level You will learn the ins and outs of machine learning, and its real world applications Also, specifically you will discover practical implementations of Markov Models in python programming This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!

Markov Models

Author: Robert Wilson
Publisher: Createspace Independent Publishing Platform
ISBN: 9781548002206
Format: PDF, Mobi
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Do you want to become a data science Savvy? If reading about Markov models, stochastic processes, and probabilities leaves you scratching your head, then you have definitely come to the right place. If you are looking for the most no-nonsense guide that will keep you on the right course during the turbulent ride filled with scientific enigmas, machine learning, and predicting probabilities of hidden, unobservable states, then you have found your perfect companion. This book will Cover: What is Markov models How to make predictions with Markov Models How to learn without supervision How do Markov Models use prediction? Hidden Markov Models and how to use them The secrets of Markov Chains Tips and tricks on how to use Markov Models and machine learning Markov Models with Python Markov Models Examples and predictions How to build and implement HMM algorithms How to use Markov Models to master machine learning The secrets of Supervised and unsupervised machine learning The three components of Hidden Markov Models And much, much more! By the end of this book, I guarantee that you will dive easily into the data science world. Save yourself the hard work and frustration by downloading this book today. Download your free copy today (Kindle Unlimited only)

Markov Models

Author: Dwayne Steven
Publisher:
ISBN: 9781974600243
Format: PDF, Docs
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Have You Ever Wondered How Artificial Intelligence And Data Science Work? Are you looking to learn about machine learning and how it relates to these topics?? Do Hidden Markov Models sound familiar and you want to learn more about them? If so, "Markov's Model And Unsupervised Machine Learning In Python" is THE book for you! It covers all you need to know about Markov's Model and machine learning and how to implement them in Python! Machine learning has become extremely popular over the last decade or two. Everyone from businesses looking for customer profiling and fraud detection, to WEB miners looking to for text mining and document search capabilities, to those working in medicine and astronomy and even on the Human Genome Project have been using machine learning to perform their work. The consulting company, McKinsey, put out a report in 2016 that machine learning's greatest potential across all industries polled lies in its abilities of forecasting and predictive analytics. This means that machine learning could change the face of industries from media to agriculture to automotive and so many in between! If you are a programmer, you don't want to be behind the times - you must learn machine learning programming tools and methods before it's too late, and this is the perfect place to start. What Separates This Book From The Rest? Most other books assume you have a working knowledge of various topics or, alternatively, remain too basic to be useful. This book teaches you from the basics to the intermediate level so that you can grow in understanding as you read. Instead of starting out by assuming you know statistical and probability axioms, we begin by introducing those and move through to explain Markov's Model, Hidden Markov Model problems, and much more! Let's take a brief look at what you will learn by reading this book. You Will Learn The Following: Fundamental Axioms Of Statistics And Probability The Markov Rule And Markov's Model The Hidden Markov Model (HMM) The Three Problems Of HMMs Solutions To The Three HMM Problems What Is Machine Learning? Uses And Applications For Machine Learning Application Of HMMs In Python And The Solutions And much more! So don't delay it any longer. Take this opportunity and invest in this guide now. You will be amazed by how much your understanding of these topics can improve in just this short read! Your journey to understanding machine learning is just beginning! A whole new world of possibilities awaits you! Download This Guide Now! See you inside!

Hands On Markov Models with Python

Author: Ankur Ankan
Publisher: Packt Publishing
ISBN: 9781788625449
Format: PDF, Docs
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Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn Key Features Build a variety of Hidden Markov Models (HMM) Create and apply models to any sequence of data to analyze, predict, and extract valuable insights Use natural language processing (NLP) techniques and 2D-HMM model for image segmentation Book Description Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you've covered the basic concepts of Markov chains, you'll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you'll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you'll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you'll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You'll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you'll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects. What you will learn Explore a balance of both theoretical and practical aspects of HMM Implement HMMs using different datasets in Python using different packages Understand multiple inference algorithms and how to select the right algorithm to resolve your problems Develop a Bayesian approach to inference in HMMs Implement HMMs in finance, natural language processing (NLP), and image processing Determine the most likely sequence of hidden states in an HMM using the Viterbi algorithm Who this book is for Hands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data. Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book

Neuronale Netze selbst programmieren

Author: Tariq Rashid
Publisher: O'Reilly
ISBN: 3960101031
Format: PDF, ePub, Mobi
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Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Sie sind Grundlage vieler Anwendungen im Alltag wie beispielsweise Spracherkennung, Gesichtserkennung auf Fotos oder die Umwandlung von Sprache in Text. Dennoch verstehen nur wenige, wie neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie neuronale Netze arbeiten: - Zunächst lernen Sie die mathematischen Konzepte kennen, die den neuronalen Netzen zugrunde liegen. Dafür brauchen Sie keine tieferen Mathematikkenntnisse, denn alle mathematischen Ideen werden behutsam und mit vielen Illustrationen und Beispielen erläutert. Eine Kurzeinführung in die Analysis unterstützt Sie dabei. - Dann geht es in die Praxis: Nach einer Einführung in die populäre und leicht zu lernende Programmiersprache Python bauen Sie allmählich Ihr eigenes neuronales Netz mit Python auf. Sie bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. - Im nächsten Schritt tunen Sie die Leistung Ihres neuronalen Netzes so weit, dass es eine Zahlenerkennung von 98 % erreicht – nur mit einfachen Ideen und simplem Code. Sie testen das Netz mit Ihrer eigenen Handschrift und werfen noch einen Blick in das mysteriöse Innere eines neuronalen Netzes. - Zum Schluss lassen Sie das neuronale Netz auf einem Raspberry Pi Zero laufen. Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und verständlich, dadurch werden neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Data Science f r Dummies

Author: Lillian Pierson
Publisher: John Wiley & Sons
ISBN: 352780675X
Format: PDF, Kindle
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Daten, Daten, Daten? Sie haben schon Kenntnisse in Excel und Statistik, wissen aber noch nicht, wie all die Datensätze helfen sollen, bessere Entscheidungen zu treffen? Von Lillian Pierson bekommen Sie das dafür notwendige Handwerkszeug: Bauen Sie Ihre Kenntnisse in Statistik, Programmierung und Visualisierung aus. Nutzen Sie Python, R, SQL, Excel und KNIME. Zahlreiche Beispiele veranschaulichen die vorgestellten Methoden und Techniken. So können Sie die Erkenntnisse dieses Buches auf Ihre Daten übertragen und aus deren Analyse unmittelbare Schlüsse und Konsequenzen ziehen.

Python Advanced Guide to Artificial Intelligence

Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1789951720
Format: PDF, Mobi
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Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models in a production environment Book Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe Bonaccorso Mastering TensorFlow 1.x by Armando Fandango Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn Explore how an ML model can be trained, optimized, and evaluated Work with Autoencoders and Generative Adversarial Networks Explore the most important Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Who this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Python Machine Learning Cookbook

Author: Giuseppe Ciaburro
Publisher: Packt Publishing Ltd
ISBN: 1789800757
Format: PDF
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Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key Features Learn and implement machine learning algorithms in a variety of real-life scenarios Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques Find easy-to-follow code solutions for tackling common and not-so-common challenges Book Description This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. What you will learn Use predictive modeling and apply it to real-world problems Explore data visualization techniques to interact with your data Learn how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Get well versed with reinforcement learning, automated ML, and transfer learning Work with image data and build systems for image recognition and biometric face recognition Use deep neural networks to build an optical character recognition system Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.