Master Machine Learning Algorithms
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Master Machine Learning Algorithms
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-03-04
Master Machine Learning Algorithms written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-04 with Computers categories.
You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.
Hands On Deep Learning Algorithms With Python
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Author : Sudharsan Ravichandiran
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-07-25
Hands On Deep Learning Algorithms With Python written by Sudharsan Ravichandiran 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 2019-07-25 with Computers categories.
Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications. Key FeaturesGet up-to-speed with building your own neural networks from scratch Gain insights into the mathematical principles behind deep learning algorithmsImplement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlowBook Description Deep learning is one of the most popular domains in the AI space, allowing you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles behind it, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into RNNs and LSTM and how to generate song lyrics with RNN. Next, you will master the math for convolutional and capsule networks, widely used for image recognition tasks. Then you learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Afterward, you will explore various GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects. What you will learnImplement basic-to-advanced deep learning algorithmsMaster the mathematics behind deep learning algorithmsBecome familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and NadamImplement recurrent networks, such as RNN, LSTM, GRU, and seq2seq modelsUnderstand how machines interpret images using CNN and capsule networksImplement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGANExplore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAEWho this book is for If you are a machine learning engineer, data scientist, AI developer, or simply want to focus on neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming, will also find the book very helpful.
Machine Learning
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Author : Kamal Kant Hiran
language : en
Publisher: BPB Publications
Release Date : 2021-09-16
Machine Learning written by Kamal Kant Hiran and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-16 with Computers categories.
Concepts of Machine Learning with Practical Approaches. KEY FEATURES ● Includes real-scenario examples to explain the working of Machine Learning algorithms. ● Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks. ● Full of Python codes, numerous exercises, and model question papers for data science students. DESCRIPTION The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches. This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naïve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning. By the end of this book, the reader can understand Machine Learning concepts and easily implement various ML algorithms to real-world problems. WHAT YOU WILL LEARN ● Perform feature extraction and feature selection techniques. ● Learn to select the best Machine Learning algorithm for a given problem. ● Get a stronghold in using popular Python libraries like Scikit-learn, pandas, and matplotlib. ● Practice how to implement different types of Machine Learning techniques. ● Learn about Artificial Neural Network along with the Back Propagation Algorithm. ● Make use of various recommended systems with powerful algorithms. WHO THIS BOOK IS FOR This book is designed for data science and analytics students, academicians, and researchers who want to explore the concepts of machine learning and practice the understanding of real cases. Knowing basic statistical and programming concepts would be good, although not mandatory. TABLE OF CONTENTS 1. Introduction 2. Supervised Learning Algorithms 3. Unsupervised Learning 4. Introduction to the Statistical Learning Theory 5. Semi-Supervised Learning and Reinforcement Learning 6. Recommended Systems
The Master Algorithm
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Author : Pedro Domingos
language : en
Publisher: Penguin UK
Release Date : 2015-09-22
The Master Algorithm written by Pedro Domingos and has been published by Penguin UK this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-22 with Science categories.
A spell-binding quest for the one algorithm capable of deriving all knowledge from data, including a cure for cancer Society is changing, one learning algorithm at a time, from search engines to online dating, personalized medicine to predicting the stock market. But learning algorithms are not just about Big Data - these algorithms take raw data and make it useful by creating more algorithms. This is something new under the sun: a technology that builds itself. In The Master Algorithm, Pedro Domingos reveals how machine learning is remaking business, politics, science and war. And he takes us on an awe-inspiring quest to find 'The Master Algorithm' - a universal learner capable of deriving all knowledge from data.
Machine Learning
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Author : Christopher William
language : en
Publisher:
Release Date : 2019-09-28
Machine Learning written by Christopher William and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-28 with categories.
Machine learning is a branch of artificial intelligence which involves the design and development of systems capable of self-improvements showing an improvement in performance based upon their previous experiences. In other words, these systems can "learn" by processes similar to human learning process. Machine learning algorithms can be classified into two broad categories, supervised and the unsupervised. In supervised learning algorithms, the training data includes both inputs and outputs. The outputs (answers to the problems) are known as targets. These in supervising the machine learning model as it tries to identify trends and patterns underlying your data. In unsupervised learning algorithms, the training data includes inputs only. he targets are not provided. The answers to the inputs have to be discovered through a deep search. There are a number of steps which must be followed during the course of machine learning. These include collecting and preparing the data and training, validating, and then applying the model. When all these steps are completed, you will be able to use your model to make predictions. Machine learning is a new and growing field, and its emergence is a promising answer to the unimaginable quantities of data which will be generated by organizations and individuals during the upcoming years. The predictive capacity of the various machine learning algorithms, is most attractive to businesses, who are rushing to incorporate machine learning into their day-to-day operations. Machine learning can help businesses predict future performance and make necessary adjustments in order to remain stable and even to increase profits. This guide has been complied to take you through the basics of machine learning that includes artificial intelligence, big data and machine learning with python. Here are some of the chapters covered; Definition of machine learning and its categories How different models work on new data Machine Learning Tools Fundamental Algorithms and Concepts of Probability Chapter 7 Data Scrubbing Setting up your data Regression Analysis Clustering Artificial Neural Network Ensemble Modeling Building a Model in Python Model Optimization Practical Codes and Exercises to Use Python And finally, where to go from here! Clearly, the future of machine learning is bright. Machine learning models can make the work of human beings easier. This fact alone should be enough to motivate human beings toward learning machine learning.
Mastering Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-05-25
Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso 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 2018-05-25 with Computers categories.
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.
Python Machine Learning
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Author : Andrew Park
language : en
Publisher: Andrew Park
Release Date : 2021-04-27
Python Machine Learning written by Andrew Park and has been published by Andrew Park this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with categories.
★ 55% OFF for Bookstores! NOW at $ 13.49 instead of $ 29.97! LAST DAYS! ★ Do you want to learn how to design and master different Machine Learning algorithms quickly and easily?Your Customers Will Love This Amazing Guide! Today, we live in the era of Artificial Intelligence. Self-driving cars, customized product recommendations, real-time pricing, speech and facial recognition are just a few examples proving this truth. Also, think about medical diagnostics or automation of mundane and repetitive labor tasks; all these highlight the fact that we live in interesting times. From research topics to projects and applications in different stages of production, there is a lot going on in the world of Machine Learning. Machines and automation represent a huge part of our daily life. They are becoming part of our experience and existence. This is Machine Learning. Artificial Intelligence is currently one of the most thriving fields any programmer would wish to delve into, and for a good reason: this is the future! Simply put, Machine Learning is about teaching machines to think and make decisions as we would. The difference between the way machines learn and the way we do is that while for the most part we learn from experiences, machines learn from data. Starting from scratch, Python Machine Learning explains how this happens, how machines build their experience and compounding knowledge. Data forms the core of Machine Learning because within data lie truths whose depths exceed our imagination. The computations machines can perform on data are incredible, beyond anything a human brain could do. Once we introduce data to a machine learning model, we must create an environment where we update the data stream frequently. This builds the machine's learning ability. The more data Machine Learning models are exposed to, the easier it is for these models to expand their potential. Some of the topics that we will discuss inside include: What is Machine Learning and how it is applied in real-world situations Understanding the differences between Machine Learning, Deep Learning, and Artificial Intelligence Supervised learning, unsupervised learning, and semi-supervised learning The place of Regression techniques in Machine Learning, including Linear Regression in Python Machine learning training models How to use Lists and Modules in Python The 12 essential libraries for Machine Learning in Python What is the Tensorflow library Artificial Neural Networks And Much More! While most books only focus on widespread details without going deeper into the different models and techniques, Python Machine Learning explains how to master the concepts of Machine Learning technology and helps you to understand how researchers are breaking the boundaries of Data Science to mimic human intelligence in machines using various Machine Learning algorithms. Even if some concepts of Machine Learning algorithms can appear complex to most computer programming beginners, this book takes the time to explain them in a simple and concise way. Would You Like To Know More? Buy It NOW And Let Your Customers Get Addicted To This Amazing Book!
Mastering Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-01-31
Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso 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 2020-01-31 with Computers categories.
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.
Machine Learning Mathematics
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Author : Samuel Hack
language : en
Publisher: Independently Published
Release Date : 2019-10-14
Machine Learning Mathematics written by Samuel Hack and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with categories.
Master the World of Machine Learning - Even if You're a Complete Beginner. Are you an aspiring entrepreneur? Or are you an amateur software developer looking for a break in the world of machine learning? Then this is the book for you. Machine learning is the way of the future - and breaking into this highly lucrative and ever-evolving field is a great way for your career, or business, to prosper. Inside this guide, you'll find simple, easy-to-follow explanations of the fundamental concepts behind machine learning, from the mathematical and statistical concepts to the programming behind them. With a wide range of comprehensive advice including machine learning models, neural networks, statistics, and much more, this guide is a highly effective tool for mastering this incredible technology. Inside, you will: Learn the Fundamental Concepts of Machine Learning Algorithms, and Their Impact in Resolving Modern Day Business Problems Understand The Four Fundamental Types of Machine Learning Algorithm Master the Concept of "Statistical Learning", a Descriptive Statistics-Based Machine Learning Algorithm Dive into the Development and Application of Six of the Most Popular Supervised and Unsupervised Machine Learning Algorithms, With Details on Linear Regression, Logistic Regression And More Learn Everything You Need to Know about Neural Networks and Data Pipelines Master the Concept of "General Setting of Learning", a Fundamental of Machine Learning Development Overview The Basics, Importance, and Applications of Data Science With Details on the "Team Data Science Process" Lifecycle And Much More! Covering everything you need to know about machine learning, now you can master the mathematics and statistics behind this field and develop your very own neural networks! Whether you want to use machine learning to help your business, or you're a programmer looking to expand your skills, this book is a must-read for anyone interested in the world of machine learning. Buy now to discover how you can master machine learning today! Scroll Up and Click the BUY NOW Button to Get Your Copy!
Mastering Machine Learning With R
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Author : Cory Lesmeister
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-10-28
Mastering Machine Learning With R written by Cory Lesmeister 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 2015-10-28 with Computers categories.
Master machine learning techniques with R to deliver insights for complex projects About This Book Get to grips with the application of Machine Learning methods using an extensive set of R packages Understand the benefits and potential pitfalls of using machine learning methods Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. What You Will Learn Gain deep insights to learn the applications of machine learning tools to the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Familiarize yourself with fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Realize why and how to apply unsupervised learning methods In Detail Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages. Style and approach This is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.