Mastering Artificial Intelligence And Machine Learning
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Mastering Artificial Intelligence And Machine Learning
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Author : Dr. L. Sridhara Rao
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2022-12-06
Mastering Artificial Intelligence And Machine Learning written by Dr. L. Sridhara Rao and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-06 with Computers categories.
The book “Mastering Artificial Intelligence and Machine Learning” provides the in-depth knowledge in the field of Artificial Learning, Expert Systems, Natural Language Processing, Deep Learning, Machine Learning etc., to the graduate, post graduate and research scholars. When we talk about Artificial Intelligence, it often evokes a world of robots or futuristic technologies. However, Artificial Intelligence is already part of our daily lives. It is impacting the business world more. Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. Machine learning is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
Mastering Artificial Intelligence And Machine Learning
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Author : Nikhilesh Mishra
language : en
Publisher: Independently Published
Release Date : 2023-08-25
Mastering Artificial Intelligence And Machine Learning written by Nikhilesh Mishra and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-25 with categories.
Embark on an illuminating journey through the captivating realm of "Mastering Artificial Intelligence and Machine Learning: Concepts, Techniques, and Applications" From foundational principles to cutting-edge applications, this comprehensive book equips you with the knowledge and insights to harness the transformative power of AI and ML. Uncover the core principles of AI and ML, from algorithms to predictive modeling. Dive deep into neural networks, deep learning, and natural language processing. Explore real-world applications in healthcare, finance, and more. Discover the ethical dimensions of AI's impact on society. Enhance your growth potential with an exclusive section dedicated to interviews and interviewers, providing valuable insights and skills that amplify your journey towards success. Whether you're a tech enthusiast or a seasoned professional, "Mastering Artificial Intelligence and Machine Learning: Concepts, Techniques, and Applications" empowers you to transform your understanding and become a visionary in shaping the future of technology. Don't miss out-get your copy today and embark on a journey of innovation and knowledge!
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.
Deep Learning With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-05-13
Deep Learning With Python 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 2016-05-13 with Computers categories.
Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
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.
Mastering Machine Learning On Aws
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Author : Dr. Saket S.R. Mengle
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-20
Mastering Machine Learning On Aws written by Dr. Saket S.R. Mengle 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.
Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.
Mastering Artificial Intelligence
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Author : Khushabu Gupta
language : en
Publisher: Subrat Gupta
Release Date : 2024-10-28
Mastering Artificial Intelligence written by Khushabu Gupta and has been published by Subrat Gupta this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-28 with Computers categories.
"Mastering Artificial Intelligence: Learn AI Through Practical Examples & Code" offers readers a detailed, accessible guide to the exciting and fast-evolving world of AI. Designed for both beginners and those with some background in the field, this book covers essential AI concepts, history, applications, and ethical considerations. It delves into machine learning, supervised and unsupervised techniques, deep learning, natural language processing, computer vision, and reinforcement learning. Through clear explanations, practical code examples, and real-world projects, readers will gain hands-on experience with Python, learn the nuances of neural networks, and understand the impact of AI across industries. This book is an indispensable resource for anyone looking to advance their knowledge and career in artificial intelligence.
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.
Mastering Reinforcement Learning With Python
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Author : Enes Bilgin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-12-18
Mastering Reinforcement Learning With Python written by Enes Bilgin 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 2020-12-18 with Computers categories.
Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices Key FeaturesUnderstand how large-scale state-of-the-art RL algorithms and approaches workApply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and moreExplore tips and best practices from experts that will enable you to overcome real-world RL challengesBook Description Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems. What you will learnModel and solve complex sequential decision-making problems using RLDevelop a solid understanding of how state-of-the-art RL methods workUse Python and TensorFlow to code RL algorithms from scratchParallelize and scale up your RL implementations using Ray's RLlib packageGet in-depth knowledge of a wide variety of RL topicsUnderstand the trade-offs between different RL approachesDiscover and address the challenges of implementing RL in the real worldWho this book is for This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.