Mastering Machine Learning With Python In Six Steps

DOWNLOAD

Download Mastering Machine Learning With Python In Six Steps PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Mastering Machine Learning With Python In Six Steps book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Mastering Machine Learning With Python In Six Steps

DOWNLOAD
Author : Manohar Swamynathan
language : en
Publisher: Apress
Release Date : 2019-10-01

Mastering Machine Learning With Python In Six Steps written by Manohar Swamynathan and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-01 with Computers categories.

Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You'll Learn Understand machine learning development and frameworks Assess model diagnosis and tuning in machine learning Examine text mining, natuarl language processing (NLP), and recommender systems Review reinforcement learning and CNN Who This Book Is For Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.

Handbook Of Hydroinformatics

DOWNLOAD
Author : Saeid Eslamian
language : en
Publisher: Elsevier
Release Date : 2022-11-30

Handbook Of Hydroinformatics written by Saeid Eslamian and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-30 with Technology & Engineering categories.

Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series. Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo–Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc. It is a fully comprehensive handbook providing all the information needed around classic soft-computing techniques. This volume is a true interdisciplinary work, and the audience includes postgraduates and early career researchers interested in Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering. - Key insights from global contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. - Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. - Introduces classic soft-computing techniques, necessary for a range of disciplines.

Computational Science And Its Applications Iccsa 2022 Workshops

DOWNLOAD
Author : Osvaldo Gervasi
language : en
Publisher: Springer Nature
Release Date : 2022-07-25

Computational Science And Its Applications Iccsa 2022 Workshops written by Osvaldo Gervasi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-25 with Computers categories.

The eight-volume set LNCS 13375 – 13382 constitutes the proceedings of the 22nd International Conference on Computational Science and Its Applications, ICCSA 2022, which was held in Malaga, Spain during July 4 – 7, 2022. The first two volumes contain the proceedings from ICCSA 2022, which are the 57 full and 24 short papers presented in these books were carefully reviewed and selected from 279 submissions. The other six volumes present the workshop proceedings, containing 285 papers out of 815 submissions. These six volumes includes the proceedings of the following workshops: ​ Advances in Artificial Intelligence Learning Technologies: Blended Learning, STEM, Computational Thinking and Coding (AAILT 2022); Workshop on Advancements in Applied Machine-learning and Data Analytics (AAMDA 2022); Advances in information Systems and Technologies for Emergency management, risk assessment and mitigation based on the Resilience (ASTER 2022); Advances in Web Based Learning (AWBL 2022); Blockchain and Distributed Ledgers: Technologies and Applications (BDLTA 2022); Bio and Neuro inspired Computing and Applications (BIONCA 2022); Configurational Analysis For Cities (CA Cities 2022); Computational and Applied Mathematics (CAM 2022), Computational and Applied Statistics (CAS 2022); Computational Mathematics, Statistics and Information Management (CMSIM); Computational Optimization and Applications (COA 2022); Computational Astrochemistry (CompAstro 2022); Computational methods for porous geomaterials (CompPor 2022); Computational Approaches for Smart, Conscious Cities (CASCC 2022); Cities, Technologies and Planning (CTP 2022); Digital Sustainability and Circular Economy (DiSCE 2022); Econometrics and Multidimensional Evaluation in Urban Environment (EMEUE 2022); Ethical AI applications for a human-centered cyber society (EthicAI 2022); Future Computing System Technologies and Applications (FiSTA 2022); Geographical Computing and Remote Sensing for Archaeology (GCRSArcheo 2022); Geodesign in Decision Making: meta planning and collaborative design for sustainable and inclusive development (GDM 2022); Geomatics in Agriculture and Forestry: new advances and perspectives (GeoForAgr 2022); Geographical Analysis, Urban Modeling, Spatial Statistics (Geog-An-Mod 2022); Geomatics for Resource Monitoring and Management (GRMM 2022); International Workshop on Information and Knowledge in the Internet of Things (IKIT 2022); 13th International Symposium on Software Quality (ISSQ 2022); Land Use monitoring for Sustanability (LUMS 2022); Machine Learning for Space and Earth Observation Data (MALSEOD 2022); Building multi-dimensional models for assessing complex environmental systems (MES 2022); MOdels and indicators for assessing and measuring the urban settlement deVElopment in the view of ZERO net land take by 2050 (MOVEto0 2022); Modelling Post-Covid cities (MPCC 2022); Ecosystem Services: nature’s contribution to people in practice. Assessment frameworks, models, mapping, and implications (NC2P 2022); New Mobility Choices For Sustainable and Alternative Scenarios (NEMOB 2022); 2nd Workshop on Privacy in the Cloud/Edge/IoT World (PCEIoT 2022); Psycho-Social Analysis of Sustainable Mobility in The Pre- and Post-Pandemic Phase (PSYCHE 2022); Processes, methods and tools towards RESilient cities and cultural heritage prone to SOD and ROD disasters (RES 2022); Scientific Computing Infrastructure (SCI 2022); Socio-Economic and Environmental Models for Land Use Management (SEMLUM 2022); 14th International Symposium on Software Engineering Processes and Applications (SEPA 2022); Ports of the future - smartness and sustainability (SmartPorts 2022); Smart Tourism (SmartTourism 2022); Sustainability Performance Assessment: models, approaches and applications toward interdisciplinary and integrated solutions (SPA 2022); Specifics of smart cities development in Europe (SPEED 2022); Smart and Sustainable Island Communities (SSIC 2022); Theoretical and Computational Chemistryand its Applications (TCCMA 2022); Transport Infrastructures for Smart Cities (TISC 2022); 14th International Workshop on Tools and Techniques in Software Development Process (TTSDP 2022); International Workshop on Urban Form Studies (UForm 2022); Urban Regeneration: Innovative Tools and Evaluation Model (URITEM 2022); International Workshop on Urban Space and Mobilities (USAM 2022); Virtual and Augmented Reality and Applications (VRA 2022); Advanced and Computational Methods for Earth Science Applications (WACM4ES 2022); Advanced Mathematics and Computing Methods in Complex Computational Systems (WAMCM 2022).

Artificial Intelligence And Industry 5 0

DOWNLOAD
Author :
language : en
Publisher: PHI Learning Pvt. Ltd.
Release Date : 2025-03-01

Artificial Intelligence And Industry 5 0 written by and has been published by PHI Learning Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-01 with Computers categories.

Artificial Intelligence and Industry 5.0 is a textbook that bridges theoretical foundations of AI with its applications in the emerging areas of Industry 5.0. The book is written to provide a foundation for machine learning and deep learning with their applications in natural sciences by providing worked-out examples and exercises. The book takes a balanced approach between the theoretical basis for machine learning and its applications. It covers topics including artificial neural networks, machine learning, supervised and unsupervised learning, deep learning, convolution neural networks, and recurrent neural networks. Besides, the book also includes topics such as pattern recognition, natural language processing and metaheuristic algorithms which will give readers to understand some of the vital areas where AI plays a significant role. The well-explained algorithms and pseudocodes for each topic help students to apply them in their relevant field. The book, besides discussing the topics prescribed in the syllabus, is enriched with the research experience of the authors from different fields, including Theoretical or Computational Chemistry, Bioinformatics, and Computer Sciences, and various training programs conducted for the students/research community. This book is a result of 6 years of group discussions that took place with the groups of eminent professors and researchers in the field. For brief lectures/PPTs, the readers can visit PHI Learning Centre or https://github.com/gnsastry/ACDS-Lectures . KEY FEATURES • Includes topics prescribed in the syllabus as well as the latest research in the field. • The book provides a mathematical foundation and learning techniques in Artificial Intelligence, Machine Learning and Deep Learning. • Each chapter comprises a set of worked-out examples and exercises which are focused on the key concepts. • The book is organized with fundamental concepts and applications in natural sciences, healthcare, drug discovery, environmental sustainability, and more. TARGET AUDIENCE • B.Tech Computer Science and Engineering • B.Tech AI and ML • B.Tech all branches for elective course

Fundamentals Of Data Science

DOWNLOAD
Author : Sanjeev J. Wagh
language : en
Publisher: CRC Press
Release Date : 2021-09-26

Fundamentals Of Data Science written by Sanjeev J. Wagh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-26 with Business & Economics categories.

Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.

Power Engineering And Intelligent Systems

DOWNLOAD
Author : Vivek Shrivastava
language : en
Publisher: Springer Nature
Release Date : 2023-12-15

Power Engineering And Intelligent Systems written by Vivek Shrivastava and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-15 with Technology & Engineering categories.

The book presents a collection of the high-quality research articles in the field of power engineering, grid integration, energy management, soft computing, artificial intelligence, signal and image processing, data science techniques, and their real-world applications. The papers are presented at International Conference on Power Engineering and Intelligent Systems (PEIS 2023), held during June 24–25, 2023, at National Institute of Technology Delhi, India.

Exploring The Role Of Icts In Healthy Aging

DOWNLOAD
Author : Mendes, David
language : en
Publisher: IGI Global
Release Date : 2020-05-31

Exploring The Role Of Icts In Healthy Aging written by Mendes, David and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-31 with Medical categories.

Technological innovation continues to be present in all areas of our lives, offering seemingly endless possibilities. As technology is increasingly implemented in healthcare applications, it is necessary to understand whether users respond to a predefined organizational strategy of model of care or whether they will become wholly dependent on the healthcare technology. This understanding is especially crucial when dealing with the possibility of generating inequities, especially with individuals that are elderly. Exploring the Role of ICTs in Healthy Aging is a collection of innovative research that proposes the detailed study of a strategic framework for the development of technological innovation in healthcare and for its adoption by health organizations. While highlighting topics including emotional health, quality of life, and telemedicine, this book is ideally designed for physicians, nurses, hospital staff, medical professionals, home care providers, hospital administrators, academicians, students, and researchers. Moreover, the book will provide insights and support executives concerned with the management of expertise, knowledge, information, and organizational development in different types of healthcare units at various levels.

Machine Learning With Python Stepbystep

DOWNLOAD
Author : Sachin Srivastava
language : en
Publisher:
Release Date : 2020-11-10

Machine Learning With Python Stepbystep written by Sachin Srivastava and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with categories.

This book is your practical guide towards novice to master in machine learning with Python in six steps. The six steps path has been designed based on the "Six degrees of separation" theory that states that everyone and everything is a maximum of six steps away. Note that the theory deals with the quality of connections, rather than their existence. So a great effort has been taken to design eminent, yet simple six steps covering fundamentals to advanced topics gradually that will help a beginner walk his way from no or least knowledge of machine learning in Python to all the way to becoming a master practitioner. This book is also helpful for current Machine Learning practitioners to learn the advanced topics such as Hyperparameter tuning, various ensemble techniques, Natural Language Processing (NLP), deep learning, and the basics of reinforcement learning.Each topic has two parts: the first part will cover the theoretical concepts and the second part will cover practical implementation with different Python packages. The traditional approach of math to machine learning, that is, learning all the mathematics then understanding how to implement it to solve problems needs a great deal of time/effort, which has proven to be not efficient for working professionals looking to switch careers.Hence the focus in this book has been more on simplification, such that the theory/math behind algorithms have been covered only to the extent required to get you started.I recommend you work with the book instead of reading it. Real learning goes on only through active participation. Hence, all the code presented in the book is available in the form of iPython notebooks to enable you to try out these examples yourselves and extend them to your advantage or interest as required later.This book will serve as a great resource for learning machine learning concepts and implementation techniques for the following:*Python developers or data engineers looking to expand their knowledge or career into the machine learning area.*A current non-Python (R, SAS, SPSS, Matlab, or any other language) machine learning practitioners looking to expand their implementation skills in Python.*Novice machine learning practitioners looking to learn advanced topics such as hyperparameter tuning, various ensemble techniques, Natural Language Processing (NLP), deep learning, and basics of reinforcement learning.

Python Machine Learning

DOWNLOAD
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 $ 17.99 instead of $ 39.97! 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!

Machine Learning With Python

DOWNLOAD
Author : Hein Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-11-23

Machine Learning With Python written by Hein Smith and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-23 with Data mining categories.

Are you stuck in getting started with machine learning with python? A Step-By-Step Guide to Learn and Master Python Machine Learning walks you through steps for getting started with Machine Learning with Python. Python is a popular and open-source programming language. In addition, it is one of the most applied languages in artificial intelligence and other scientific fields. On the other hand, machine learning is a branch of AI that applied algorithms to learn from data and create predictions. Machine learning is important in predicting the world around us.All the way from self-driving cars to predictions in the stock market, there is no place where machine learning cannot be utilized. Today, it is a top skill in high demand in the job market. For that reason, why not grab a Step-By-Step Guide to Learn and Master Python Machine Learning?You'll discover the steps required to develop a successful machine-learning application using Python and Scikit-learn library. As a discipline, ML tries to design and understand computer programs for purpose of prediction. With a Step-By-Step Guide to Learn and Master Python Machine Learning, you'll learn: The important concepts and real-world application of machine learning. Pros and cons of most popular machine learning algorithms The basics of Python Learn about data preprocessing, analysis, and visualization Preprocessing techniques to use in data Regression methods Clustering Recommendation engines And many moreIf you are serious about machine learning with Python and don't know how to get started, A Step-By-Step Guide to Learn and Master Python Machine Learning is your best tool to use.