Practical Approach for Machine Learning and Deep Learning Algorithms PDF Download

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Practical Approach for Machine Learning and Deep Learning Algorithms

Practical Approach for Machine Learning and Deep Learning Algorithms PDF Author: Pandey Abhishek Kumar
Publisher: BPB Publications
ISBN: 9389328128
Category : Computers
Languages : en
Pages : 280

Book Description
Guide covering topics from machine learning, regression models, neural network to tensor flow Key features Machine learning in MATLAB using basic concepts and algorithms. Deriving and accessing of data in MATLAB and next, pre-processing and preparation of data. Machine learning workflow for health monitoring. The neural network domain and implementation in MATLAB with explicit explanation of code and results. How predictive model can be improved using MATLAB? MATLAB code for an algorithm implementation, rather than for mathematical formula. Machine learning workflow for health monitoring. Description Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing. In this book, we have made an honest effort to make the concepts of machine learning easy and give basic programs in MATLAB right from the installation part. Although the real-time application of machine learning is endless, however, the basic concepts and algorithms are discussed using MATLAB language so that not only graduation students but also researchers are benefitted from it.What will you learn Pre-requisites to machine learning Finding natural patterns in data Building classification methods Data pre-processing in Python Building regression models Creating neural networks Deep learning Who this book is forThe book is basically meant for graduate and research students who find the algorithms of machine learning difficult to implement. We have touched all basic algorithms of machine learning in detail with a practical approach. Primarily, beginners will find this book more effective as the chapters are subdivided in a manner that they find the building and implementation of algorithms in MATLAB interesting and easy at the same time.Table of contents1. Pre-requisite to Machine Learning2. An introduction to Machine Learning3. Finding Natural Patterns in Data4. Building Classification Methods5. Data Pre-Processing in Python6. Building Regression Models7. Creating Neural Networks8. Introduction to Deep LearningAbout the authorAbhishek Kumar Pandey is pursuing his Doctorate in computer science and done M.Tech in Computer Sci. & Engineering. He has been working as an Assistant professor of Computer Science at Aryabhatt Engineering College and Research center, Ajmer and also visiting faculty in Government University MDS Ajmer. He has total Academic teaching experience of more than eight years with more than 50 publications in reputed National and International Journals. His research area includes- Artificial intelligence, Image processing, Computer Vision, Data Mining, Machine Learning. His Blog: http://veenapandey.simplesite.com/His LinkedIn Profile: https://www.linkedin.com/in/abhishek-pandey-ba6a6a64/ Pramod Singh Rathore is M. Tech in Computer Sci. and Engineering from Government Engineering College Ajmer, Rajasthan Technical University, Kota, India. He have been working as an Assistant Professor Computer Science at Aryabhatt Engineering College and Research center, Ajmer and also a visiting faculty in Government University Ajmer. He has authored a book in Network simulation which published worldwide. He has a total academic teaching experience more than 7 years with many publications in reputed national group, CRC USA, and has 40 publications as Research papers and Chapters in reputed National and International E-SCI SCOPUS. His research area includes machine learning, NS2, Computer Network, Mining, and DBMS. Dr S. Balamurugan is the Head of Research and Development, Quants IS & CS, India. Formely, he was the Director of Research and Development at Mindnotix Technologies, India. He has authored/co-authored 33 books and has 200 publications in various international journals and conferences to his credit. He was awarded with Three Post-Doctoral Degrees- Doctor of Science (D.Sc.) degree and Two Doctor of Letters(D.Litt) degrees for his significant contribution to research and development in Engineering, and is the recepient of thee Best Director Award, 2018. His biography is listed in "e;World Book of Researchers"e; 2018, Oxford, UK and in "e;Marquis WHO'S WHO"e; 2018 issue, New Jersey, USA. He carried out a healthcare consultancy project for VGM Hospitals between 2013 and 2016, and his current research projects include "e;Women Empowerment using IoT"e;, "e;Health-Aware Smart Chair"e;, "e;Advanced Brain Simulators for Assisting Physiological Medicine"e;, "e;Designing Novel Health Bands"e; and "e;IoT -based Devices for Assisting Elderly People"e;. His LinkedIn Profile: https://www.linkedin.com/in/dr-s-balamurugan-008a7512/

Unsupervised Learning Algorithms

Unsupervised Learning Algorithms PDF Author: M. Emre Celebi
Publisher: Springer
ISBN: 3319242113
Category : Technology & Engineering
Languages : en
Pages : 558

Book Description
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.

Master Machine Learning Algorithms

Master Machine Learning Algorithms PDF Author: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Category : Computers
Languages : en
Pages : 163

Book Description
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.

Practical Approach for Machine Learning and Deep Learning Algorithms

Practical Approach for Machine Learning and Deep Learning Algorithms PDF Author: Pandey Abhishek Kumar
Publisher: BPB Publications
ISBN: 9389328128
Category : Computers
Languages : en
Pages : 280

Book Description
Guide covering topics from machine learning, regression models, neural network to tensor flow Key features Machine learning in MATLAB using basic concepts and algorithms. Deriving and accessing of data in MATLAB and next, pre-processing and preparation of data. Machine learning workflow for health monitoring. The neural network domain and implementation in MATLAB with explicit explanation of code and results. How predictive model can be improved using MATLAB? MATLAB code for an algorithm implementation, rather than for mathematical formula. Machine learning workflow for health monitoring. Description Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing. In this book, we have made an honest effort to make the concepts of machine learning easy and give basic programs in MATLAB right from the installation part. Although the real-time application of machine learning is endless, however, the basic concepts and algorithms are discussed using MATLAB language so that not only graduation students but also researchers are benefitted from it.What will you learn Pre-requisites to machine learning Finding natural patterns in data Building classification methods Data pre-processing in Python Building regression models Creating neural networks Deep learning Who this book is forThe book is basically meant for graduate and research students who find the algorithms of machine learning difficult to implement. We have touched all basic algorithms of machine learning in detail with a practical approach. Primarily, beginners will find this book more effective as the chapters are subdivided in a manner that they find the building and implementation of algorithms in MATLAB interesting and easy at the same time.Table of contents1. Pre-requisite to Machine Learning2. An introduction to Machine Learning3. Finding Natural Patterns in Data4. Building Classification Methods5. Data Pre-Processing in Python6. Building Regression Models7. Creating Neural Networks8. Introduction to Deep LearningAbout the authorAbhishek Kumar Pandey is pursuing his Doctorate in computer science and done M.Tech in Computer Sci. & Engineering. He has been working as an Assistant professor of Computer Science at Aryabhatt Engineering College and Research center, Ajmer and also visiting faculty in Government University MDS Ajmer. He has total Academic teaching experience of more than eight years with more than 50 publications in reputed National and International Journals. His research area includes- Artificial intelligence, Image processing, Computer Vision, Data Mining, Machine Learning. His Blog: http://veenapandey.simplesite.com/His LinkedIn Profile: https://www.linkedin.com/in/abhishek-pandey-ba6a6a64/ Pramod Singh Rathore is M. Tech in Computer Sci. and Engineering from Government Engineering College Ajmer, Rajasthan Technical University, Kota, India. He have been working as an Assistant Professor Computer Science at Aryabhatt Engineering College and Research center, Ajmer and also a visiting faculty in Government University Ajmer. He has authored a book in Network simulation which published worldwide. He has a total academic teaching experience more than 7 years with many publications in reputed national group, CRC USA, and has 40 publications as Research papers and Chapters in reputed National and International E-SCI SCOPUS. His research area includes machine learning, NS2, Computer Network, Mining, and DBMS. Dr S. Balamurugan is the Head of Research and Development, Quants IS & CS, India. Formely, he was the Director of Research and Development at Mindnotix Technologies, India. He has authored/co-authored 33 books and has 200 publications in various international journals and conferences to his credit. He was awarded with Three Post-Doctoral Degrees- Doctor of Science (D.Sc.) degree and Two Doctor of Letters(D.Litt) degrees for his significant contribution to research and development in Engineering, and is the recepient of thee Best Director Award, 2018. His biography is listed in "e;World Book of Researchers"e; 2018, Oxford, UK and in "e;Marquis WHO'S WHO"e; 2018 issue, New Jersey, USA. He carried out a healthcare consultancy project for VGM Hospitals between 2013 and 2016, and his current research projects include "e;Women Empowerment using IoT"e;, "e;Health-Aware Smart Chair"e;, "e;Advanced Brain Simulators for Assisting Physiological Medicine"e;, "e;Designing Novel Health Bands"e; and "e;IoT -based Devices for Assisting Elderly People"e;. His LinkedIn Profile: https://www.linkedin.com/in/dr-s-balamurugan-008a7512/

Optimizing Hyperparameters for Machine Learning Algorithms in Production

Optimizing Hyperparameters for Machine Learning Algorithms in Production PDF Author: Jonathan Krauß
Publisher: Apprimus Wissenschaftsverlag
ISBN: 3985550743
Category : Technology & Engineering
Languages : en
Pages : 258

Book Description
Machine learning (ML) offers the potential to train data-based models and therefore to extract knowledge from data. Due to an increase in networking and digitalization, data and consequently the application of ML are growing in production. The creation of ML models includes several tasks that need to be conducted within data integration, data preparation, modeling, and deployment. One key design decision in this context is the selection of the hyperparameters of an ML algorithm – regardless of whether this task is conducted manually by a data scientist or automatically by an AutoML system. Therefore, data scientists and AutoML systems rely on hyperparameter optimization (HPO) techniques: algorithms that automatically identify good hyperparameters for ML algorithms. The selection of the HPO technique is of great relevance, since it can improve the final performance of an ML model by up to 62 % and reduce its errors by up to 95 %, compared to computing with default values. As the selection of the HPO technique depends on different domain-specific influences, it becomes more and more popular to use decision support systems to facilitate this selection. Since no approach exists, which covers the requirements from the production domain, the main research question of this thesis was: Can a decision support system be developed that supports in the selecting of HPO techniques in the production domain?

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification PDF Author: Anil Kumar
Publisher: CRC Press
ISBN: 1000091546
Category : Computers
Languages : en
Pages : 194

Book Description
This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.

Machine Learning Algorithms

Machine Learning Algorithms PDF Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1789345480
Category : Computers
Languages : en
Pages : 522

Book Description
Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. This book will act as an entry point for anyone who wants to make a career in Machine Learning. It covers algorithms like Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering.

Mastering Machine Learning Algorithms

Mastering Machine Learning Algorithms PDF Author: Giuseppe Bonaccorso
Publisher: Packt Publishing Ltd
ISBN: 1838821910
Category : Computers
Languages : en
Pages : 798

Book Description
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications Book 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 learn Understand the characteristics of a machine learning algorithm Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains Learn how regression works in time-series analysis and risk prediction Create, 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 GANs Who 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 Algorithms for Problem Solving in Computational Applications: Intelligent Techniques

Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques PDF Author: Kulkarni, Siddhivinayak
Publisher: IGI Global
ISBN: 1466618345
Category : Computers
Languages : en
Pages : 464

Book Description
Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.

Machine Learning Algorithms and Applications

Machine Learning Algorithms and Applications PDF Author: Mettu Srinivas
Publisher: John Wiley & Sons
ISBN: 1119768853
Category : Computers
Languages : en
Pages : 368

Book Description
Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.

Machine Learning Algorithms for Industrial Applications

Machine Learning Algorithms for Industrial Applications PDF Author: Santosh Kumar Das
Publisher: Springer Nature
ISBN: 303050641X
Category : Technology & Engineering
Languages : en
Pages : 315

Book Description
This book explores several problems and their solutions regarding data analysis and prediction for industrial applications. Machine learning is a prominent topic in modern industries: its influence can be felt in many aspects of everyday life, as the world rapidly embraces big data and data analytics. Accordingly, there is a pressing need for novel and innovative algorithms to help us find effective solutions in industrial application areas such as media, healthcare, travel, finance, and retail. In all of these areas, data is the crucial parameter, and the main key to unlocking the value of industry. The book presents a range of intelligent algorithms that can be used to filter useful information in the above-mentioned application areas and efficiently solve particular problems. Its main objective is to raise awareness for this important field among students, researchers, and industrial practitioners.