By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

Author: Giuseppe Bonaccorso

Publisher: Packt Publishing Ltd

ISBN: 9781838821913

Category: Computers

Page: 798

View: 675

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.

This book summarizes the state-of-the-art in unsupervised learning.

Author: M. Emre Celebi

Publisher: Springer

ISBN: 9783319242118

Category: Technology & Engineering

Page: 558

View: 142

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.

Currently, the healthcare industry uses Big Data for essential patient care information.

Author: Nikhil Mukund Jagirdar

Publisher:

ISBN: OCLC:1110727339

Category:

Page: 65

View: 499

Currently, the healthcare industry uses Big Data for essential patient care information. Electronic Health Records (EHR) store massive data and are continuously updated with information such as laboratory results, medication, and clinical events. There are various methods by which healthcare data is generated and collected, including databases, healthcare websites, mobile applications, wearable technologies, and sensors. The continuous flow of data will improve healthcare service, medical diagnostic research and, ultimately, patient care. Thus, it is important to implement advanced data analysis techniques to obtain more precise prediction results. Machine Learning (ML) has acquired an important place in Big Healthcare Data (BHD). ML has the capability to run predictive analysis, detect patterns or red flags, and connect dots to enhance personalized treatment plans. Because predictive models have dependent and independent variables, ML algorithms perform mathematical calculations to find the best suitable mathematical equations to predict dependent variables using a given set of independent variables. These model performances depend on datasets and response, or dependent, variable types such as binary or multi-class, supervised or unsupervised. The current research analyzed incremental, or streaming or online, algorithm performance with offline or batch learning (these terms are used interchangeably) using performance measures such as accuracy, model complexity, and time consumption. Batch learning algorithms are provided with the specific dataset, which always constrains the size of the dataset depending on memory consumption. In the case of incremental algorithms, data arrive sequentially, which is determined by hyperparameter optimization such as chunk size, tree split, or hoeffding bond. The model complexity of an incremental learning algorithm is based on a number of parameters, which in turn determine memory consumption.

Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available. Make the greatest investment in yourself by investing in your knowledge!

Machine Learning Second Edition I listened carefully to feedback from customers for my original book, and revamped this new edition. I'm excited to present you the second edition with various high quality diagrams, explanations, extensive information and so much more value packed within. What You'll Learn... Supervised Learning Unsupervised Learning Reinforced Learning Algorithms Decision Tree Random Forest And much, much more! Don't miss out on this opportunity to expand your knowledge base with the second edition of my original machine learning book in the artificial intelligence series. Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available. Make the greatest investment in yourself by investing in your knowledge! Buy Now *Note: For the best visual experience of diagrams it is highly recommend you purchase the paperback version*

Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry: Supervised Learning Algorithms K-Nearest Neighbour Na ve Bayes Regressions Unsupervised Learning ...

Computers can't LEARN... Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a list of detailed instructions and instead use a set of high-level commands which they can apply to many unknown scenarios - these are called algorithms. In practice, they want to give computers the ability to Learn and to ADAPT. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before (or even knew existed). Unfortunately, the true power and applications of today's Machine Learning Algorithms is misunderstood by most people. Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry: Supervised Learning Algorithms K-Nearest Neighbour Na�ve Bayes Regressions Unsupervised Learning Algorithms: Support Vector Machines Decision Trees

This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.

Author: Tarek Amr

Publisher: Packt Publishing Ltd

ISBN: 9781838823580

Category: Computers

Page: 384

View: 709

Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the art of data-driven problem-solving with hands-on examples Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms Book Description Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production. What you will learn Understand when to use supervised, unsupervised, or reinforcement learning algorithms Find out how to collect and prepare your data for machine learning tasks Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff Apply supervised and unsupervised algorithms to overcome various machine learning challenges Employ best practices for tuning your algorithm’s hyper parameters Discover how to use neural networks for classification and regression Build, evaluate, and deploy your machine learning solutions to production Who this book is for This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.

This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how ...

Author: Mohssen Mohammed

Publisher: CRC Press

ISBN: 9781498705394

Category: Computers

Page: 226

View: 546

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms.

Author: Hyatt Saleh

Publisher: Packt Publishing Ltd

ISBN: 9781838985462

Category: Computers

Page: 286

View: 981

With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problems.

Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models. -Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring. -Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading. Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition.

By the end of this book, you'll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.

Author: Aaron Jones

Publisher: Packt Publishing Ltd

ISBN: 9781800206243

Category: Computers

Page: 550

View: 921

Learning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities Key Features Get familiar with the ecosystem of unsupervised algorithms Learn interesting methods to simplify large amounts of unorganized data Tackle real-world challenges, such as estimating the population density of a geographical area Book Description Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you'll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you'll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you'll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights. What you will learn Distinguish between hierarchical clustering and the k-means algorithm Understand the process of finding clusters in data Grasp interesting techniques to reduce the size of data Use autoencoders to decode data Extract text from a large collection of documents using topic modeling Create a bag-of-words model using the CountVectorizer Who this book is for If you are a data scientist who is just getting started and want to learn how to implement machine learning algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the Python programming language is recommended, as you'll be editing classes and functions instead of creating them from scratch.

The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science.

Author: Giuseppe Bonaccorso

Publisher: Packt Publishing Ltd

ISBN: 9781785884511

Category: Computers

Page: 360

View: 596

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. This book develops supervised learning techniques for clustering (hierarchical clustering, non hierarchical clustering, Gaussian Mixture Models, Hidden Markov Models, Nearest Neighbors. KNN Classifiers, cluster visualization, Clusters with Self Organizing Map, Competitive Neural Networks, Competitive Layers, Autoencoders and clustering whit 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.

Author: Giuseppe Bonaccorso

Publisher:

ISBN: OCLC:1142100516

Category:

Page: 798

View: 448

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.

Author: V Kishore AyyadevaraPublish On: 2018-06-30

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models.

Author: V Kishore Ayyadevara

Publisher: Apress

ISBN: 9781484235645

Category: Computers

Page: 372

View: 153

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. What You Will Learn Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning Who This Book Is For Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.

Simply, intelligent machines depend on knowledge to sustain their functionalities
and machine learning offers such a knowledge. In essence, machine learning algorithms are embedded into machines and data streams provided so that ...

Author: Michael W. Berry

Publisher: Springer Nature

ISBN: 9783030224752

Category: Technology & Engineering

Page: 187

View: 667

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent.

Author: Harsh Bhasin

Publisher: BPB Publications

ISBN: 9789389845426

Category: Computers

Page: 262

View: 807

Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms KEY FEATURES ● Understand the types of Machine learning. ● Get familiar with different Feature extraction methods. ● Get an overview of how Neural Network Algorithms work. ● Learn how to implement Decision Trees and Random Forests. ● The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling. DESCRIPTION This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. WHAT WILL YOU LEARN ● Learn how to prepare Data for Machine Learning. ● Learn how to implement learning algorithms from scratch. ● Use scikit-learn to implement algorithms. ● Use various Feature Selection and Feature Extraction methods. ● Learn how to develop a Face recognition system. WHO THIS BOOK IS FOR The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular. TABLE OF CONTENTS 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography

There are many types of machine learning algorithms that can be used to classify
air pollution, identify anomalies, and perform intelligent data analysis. The main
contribution of this paper is to shed light on some specific machine learning ...

Author: Sabu M. Thampi

Publisher: Springer Nature

ISBN: 9789811543012

Category: Computers

Page: 265

View: 132

This book constitutes the refereed proceedings of the First Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, held in Trivandrum, India, in December 2019. The 17 full papers and 6 short papers presented in this volume were thoroughly reviewed and selected from 53 qualified submissions. The papers cover such topics as machine learning, artificial intelligence, Internet of Things, modeling and simulation, disctibuted computing methodologies, computer graphics, etc.

This book helps readers understand the mathematics of machine learning, and apply them in different situations.

Author: Abhijit Ghatak

Publisher: Springer

ISBN: 9789811068089

Category: Computers

Page: 210

View: 732

This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it’s applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.

With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.

Author: Giuseppe Bonaccorso

Publisher: Packt Publishing Ltd

ISBN: 9781789349276

Category: Computers

Page: 386

View: 148

Discover the skill-sets required to implement various approaches to Machine Learning with Python Key Features Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more Build your own neural network models using modern Python libraries Practical examples show you how to implement different machine learning and deep learning techniques Book Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learn Use cluster algorithms to identify and optimize natural groups of data Explore advanced non-linear and hierarchical clustering in action Soft label assignments for fuzzy c-means and Gaussian mixture models Detect anomalies through density estimation Perform principal component analysis using neural network models Create unsupervised models using GANs Who this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.

Learn unsupervised learning in Python with hands-on practical applications for each learning model.

Author: Mahsa Lotfi

Publisher:

ISBN: OCLC:1137339004

Category:

Page:

View: 925

Learn unsupervised learning in Python with hands-on practical applications for each learning model. About This Video Acquire an in-depth understanding of unsupervised learning models and their various types Learn how to implement different unsupervised learning algorithms using Python and the TensorFlow library Build unsupervised learning models for more complex problems based on the simple examples in this tutorial In Detail Nowadays, machine learning is becoming increasingly important to businesses. It is used to solve various business problems using supervised and unsupervised algorithms. In unsupervised learning, Artificial Intelligence systems try to categorize unlabeled and unsorted data based on the similarities and differences that exist among data. In this case, the capabilities of unsupervised learning methods to generate a model based on data make it possible to deal with complex and more difficult problems in comparison with the capabilities of supervised learning. In this course, we examine different unsupervised learning methods and solve practical problems using the TensorFlow platform. Solving examples of real-world problems using TensorFlow is more inspiring and compelling and will enhance your practical skills. By the end of this course, you will gain significant hands-on experience using unsupervised learning algorithms with TensorFlow and will be able to make your own model to solve relevant real-world learning problems.