Mastering Net Machine Learning

Author : Jamie Dixon
Publisher : Packt Publishing Ltd
ISBN 13 : 1785881191
Total Pages : 358 pages
Book Rating : 4.90/5 ( download)

DOWNLOAD NOW!

Download or read book Mastering .NET Machine Learning written by Jamie Dixon and published by Packt Publishing Ltd. This book was released on 2016-03-29 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the art of machine learning with .NET and gain insight into real-world applications About This Book Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0 Set up your business application to start using machine learning techniques Familiarize the user with some of the more common .NET libraries for machine learning Implement several common machine learning techniques Evaluate, optimize and adjust machine learning models Who This Book Is For This book is targeted at .Net developers who want to build complex machine learning systems. Some basic understanding of data science is required. What You Will Learn Write your own machine learning applications and experiments using the latest .NET framework, including .NET Core 1.0 Set up your business application to start using machine learning. Accurately predict the future using regressions. Discover hidden patterns using decision trees. Acquire, prepare, and combine datasets to drive insights. Optimize business throughput using Bayes Classifier. Discover (more) hidden patterns using KNN and Naive Bayes. Discover (even more) hidden patterns using K-Means and PCA. Use Neural Networks to improve business decision making while using the latest ASP.NET technologies. Explore “Big Data”, distributed computing, and how to deploy machine learning models to IoT devices – making machines self-learning and adapting Along the way, learn about Open Data, Bing maps, and MBrace In Detail .Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in .NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions. You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results. Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the fly Style and approach This book will guide you in learning everything about how to tackle the flood of data being encountered these days in your .NET applications with the help of popular machine learning libraries offered by the .NET framework.

Author : Saket S. R. Mengle
Publisher : Packt Publishing
ISBN 13 : 9781789349795
Total Pages : 306 pages
Book Rating : 4.96/5 ( download)

DOWNLOAD NOW!

Download or read book Mastering Machine Learning on AWS written by Saket S. R. Mengle and published by Packt Publishing. This book was released on 2019-05-17 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Ian Griffiths
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 9780596003609
Total Pages : 420 pages
Book Rating : 4.09/5 ( download)

DOWNLOAD NOW!

Download or read book Mastering Visual Studio .NET written by Ian Griffiths and published by "O'Reilly Media, Inc.". This book was released on 2003 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book enables intermediate and advanced programmers the kind of depth that's really needed, such as advanced window functionality, macros, advanced debugging, and add-ins, etc. With this book, developers will learn the VS.NET development environment from top to bottom.

Author : Gavin Hackeling
Publisher : Packt Publishing Ltd
ISBN 13 : 1788298497
Total Pages : 254 pages
Book Rating : 4.90/5 ( download)

DOWNLOAD NOW!

Download or read book Mastering Machine Learning with scikit-learn written by Gavin Hackeling and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks In Detail Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn's API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model's performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.

Author : Evangelos Petroutsos
Publisher : John Wiley & Sons
ISBN 13 : 0782152341
Total Pages : 1112 pages
Book Rating : 4.40/5 ( download)

DOWNLOAD NOW!

Download or read book Mastering Visual Basic .NET written by Evangelos Petroutsos and published by John Wiley & Sons. This book was released on 2006-02-20 with total page 1112 pages. Available in PDF, EPUB and Kindle. Book excerpt: VB Programmers: Get in Step with .NET With the introduction of Visual Basic .NET, VB transcends its traditional second-class status to become a full-fledged citizen of the object-oriented programming, letting you access the full power of the Windows platform for the first time. Written bythe author of the best-selling Mastering Visual Basic 6 this all-new edition is the resource you need to make a successful transition to .NET. Comprising in-depth explanations, practical examples, and handy reference information, its coverage includes: Mastering the new Windows Forms Designer and controls Building dynamic forms Using powerful Framework classes such as ArrayLists and HashTables Persisting objects to disk files Handling graphics and printing Achieving robustness via structured exception handling and debugging Developing your own classes and extending existing ones via inheritance Building custom Windows controls Building menus and list controls with custom-drawn items Using ADO.NET to build disconnected, distributed applications Using SQL queries and stored procedures with ADO.NET Facilitating database programming with the visual database tools Building web applications with ASP.NET and the rich web controls Designing web applications to access databases Using the DataGrid and DataList web controls Building XML web services to use with Windows and web applications Special topics like the Multiple Document Interface and powerful recursive programming techniques Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

Author : Chiheb Chebbi
Publisher : Packt Publishing Ltd
ISBN 13 : 178899311X
Total Pages : 264 pages
Book Rating : 4.11/5 ( download)

DOWNLOAD NOW!

Download or read book Mastering Machine Learning for Penetration Testing written by Chiheb Chebbi and published by Packt Publishing Ltd. This book was released on 2018-06-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.

Author : Rajdeep
Publisher : BPB Publications
ISBN 13 : 9391392229
Total Pages : 353 pages
Book Rating : 4.22/5 ( download)

DOWNLOAD NOW!

Download or read book Mastering TensorFlow 2.x written by Rajdeep and published by BPB Publications. This book was released on 2022-03-24 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work with TensorFlow and Keras for real performance of deep learning KEY FEATURES ● Combines theory and implementation with in-detail use-cases. ● Coverage on both, TensorFlow 1.x and 2.x with elaborated concepts. ● Exposure to Distributed Training, GANs and Reinforcement Learning. DESCRIPTION Mastering TensorFlow 2.x is a must to read and practice if you are interested in building various kinds of neural networks with high level TensorFlow and Keras APIs. The book begins with the basics of TensorFlow and neural network concepts, and goes into specific topics like image classification, object detection, time series forecasting and Generative Adversarial Networks. While we are practicing TensorFlow 2.6 in this book, the version of Tensorflow will change with time; however you can still use this book to witness how Tensorflow outperforms. This book includes the use of a local Jupyter notebook and the use of Google Colab in various use cases including GAN and Image classification tasks. While you explore the performance of TensorFlow, the book also covers various concepts and in-detail explanations around reinforcement learning, model optimization and time series models. WHAT YOU WILL LEARN ● Getting started with Tensorflow 2.x and basic building blocks. ● Get well versed in functional programming with TensorFlow. ● Practice Time Series analysis along with strong understanding of concepts. ● Get introduced to use of TensorFlow in Reinforcement learning and Generative Adversarial Networks. ● Train distributed models and how to optimize them. WHO THIS BOOK IS FOR This book is designed for machine learning engineers, NLP engineers and deep learning practitioners who want to utilize the performance of TensorFlow in their ML and AI projects. Readers are expected to have some familiarity with Tensorflow and the basics of machine learning would be helpful. TABLE OF CONTENTS 1. Getting started with TensorFlow 2.x 2. Machine Learning with TensorFlow 2.x 3. Keras based APIs 4. Convolutional Neural Networks in Tensorflow 5. Text Processing with TensorFlow 2.x 6. Time Series Forecasting with TensorFlow 2.x 7. Distributed Training and DataInput pipelines 8. Reinforcement Learning 9. Model Optimization 10. Generative Adversarial Networks

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1788625900
Total Pages : 567 pages
Book Rating : 4.06/5 ( download)

DOWNLOAD NOW!

Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-05-25 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Christoph Körner
Publisher : Packt Publishing Ltd
ISBN 13 : 1789801524
Total Pages : 437 pages
Book Rating : 4.21/5 ( download)

DOWNLOAD NOW!

Download or read book Mastering Azure Machine Learning written by Christoph Körner and published by Packt Publishing Ltd. This book was released on 2020-04-30 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes Key FeaturesMake sense of data on the cloud by implementing advanced analyticsTrain and optimize advanced deep learning models efficiently on Spark using Azure DatabricksDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)Book Description The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure. What you will learnSetup your Azure Machine Learning workspace for data experimentation and visualizationPerform ETL, data preparation, and feature extraction using Azure best practicesImplement advanced feature extraction using NLP and word embeddingsTrain gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine LearningUse hyperparameter tuning and Azure Automated Machine Learning to optimize your ML modelsEmploy distributed ML on GPU clusters using Horovod in Azure Machine LearningDeploy, operate and manage your ML models at scaleAutomated your end-to-end ML process as CI/CD pipelines for MLOpsWho this book is for This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.

Author : Jason Bell
Publisher : John Wiley & Sons
ISBN 13 : 1119642140
Total Pages : 432 pages
Book Rating : 4.45/5 ( download)

DOWNLOAD NOW!

Download or read book Machine Learning written by Jason Bell and published by John Wiley & Sons. This book was released on 2020-03-10 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.