Raghav Bali - Böcker

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered.Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment.Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!What You'll LearnExecute end-to-end machine learning projects and systemsImplement hands-on examples with industry standard, open source, robust machine learning tools and frameworksReview case studies depicting applications of machine learning and deep learning on diverse domains and industriesApply a wide range of machine learning models including regression, classification, and clustering.Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.Who This Book Is ForIT professionals, analysts, developers, data scientists, engineers, graduate students

Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.About This Book* Build your confidence with R and find out how to solve a huge range of data-related problems* Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today* Don't just learn - apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysisWho This Book Is ForAimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data scienceWhat You Will Learn* Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results* Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action* Solve interesting real-world problems using machine learning and R as the journey unfolds* Write reusable code and build complete machine learning systems from the ground up* Learn specialized machine learning techniques for text mining, social network data, big data, and more* Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems* Evaluate and improve the performance of machine learning models* Learn specialized machine learning techniques for text mining, social network data, big data, and moreIn DetailR is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it...This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media.This Learning Path has been curated from three Packt products:* R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar* Machine Learning with R Learning - Second Edition By Brett Lantz* Mastering Machine Learning with R By Cory LesmeisterStyle and approachThis is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey FeaturesBuild deep learning models with transfer learning principles in Pythonimplement transfer learning to solve real-world research problemsPerform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.What you will learnSet up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL modelsExplore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functionsGet to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLPWho this book is forHands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.

This edition is heavily outdated and we have a new edition with PyTorch examples published!Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook DescriptionMachines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment.Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is forThis is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.