Python machine learning

This book covers machine learning, one of the hottest topics in more recent years. With computing power increasing exponentially and costs decreasing at the same time, there is no better time for mach

Are you stuck in getting started with machine learning with python? A Step-By-Step Guide to Learn and Master Python Machine Learning walks you through steps for getting started with Machine Learning w

本書以案例驅動的方式講解機器學習算法的知識點，並以Python語言作為基礎開發語言實現算法，包括目前機器學習主流算法的原理、算法流程圖、算法的詳細設計步驟、算法實例、算法應用、算法的改進與優化等環節。全書共分 17 章，前兩章介紹機器學習與 Python 語言的相關基礎知識，後面各章以案例的方式分別介紹線性回歸算法、邏輯回歸算法、K *近鄰算法、PCA 降維算法、k-means算法、支持向量機算法

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 ecosyste

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves

The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this c

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

Create AI applications in Python and lay the foundations for your career in data scienceKey FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail wit

Business analysts, managers, researchers, and students are rushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine Learni

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabiliti

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It il

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investmentBig Data and Machine Learning in Quantitative Investment is not just about demonstrat

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-wor

This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpin

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook f

This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different ap

An introduction to machine learning that includes the fundamental techniques, methods, and applications Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concept

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric conve

Machine learning can be a difficult subject if you’re not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the sta

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.Key FeaturesExplore machine learning techniques with prominent ope

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for ad

The first machine-learning guide that helps you understand customers, frame decisions, and drive value Business Data Science reveals the best ways for utilizing machine learning (ML) to mak

Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scala

This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonst

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use i

Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learnKey FeaturesDevelop a range of healthcare analytics projects using real-world datasetsImplement key machin

3 comprehensive manuscripts in 1 bookMachine Learning: An Essential Guide to Machine Learning for Beginners Who Want to Understand Applications, Artificial Intelligence, Data Mining, Big Data and More

Just about anyone with the slightest bit of interest in modern technology is looking to learn more about Machine Learning. This innovative new form of computer programming is the primary tool that mak

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine

Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework dra

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex p

This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer