machine learning with python cookbook chris albon kyle gallatin

This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work.

Objev podobné jako Machine Learning with Python Cookbook - Chris Albon, Kyle Gallatin

This book is a comprehensive guide suitable for beginners and experienced developers alike. It teaches readers how to master object-oriented programming (OOP) with Python and use it in real-world applications.Start by solidifying your OOP foundation with clear explanations of core concepts like use cases and class diagrams. This book goes beyond theory as you get practical examples with well-documented source code available in the book and on GitHub.This book doesn''t stop at the basics. Explore how OOP empowers fields like data persistence, graphical user interfaces (GUIs), machine learning, and data science, including social media analysis. Learn about machine learning algorithms for classification, regression, and unsupervised learning, putting you at the forefront of AI innovation.Each chapter is designed for hands-on learning. You''ll solidify your understanding with case studies, exercises, and projects that apply your newfound knowledge to real-world scenarios. The progressive structure ensures mastery, with each chapter building on the previous one, reinforced by exercises and projects.Numerous code examples and access to the source code enhance your learning experience. This book is your one-stop shop for mastering OOP with Python and venturing into the exciting world of machine learning and data science.

Objev podobné jako The Object-Oriented Approach to Problem Solving and Machine Learning with Python - Maha Hadid, Sujith Samuel Mathew, Shahbano Farooq, Mohammad Amin K

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions.

Objev podobné jako Introduction to Machine Learning with Python - Andreas C. Mueller

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This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

Objev podobné jako Machine Learning with Neural Networks - Bernhard Mehlig

This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.

Objev podobné jako Introduction to Machine Learning with Applications in Information Security - Mark Stamp

This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.

Objev podobné jako Graph-Powered Analytics and Machine Learning with TigerGraph - Ph.D., Victor Lee, Xinyu Chang, Phuc Kien Nguyen

This best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

Objev podobné jako Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e - Géron Aurélien

Discover practical solutions for a wide range of real-world network programming tasksAbout This Book• Solve real-world tasks in the area of network programming, system/networking administration, network monitoring, and more. • Familiarize yourself with the fundamentals and functionalities of SDN• Improve your skills to become the next-gen network engineer by learning the various facets of Python programmingWho This Book Is ForThis book is for network engineers, system/network administrators, network programmers, and even web application developers who want to solve everyday network-related problems. If you are a novice, you will develop an understanding of the concepts as you progress with this book. What You Will Learn• Develop TCP/IP networking client/server applications• Administer local machines' IPv4/IPv6 network interfaces• Write multi-purpose efficient web clients for HTTP and HTTPS protocols• Perform remote system administration tasks over Telnet and SSH connections• Interact with popular websites via web services such as XML-RPC, SOAP, and REST APIs• Monitor and analyze major common network security vulnerabilities• Develop Software-Defined Networks with Ryu, OpenDaylight, Floodlight, ONOS, and POX Controllers• Emulate simple and complex networks with Mininet and its extensions for network and systems emulations• Learn to configure and build network systems and Virtual Network Functions (VNF) in heterogeneous deployment environments• Explore ... Unknown localization key: "more"

Objev podobné jako Python Network Programming Cookbook - - Dr. M. O. Faruque Sarker, Pradeeban Kathiravelu

It''s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily available machine learning tools! In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you’re grokking as you go. You’ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Key Features · Different types of machine learning, including supervised and unsupervised learning · Algorithms for simplifying, classifying, and splitting data · Machine learning packages and tools · Hands-on exercises with fully-explained Python code samples For readers with intermediate programming knowledge in Python or a similar language. About the technology Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis ... Unknown localization key: "more"

Objev podobné jako Grokking Machine Learning - Luis Serrano

This series supports learners through the Cambridge IGCSE™ and O Level Computer Science syllabuses (0478/0984/2210). Develop skills and confidence with our programming book for Python. Created to support students undertaking the Cambridge IGCSE™ and O Level Computer Science syllabuses, this resource provides tailored support when programming with Python. A three-tiered approach to programming tasks across the book provides scaffolded support for students of all levels of understanding. Answers are accessible in the ''Solutions'' chapter in the digital part of the resource on the Cambridge GO platform, enabling students to practise their programming skills in class or at home.

Objev podobné jako Cambridge IGCSEâ„¢ and O Level Computer Science Programming Book for Python with Digital Access (2 Years) - Chris Roffey

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can't doLearn the basics of machine learning code and how it integrates with large datasetsUnderstand the mathematical principles that AI uses to make itself smarterConsider real-world applications of machine learning and write your own algorithms With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

Objev podobné jako Machine Learning For Dummies - John Paul Mueller, Luca Massaron

Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Business Analytics

Stable Diffusion, ChatGPT, Whisper - these are just a few examples of incredible applications powered by developments in machine learning. Despite the ubiquity of machine learning applications running in production, there are only a few viable language choices for data science and machine learning tasks. Elixir''s Nx project seeks to change that. With Nx, you can leverage the power of machine learning in your applications, using the battle-tested Erlang VM in a pragmatic language like Elixir. In this book, you''ll learn how to leverage Elixir and the Nx ecosystem to solve real-world problems in computer vision, natural language processing, and more.The Elixir Nx project aims to make machine learning possible without the need to leave Elixir for solutions in other languages. And even if concepts like linear models and logistic regression are new to you, you''ll be using them and much more to solve real-world problems in no time.Start with the basics of the Nx programming paradigm - how it differs from the Elixir programming style you''re used to and how it enables you to write machine learning algorithms. Use your understanding of this paradigm to implement foundational machine learning algorithms from scratch. Go deeper and discover the power of ... Unknown localization key: "more"

Objev podobné jako Machine Learning in Elixir - Sean Moriarity

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using RAn expanded chapter focused on discussion of deep learning techniquesA new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Business Analytics - Peter Gedeck, Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Inbal Yahav

Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations.

Objev podobné jako Machine Learning for Managers - Paul Geertsema

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 applications. However, because of the nature of "black-box" in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications.This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making.This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially ... Unknown localization key: "more"

Objev podobné jako Human and Machine Learning

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This fourth edition of Machine Learning for Business Analytics also includes: An expanded chapter on deep learningA new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learningA new chapter on responsible data scienceUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their studentsA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniquesEnd-of-chapter ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Business Analytics - Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Kuber R. Deokar

Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimisation for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimisation using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML ... Unknown localization key: "more"

Objev podobné jako Machine Learning Algorithms in Depth - Vadim Smolyakov

Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you''ll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.

Objev podobné jako Ensemble Methods for Machine Learning - Gautam Kunapuli

A practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models.Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author’s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems. •   Integrates coverage of cutting-edge research, existing tools, and real-world applications•   Provides students and professionals with an engineering view for production-ready machine learning systems•   Proven in the classroom•   Offers supplemental resources including slides, videos, ... Unknown localization key: "more"

Objev podobné jako Machine Learning in Production - Christian Kastner

The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field.It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application domains. It demonstrates the fundamental techniques involved in data collection, integration, cleansing, transformation, development, and deployment of machine learning models. This book emphasizes the importance of integrating responsible and explainable AI into machine learning models, ensuring these principles are prioritized rather than treated as an afterthought. To support learning, this book also offers information on accessing additional machine learning resources such as datasets, libraries, pre-trained models, and tools for tracking machine learning models.This is a core resource for students and instructors of machine learning and data science looking for beginner-friendly material which offers real-world applications and takes ethical discussions into account.

Objev podobné jako Practical Machine Learning - Ally S. Nyamawe, Salim A. Diwani, Noe E. Nnko, Mohamedi M. Mjahidi, Kulwa Malyango, Godbless G. Minja

An Absolute Beginner''s Guide to Learning Data Analysis Using Python, a Demanding Skill for TodayKey FeaturesHands-on learning experience of Python''s fundamentals.Covers various examples of how to code end-to-end data analysis with easy illustrations.An excellent starting point to begin your data analysis journey with Python programming.DescriptionIn an effort to provide content for beginners, the book Data Analysis with Python provides a concrete first step in learning data analysis. Written by a data professional with decades of experience, this book provides a solid foundation in data analysis and numerous data science processes. In doing so, readers become familiar with common Python libraries and straightforward scripting techniques.Python and many of its well-known data analysis libraries, such as Pandas, NumPy, and Matplotlib, are utilized throughout this book to carry out various operations typical of data analysis projects.Following an introduction to Python programming fundamentals, the book combines well-known numerical calculation and statistical libraries to demonstrate the fundamentals of programming, accompanied by many practical examples. This book provides a solid groundwork for data analysis by teaching Python programming as well as Python''s built-in data analysis capabilities.What you will learnLearn the fundamentals of core Python programming for data analysis.Master Python''s most demanding data analysis and visualization libraries, ... Unknown localization key: "more"

Objev podobné jako Data Analysis with Python - Rituraj Dixit

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompanimentÂ

Objev podobné jako Probabilistic Machine Learning - Kevin P. Murphy

Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software.The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued. With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical and computational sciences for years to come.  Â

Objev podobné jako Multivariate Statistics and Machine Learning - Daniel J. Denis

For courses in Python programming. A clear and student-friendly introduction to Python Starting Out with Python® introduces programming concepts and problem-solving skills using Tony Gaddis'' accessible approach. Control structures are discussed before classes to familiarize new programmers with the fundamentals. Every chapter includes easy-to-read code listings, practical examples, and application exercises to help students gain confidence in their skills and learn to recognize the logic of developing high-quality programs. The 6th Edition is thoroughly updated with new language features and functionality for versions of Python up through Python 3.9. Hallmark features of this title Student-friendly presentation Written for novice programmers, Gaddis uses easy-to-understand language to introduce concepts. Control structures are explained, then classes and GUI applications. Optional turtle graphics sections are extremely effective at teaching the procedural steps of programming to beginners. Student-focused features Hundreds of example programs explore specific topics or more involved problem solving. Students can run the programs themselves using the source code. In the Spotlight case studies analyze how to solve a programming problem in step-by-step detail. Checkpoints and review questions let students test their understanding at regular intervals. Programming exercises offer hands-on practice opportunities at the end of each chapter. New and updated features of ... Unknown localization key: "more"

Objev podobné jako Starting Out with Python, Global Edition - Tony Gaddis

Create simple, easy programs in the popular Python language Beginning Programming with Python For Dummies is the trusted way to learn the foundations of programming using the Python programming language. Python is one of the top-ranked languages, and there’s no better way to get started in computer programming than this friendly guide. You’ll learn the basics of coding and the process of creating simple, fun programs right away. This updated edition features new chapters, including coverage of Google Colab, plus expanded information on functions and objects, and new examples and graphics that are relevant to today’s beginning coders. Dummies helps you discover the wealth of things you can achieve with Python. Employ an online coding environment to avoid installation woes and code anywhere, any time Learn the basics of programming using the popular Python language Create easy, fun projects to show off your new coding chops Fix errors in your code and use Python with external data sets Beginning Programming with Python For Dummies will get new programmers started—the easy way.Â

Objev podobné jako Beginning Programming with Python For Dummies - John Paul Mueller

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book''s web site.

Objev podobné jako Mathematics for Machine Learning - A. Aldo Faisal, Marc Peter Deisenroth, Cheng Soon Ong

A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and ... Unknown localization key: "more"

Objev podobné jako Machine Learning - Stephen Marsland

Learn to code like a professional with Python – an open source, versatile, and powerful programming languageKey FeaturesLearn the fundamentals of programming with Python – one of the best languages ever createdDevelop a strong set of programming skills that you will be able to express in any situation, on every platform, thanks to Python’s portabilityCreate outstanding applications of all kind, from websites to scripting, and from GUIs to data scienceBook DescriptionLearning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to ... Unknown localization key: "more"

Objev podobné jako Learning Python - Fabrizio Romano

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.

Objev podobné jako System Design for Epidemics Using Machine Learning and Deep Learning

Wrangle stats as you learn how to graph, analyze, and interpret data with Python Statistical Analysis with Python For Dummies introduces you to the tool of choice for digging deep into data to inform business decisions. Even if you're new to coding, this book unlocks the magic of Python and shows you how to apply it to statistical analysis tasks. You'll learn to set up a coding environment and use Python's libraries and functions to mine data for correlations and test hypotheses. You'll also get a crash course in the concepts of probability, including graphing and explaining your results. Part coding book, part stats class, part business analyst guide, this book is ideal for anyone tasked with squeezing insight from data. Get clear explanations of the basics of statistics and data analysisLearn how to summarize and analyze data with Python, step by stepImprove business decisions with objective evidence and analysisExplore hypothesis testing, regression analysis, and prediction techniques This is the perfect introduction to Python for students, professionals, and the stat-curious.

Objev podobné jako Statistical Analysis with Python For Dummies - Joseph Schmuller

Doing Math with Python shows you how to use Python to delve into high school level math topics like statistics, geometry, probability, and calculus. You ll start with simple projects, like a factoring program and a quadratic-equation solver, and then create more complex projects once you ve gotten the hang of things. Along the way, you ll discover new ways to explore math and gain valuable programming skills that you ll use throughout your study of math and computer science. Learn how to: Describe your data with statistics, and visualize it with line graphs, bar charts, and scatter plots Explore set theory and probability with programs for coin flips, dicing, and other games of chance Solve algebra problems using Python s symbolic math functions Draw geometric shapes and explore fractals like the Barnsley fern, the Sierpinski triangle, and the Mandelbrot set Write programs to find derivatives and integrate functions Creative coding challenges and applied examples help you see how you can put your new math and coding skills into practice. You ll write an inequality solver, plot gravity s effect on how far a bullet will travel, shuffle a deck of cards, estimate the area of a circle by throwing ... Unknown localization key: "more"

Objev podobné jako Doing Math with Python - Amit Saha

"Simplified Machine Learning" is a comprehensive guide that navigates readers through the intricate landscape of Machine Learning, offering a balanced blend of theory, algorithms, and practical applications. The first section introduces foundational concepts such as supervised and unsupervised learning, regression, classification, clustering, and feature engineering, providing a solid base in Machine Learning theory. The second section explores algorithms like decision trees, support vector machines, and neural networks, explaining their functions, strengths, and limitations, with a special focus on deep learning, reinforcement learning, and ensemble methods. The book also covers essential topics like model evaluation, hyperparameter tuning, and model interpretability. The final section transitions from theory to practice, equipping readers with hands-on experience in deploying models, building scalable systems, and understanding ethical considerations.

Objev podobné jako Simplified Machine Learning - Pooja Sharma

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today''s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available ... Unknown localization key: "more"

Objev podobné jako Machine Learning - Kevin P. Murphy

Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithmsConduct research with ML algorithms on big dataUse supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Objev podobné jako Advances in Financial Machine Learning - Marcos Lopez de Prado

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

Objev podobné jako Machine Learning Refined - Aggelos K. Katsaggelos, Reza Borhani, Jeremy Watt

This textbook is an introductory guide to applied machine learning, specifically for biology students. It familiarizes biology students with the basics of modern computer science and mathematics and emphasizes the real-world applications of these subjects. The chapters give an overview of computer systems and programming languages to establish a basic understanding of the important concepts in computer systems. Readers are introduced to machine learning and artificial intelligence in the field of bioinformatics, connecting these applications to systems biology, biological data analysis and predictions, and healthcare diagnosis and treatment. This book offers a necessary foundation for more advanced computer-based technologies used in biology, employing case studies, real-world issues, and various examples to guide the reader from the basic prerequisites to machine learning and its applications.

Objev podobné jako A Guide to Applied Machine Learning for Biologists

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively.Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.

Objev podobné jako Data Science and Machine Learning for Non-Programmers - Dothang Truong

A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of machine learning concepts from basic principles and illustrates every concept using examples in both Python and MATLAB®, which are available on GitHub and can be run from there in Binder in a web browser. Each chapter concludes with exercises to explore the content.The emphasis of the book is on the question of Why—only if “why” an algorithm is successful is understood, can it be properly applied and the results trusted. Standard techniques are treated rigorously, including an introduction to the necessary probability theory. This book addresses the commonalities of methods, aims to give a thorough and in-depth treatment and develop intuition for the inner workings of algorithms, while remaining concise.This useful reference should be essential on the bookshelf of anyone employing machine learning techniques, since it is born out of strong experience in university teaching and research on algorithms, while remaining approachable and readable.

Objev podobné jako A Concise Introduction to Machine Learning - A.C. Faul

As one of the most comprehensive machine learning texts around, this book does justice to the field''s incredible richness, but without losing sight of the unifying principles. Peter Flach''s clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

Objev podobné jako Machine Learning - Peter Flach

A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the ... Unknown localization key: "more"

Objev podobné jako Applied Machine Learning for Data Science Practitioners - Vidya Subramanian

Understand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python.What You''ll LearnUse Python and numerical computation to demonstrate the power of simulationChoose a paradigm to run a simulationDraw statistical insights from numerical experimentsKnow how simulation is used to solve real-world problems Who This Book Is ForEntry-level to mid-level Python developers from various backgrounds, including backend developers, academic research programmers, data scientists, and machine learning engineers. The book is also useful to high school students and college undergraduates and graduates with STEM backgrounds.

Objev podobné jako Simulation with Python - Aiichiro Nakano, Rongpeng Li

This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant ... Unknown localization key: "more"

Objev podobné jako Machine Learning for Text - Charu C. Aggarwal

AN INSPIRATIONAL ANGELS BOOK FROM INTERNATIONAL SPEAKER AND BEST-SELLING AUTHOR OF RAISE YOUR VIBRATION, KYLE GRAY DISCOVER HOW TO BRING THE HEALING ANGELIC MESSAGES FROM HEAVEN INTO ALL AREAS OF YOUR LIFE FOR SPIRITUAL GROWTH AND POWERFUL TRANSFORMATION“The hottest name in spirituality!” – Soul & Spirit magazine Kyle Gray is one of the UK’s most sought-after angel experts. He discovered his spiritual gift at the tender age of four and now dedicates his life to helping others tune in to their own intuitive talents. In this angels book, Kyle teaches readers how to connect with the angels and bring their divine, loving presence into all areas of life for powerful transformations. You will learn about:· How to connect to your own guardian angel· How to see, hear, and feel the presence of the angels· Powerful methods to communicate with angels· How angels can help with anything· Choirs of angels· Guardian angels Sections Include: PART I: WHAT ARE ANGELS?· Angels are real· The purpose of angels· How angels can help· Choirs of angels· Guardian angelsPART II: WORKING WITH ANGELS· Meeting your guardian angel· The power of archangels· Healing angels· Seeing angels· Hearing angels· Feeling angels· Building the bridge· Angelic toolbox“Angels are ... Unknown localization key: "more"

Objev podobné jako Connecting with the Angels Made Easy - Kyle Gray

If you''ve ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you? In this fully revised edition of the best-selling classic Automate the Boring Stuff with Python, you''ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand - no prior programming experience required. You''ll learn the basics of coding in Python and explore the language''s rich library of modules for performing specific tasks, like scraping data from websites, searching for text across multiple files, and merging, watermarking, or encrypting PDFs. The third edition includes: Expanded coverage of developer techniques, like creating command line programs; Updated examples and new projects; Additional chapters about working with SQLite databases, speech-recognition technology, video and audio editing, and text-to-speech capabilities; Simplified explanations (based on reader feedback) of beginner programming concepts, like loops and conditionals. Even if you''ve never written a line of code, Automate the Boring Stuff with Python, 3rd Edition will teach you how to make your computer take on tedious tasks and do all your grunt work - the ... Unknown localization key: "more"

Objev podobné jako Automate the Boring Stuff with Python, 3rd Edition - Al Sweigart

2020 finalist for the prestigious Next Generation Indie Book Awards!65 Deliciously Authentic Recipes Straight from Mama’s Kitchen, Now in Paperback!My Big Fat Greek Cookbook is a comprehensive, contemporary overview of Greek food, recipes, and family culture as documented by the son of a Greek immigrant as his mother neared the end of her life. “This Greek eating tragedy has a beginning (appetizer), a middle (main course), and an end (dessert),” Christos shared. “As my Mama is in her final act, it’s fitting that a quarter of her recipes are desserts. Bon appétit! Kali Orexi! (Insert the sound of breaking plates here . . .)”This is more than just a list of ingredients or series of steps, of course. It’s filled with simple recipes, gorgeous photographs, traditional meals, memories, and tidbits of information that draw family and friends to Greek tables time and again.It has everything from iconic egg-lemon sauce to rich soups, sweet pies, and traditional delicacies like rabbit stew and octopus with pasta, accompanied by tales of Greek history and insight into cultural nuances. Recipes include:Meatballs (keftedes)Lentils (fatkes)Stuffed vegetables (gemistra)Spinach pie (spanakopita)TzatzikiSpaghetti with cheese (makaronia me tyri)Roast lamb (arni sto fourno)MoussakaApple cake (milopita)Rice pudding (rizogalo)And more!With stunning photographs throughout ... Unknown localization key: "more"

Objev podobné jako My Big Fat Greek Cookbook - Christos Sourligas, Evdokia Antginas

A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learningExpansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learningPractical AI and Python “cheat sheet” quick referencesThis hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.

Objev podobné jako Artificial Intelligence Programming with Python - Perry Xiao

Coding with Python – Create Amazing Graphics introduces coding in Python through a variety of projects. Each one teaches new coding concepts and results in some amazing graphics.Python is a powerful, text-based programming language essential to grasp for serious coding but can be dull to learn. This book focuses on inspired learning. Step-by-step, it illustrates how to use Python code to create exciting and colourful graphics — making learning Python great fun!Learn Python code to:Use random numbers to create unique artworkMix colours together using variables to create amazing effectsUse loops to repeat your code and create intricate patternsCode your own functions and build up your own designs

Objev podobné jako Coding with Python - Create Amazing Graphics - Max Wainewright

Al Sweigart''s classic coding book Automate the Boring Stuff with Python, now in its third edition, has taught more than half a million readers how to dispense with tedious tasks using the Python programming language. In this hands-on companion workbook, Sweigart gives those readers - and any Python programming beginner - hundreds of new ways to practice what they''ve learned. Through a wide variety of exercises, readers will test their understanding of programming concepts, face down tricky challenges, and explore use cases of common techniques. The workbook''s creative projects encourage readers to build games, animations, and digital tools, offering novel ways to think about Python''s applications in their everyday life. Covers Python 3.x and its ecosystem of third-party libraries.

Objev podobné jako Automate the Boring Stuff with Python Workbook - Al Sweigart

Prepares students for Microsoft Exam 70-774-and helps them demonstrate real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT students ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level.

Objev podobné jako Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning - Ginger Grant, Tamanaco Francisquez, Pau Sempere, Paco Gonzalez, Julio Granado