Biznes IT
Lider wystarczająco dobry. 12 lekcji autentycznego przywództwa na czasy niepewności
Piotr Prokopowicz, Sebastian Drzewiecki
Pewny (siebie) menedżer w niepewnych (czegokolwiek) czasach Oto przewodnik przeznaczony dla liderów i menedżerów, którzy szukają recept na to, jak budować autentyczne relacje w swojej organizacji w czasach nacechowanych niepewnością, płynnością i rezygnacją z bezpośrednich kontaktów na rzecz spotkań online. Lider wystarczająco dobry łączy perspektywę naukową i praktyczną, oferuje szereg narzędzi i rozwiązań gotowych do tego, by od razu wdrożyć je we własnej firmie. W książce znajdziesz relacje z placu boju, doświadczenia organizacji, które zostały zmuszone do działania w sytuacji kryzysu i sobie z nim poradziły - dzięki konkretnym zachowaniom, odpowiednim sposobom komunikacji i skierowanemu we właściwą stronę myśleniu liderów. Autorzy, psycholog i socjolog organizacji oraz lider i dyrektor zarządzający, odsłonią przed Tobą kluczowe paradoksy biznesowego przywództwa i wskażą, jak się od nich uwolnić, by stać się liderem skutecznym. Wystarczająco dobrym w złych czasach niepewności. A to bardzo dużo. Uwaga! Wskazówki zawarte w poradniku można zastosować zarówno w małym biznesie, jak i w start-upach i międzynarodowych korporacjach! Zajrzyj na stronę książki! Posłuchaj audiobooka:
Lifehacker. Jak żyć i pracować z głową. Kolejne wskazówki
Adam Pash, Gina Trapani
Doba może mieć więcej niż 24 godziny! Co za czasy! Zewsząd słychać, że komputery wspomagają nas we wszelkich dziedzinach życia, na każdym kroku czekają nas ułatwienia, a my mimo wszystko pracujemy coraz więcej. Więcej? Nie należy mylić ilości z wydajnością! Jak sobie z tym poradzić? Jak zapanować nad natłokiem zadań i obowiązków? I - co najważniejsze - jak wykonać więcej (oraz lepiej) zadań w tym samym czasie? Myślisz, że to niemożliwe??? Z tą książką poprawa wydajności jest w zasięgu Twoich rąk! Druga część bestsellera Lifehacker. Jak żyć i pracować z głową zawiera niezwykle przydatne informacje na temat automatyzacji często wykonywanych czynności, dostępu do swoich danych z dowolnego urządzenia i miejsca na świecie oraz wydajnego korzystania z możliwości wyszukiwarek internetowych. Znajdziesz tu również dobre rady na temat pracy z urządzeniami mobilnymi oraz zarządzania wieloma komputerami. Jeżeli chcesz zaoszczędzić konkretną ilość czasu w każdym tygodniu, ta książka jest właśnie dla Ciebie! Dzięki tej książce zoptymalizujesz swoje działania i dowiesz się, jak: zautomatyzować najczęściej wykonywane zadania uzyskać dostęp do swoich danych z dowolnego miejsca i urządzenia wykorzystać potencjał wyszukiwarek do granic możliwości wydajniej korzystać ze swojego telefonu zaoszczędzić cenny czas Sięgnij po sprawdzone triki i sztuczki z komputerem, które ułatwią Ci życie!
Andreas M. Antonopoulos, Olaoluwa Osuntokun, René Pickhardt
Potencjał blockchainu jest ogromny i zapewne wciąż nie znamy wszystkich jego możliwych zastosowań. Jest to dosyć nowa, dynamicznie rozwijająca się technologia, jednak już teraz dostrzegalne są pewne jej ograniczenia. Problemem jest na przykład skalowalność Bitcoina. Rozwiązaniem wydaje się Lightning Network (LN), protokół drugiej warstwy, który zapewnia niemal natychmiastowe transakcje w systemie Bitcoin. Umożliwia on również zwiększenie szybkości i prywatności zawieranych transakcji, pozwalając przy tym na zmniejszenie opłat. Ta książka jest przeznaczona dla osób, które chcą zrozumieć działanie Lightning Network i wykorzystać możliwości tej technologii we własnych aplikacjach. Z lektury tej pozycji skorzystają programiści, architekci systemowi i inżynierowie. Omówiono w niej podstawy funkcjonowania sieci LN i sposoby jej użycia w praktyce. Przedstawiono również zasady oprogramowywania węzłów Lightning, ich implementacji i konfiguracji, a także zagadnienia budowania kanałów płatności w systemie Bitcoin, obsługi kanałów i przekazywania płatności. Zaprezentowano też informacje dotyczące bezpieczeństwa i prywatności w sieci Lightning Network. Poszczególne zagadnienia zilustrowano praktycznymi przykładami kodu w językach Go, C++, Python oraz przy użyciu wiersza poleceń uniksowych systemów operacyjnych. W książce między innymi: technologia Lightning Network a skalowanie blockchainu standardy stosowane w Lightning Network warstwy zestawu protokołów Lightning Network portfele i węzły oraz ich obsługa kanały płatności Lightning, routing cebulowy i protokół plotkarski kanały płatności od nadawcy do odbiorcy w trybie off-chain Poznaj potencjał LN i zostań mistrzem technologii przyszłości!
Andreas M. Antonopoulos, Olaoluwa Osuntokun, René Pickhardt
Potencjał blockchainu jest ogromny i zapewne wciąż nie znamy wszystkich jego możliwych zastosowań. Jest to dosyć nowa, dynamicznie rozwijająca się technologia, jednak już teraz dostrzegalne są pewne jej ograniczenia. Problemem jest na przykład skalowalność Bitcoina. Rozwiązaniem wydaje się Lightning Network (LN), protokół drugiej warstwy, który zapewnia niemal natychmiastowe transakcje w systemie Bitcoin. Umożliwia on również zwiększenie szybkości i prywatności zawieranych transakcji, pozwalając przy tym na zmniejszenie opłat. Ta książka jest przeznaczona dla osób, które chcą zrozumieć działanie Lightning Network i wykorzystać możliwości tej technologii we własnych aplikacjach. Z lektury tej pozycji skorzystają programiści, architekci systemowi i inżynierowie. Omówiono w niej podstawy funkcjonowania sieci LN i sposoby jej użycia w praktyce. Przedstawiono również zasady oprogramowywania węzłów Lightning, ich implementacji i konfiguracji, a także zagadnienia budowania kanałów płatności w systemie Bitcoin, obsługi kanałów i przekazywania płatności. Zaprezentowano też informacje dotyczące bezpieczeństwa i prywatności w sieci Lightning Network. Poszczególne zagadnienia zilustrowano praktycznymi przykładami kodu w językach Go, C++, Python oraz przy użyciu wiersza poleceń uniksowych systemów operacyjnych. W książce między innymi: technologia Lightning Network a skalowanie blockchainu standardy stosowane w Lightning Network warstwy zestawu protokołów Lightning Network portfele i węzły oraz ich obsługa kanały płatności Lightning, routing cebulowy i protokół plotkarski kanały płatności od nadawcy do odbiorcy w trybie off-chain Poznaj potencjał LN i zostań mistrzem technologii przyszłości!
Andreas M. Antonopoulos, Olaoluwa Osuntokun, René Pickhardt
Potencjał blockchainu jest ogromny i zapewne wciąż nie znamy wszystkich jego możliwych zastosowań. Jest to dosyć nowa, dynamicznie rozwijająca się technologia, jednak już teraz dostrzegalne są pewne jej ograniczenia. Problemem jest na przykład skalowalność Bitcoina. Rozwiązaniem wydaje się Lightning Network (LN), protokół drugiej warstwy, który zapewnia niemal natychmiastowe transakcje w systemie Bitcoin. Umożliwia on również zwiększenie szybkości i prywatności zawieranych transakcji, pozwalając przy tym na zmniejszenie opłat. Ta książka jest przeznaczona dla osób, które chcą zrozumieć działanie Lightning Network i wykorzystać możliwości tej technologii we własnych aplikacjach. Z lektury tej pozycji skorzystają programiści, architekci systemowi i inżynierowie. Omówiono w niej podstawy funkcjonowania sieci LN i sposoby jej użycia w praktyce. Przedstawiono również zasady oprogramowywania węzłów Lightning, ich implementacji i konfiguracji, a także zagadnienia budowania kanałów płatności w systemie Bitcoin, obsługi kanałów i przekazywania płatności. Zaprezentowano też informacje dotyczące bezpieczeństwa i prywatności w sieci Lightning Network. Poszczególne zagadnienia zilustrowano praktycznymi przykładami kodu w językach Go, C++, Python oraz przy użyciu wiersza poleceń uniksowych systemów operacyjnych. W książce między innymi: technologia Lightning Network a skalowanie blockchainu standardy stosowane w Lightning Network warstwy zestawu protokołów Lightning Network portfele i węzły oraz ich obsługa kanały płatności Lightning, routing cebulowy i protokół plotkarski kanały płatności od nadawcy do odbiorcy w trybie off-chain Poznaj potencjał LN i zostań mistrzem technologii przyszłości!
Logo Design Love. Tworzenie genialnych logotypów. Nowa odsłona
David Airey
Twórz logotypy, które rzucają na kolana! Jak czerpać od najlepszych i zachować oryginalność? Gdzie szukać twórczych inspiracji? Jak negocjować z klientami? Logo to jeden z kluczowych elementów tożsamości wizualnej każdej marki. Może pomóc jej osiągnąć szczyt popularności albo zepchnąć ją w otchłań niebytu. Wszystko zależy od Ciebie. Dlatego zanim otworzysz jakikolwiek program graficzny lub weźmiesz do ręki ołówek, zobacz, jak robią to najlepsi, i zaczerpnij wiedzę całymi garściami. Powraca kultowa książka, którą pokochali wszyscy kreatywni projektanci! W wydaniu drugim, zaktualizowanym i rozszerzonym, autor otwiera przed Tobą fascynujący świat wizualnej identyfikacji marki. Pozwól mu przeprowadzić się bezpiecznie przez wszystkie etapy projektowania. Znajdziesz tu jeszcze więcej analiz przypadków, szkiców, logotypów, wskazówek dotyczących współpracy z klientami, anegdot oraz praktycznych informacji o tym, jak skutecznie zamknąć projekt - i zrobić to dobrze. Ta książka da także solidnego kopa Twojemu zmysłowi twórczemu i pomoże uniknąć zabójczej rutyny. Proces tworzenia logo - od ołówka do PDF-a. Zasady wyceniania projektów. Sztuka prowadzenia rozmów z klientami. Rozwiązywanie problemów z brakiem oryginalności. Analiza kultowych projektów. Unikanie zagrożeń związanych z rebrandingiem. Pomiary zwrotu z inwestycji w projekt. Tworzenie trwałych wizerunków marki w 31 praktycznych wskazówkach. Zrób logo, które stanie się kultowe! David Airey - niezależny projektant graficzny z Irlandii Północnej. Swoje umiejętności szlifował w Wielkiej Brytanii i Stanach Zjednoczonych, po czym podjął decyzję o wyspecjalizowaniu się w projektowaniu wizerunku marki. Poświęcone projektowaniu graficznemu blogi Davida - davidairey.com, logodesignlove.com oraz identitydesigned.com - każdego miesiąca odwiedza ponad 600 tysięcy internautów. Lista klientów Davida obejmuje wiele znanych firm, takich jak Yellow Pages, Asian Development Bank, blinkbox czy BBC.
Giuseppe Bonaccorso
Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight.This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Giuseppe Bonaccorso
Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight.This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Gregory Keys, David Whiting
H2O is an open source, fast, and scalable machine learning framework that allows you to build models using big data and then easily productionalize them in diverse enterprise environments.Machine Learning at Scale with H2O begins with an overview of the challenges faced in building machine learning models on large enterprise systems, and then addresses how H2O helps you to overcome them. You’ll start by exploring H2O’s in-memory distributed architecture and find out how it enables you to build highly accurate and explainable models on massive datasets using your favorite ML algorithms, language, and IDE. You’ll also get to grips with the seamless integration of H2O model building and deployment with Spark using H2O Sparkling Water. You’ll then learn how to easily deploy models with H2O MOJO. Next, the book shows you how H2O Enterprise Steam handles admin configurations and user management, and then helps you to identify different stakeholder perspectives that a data scientist must understand in order to succeed in an enterprise setting. Finally, you’ll be introduced to the H2O AI Cloud platform and explore the entire machine learning life cycle using multiple advanced AI capabilities.By the end of this book, you’ll be able to build and deploy advanced, state-of-the-art machine learning models for your business needs.
Dario Radečić
The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods.With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets.By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level.
Andrew P. McMahon, Adi Polak
The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field.The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized model factory for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift.Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
Natu Lauchande
MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments.This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you’ll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins.By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.
Andrew McMahon
Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems.By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.
Stefan Jansen
The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.
Emmanuel Tsukerman
Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers.You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach.
Jesus Salcedo
Machine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with several ways to look at data. This book will help you improve your data mining techniques by using smart modeling techniques.This book will teach you how to implement ML algorithms and techniques in your data mining work. It will enable you to pair the best algorithms with the right tools and processes. You will learn how to identify patterns and make predictions with minimal human intervention. You will build different types of ML models, such as the neural network, the Support Vector Machines (SVMs), and the Decision tree. You will see how all of these models works and what kind of data in the dataset they are suited for. You will learn how to combine the results of different models in order to improve accuracy. Topics such as removing noise and handling errors will give you an added edge in model building and optimization.By the end of this book, you will be able to build predictive models and extract information of interest from the dataset
Allan Ramsay, Tariq Ahmad
Artificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially. With this book, you’ll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you’ll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions.The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you’re set up for success, you’ll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you’ll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion. By the end of this book, you’ll be well-equipped to use emotion mining and analysis to drive business decisions.
Machine Learning for Finance. Principles and practice for financial insiders
Jannes Klaas
Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways.The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Eduonix Learning Solutions
Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics.This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks.By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain.
Eduonix Learning Solutions
Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics.This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks.By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain.
Kumar Abhishek, Dr. Mounir Abdelaziz
As machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance.Machine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods.As you progress, you’ll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that’ll demonstrate the practical implementation of each technique.By the end of this book, you’ll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models.
Revathi Gopalakrishnan, Avinash Venkateswarlu
Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples.You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains.By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.
Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler
OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition.You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system.By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.
Machine Learning for OpenCV. Intelligent image processing with Python
Michael Beyeler
Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind.OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for.Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning.By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Ben Auffarth
The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems.Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You’ll also have a look at real-world case studies covering weather, traffic, biking, and stock market data.By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series.
Machine Learning Hero. Master Data Science with Python Essentials
Cuantum Technologies LLC
This book takes you on a journey through the world of machine learning, beginning with foundational concepts such as supervised and unsupervised learning, and progressing to advanced topics like feature engineering, hyperparameter tuning, and dimensionality reduction. Each chapter blends theory with practical exercises to ensure a deep understanding of the material.The book emphasizes Python, introducing essential libraries like NumPy, Pandas, Matplotlib, and Scikit-learn, along with deep learning frameworks like TensorFlow and PyTorch. You’ll learn to preprocess data, visualize insights, and build models capable of tackling complex datasets. Hands-on coding examples and exercises reinforce concepts and help bridge the gap between knowledge and application.In the final chapters, you'll work on real-world projects like predictive analytics, clustering, and regression. These projects are designed to provide a practical context for the techniques learned and equip you with actionable skills for data science and AI roles. By the end, you'll be prepared to apply machine learning principles to solve real-world challenges with confidence.
Saleh Alkhalifa
The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time.You’ll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data.By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP.
AshishSingh Bhatia, Bostjan Kaluza
As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge.Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11.Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level.By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.