Programowanie
Darmie Akinlaja, Damilare Akinlaja
L?ñVE is a game development framework for making 2D games using the Lua programming language. L?ñVE is totally free, and can be used in anything from friendly open-source hobby projects, to closed-source commercial ones. Using the Lua programming framework, one can use L?ñVE2D to make any sort of interesting games.L?ñVE for Lua Game Programming will quickly and efficiently guide you through how to develop a video game from idea to prototype. Even if you are new to game programming, with this book, you will soon be able to create as many game titles as you wish without stress.The L?ñVE framework is the quickest and easiest way to build fully-functional 2D video games. It leverages the Lua programming language, which is known to be one of the easiest game development languages to learn and use. With this book, you will master how to develop multi-platform games for Windows, Linux, and Mac OS X. After downloading and installing L?ñVE, you will learn by example how to draw 2D objects, animate characters using sprites, and how to create game physics and game world maps.L?ñVE for Lua Game Programming makes it easier and quicker for you to learn everything you need to know about game programming. If you're interested in game programming, then this book is exactly what you've been looking for.
Robert Wiebe
It's never been more important to have the ability to develop an App for Mac OS X. Whether it's a System Preference, a business app that accesses information in the Cloud, or an application that uses multi-touch or uses a camera, you will have a solid foundation in app development to get the job done.Mac Application Development by Example takes you through all the aspects of using the Xcode development tool to produce complete working apps that cover a broad range of topics. This comprehensive book on developing applications covers everything a beginner needs to know and demonstrates the concepts using examples that take advantage of some of the most interesting hardware and software features available.You will discover the fundamental aspects of OS X development while investigating innovative platform features to create a final product which take advantage of the unique aspects of OS X.Learn how to use Xcode tools to create and share Mac OS X apps. Explore numerous OS X features including iCloud, multi-touch trackpad, and the iSight camera.This book provides you with an illustrated and annotated guide to bring your idea to life using fundamental concepts that work on Mac.
Oliver Theobald
Starting with Python syntax and data types, this guide builds toward implementing key machine learning models. Learn about loops, functions, OOP, and data cleaning, then transition into algorithms like regression, KNN, and neural networks. A final section walks you through model optimization and building projects in Python.The book is split into two major sections—foundational Python programming and introductory machine learning. Readers are guided through essential concepts such as data types, variables, control flow, object-oriented programming, and using libraries like pandas for data manipulation.In the machine learning section, topics like model selection, supervised vs unsupervised learning, bias-variance, and common algorithms are demystified with practical coding examples. It’s a structured, clear roadmap to mastering both programming and applied ML from zero knowledge.
Giuseppe Bonaccorso
In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem
Giuseppe Bonaccorso
In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem
Yoon Hyup Hwang, Nicholas C. Burtch
In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience.This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales.Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standardsBy the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge.
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 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.
Rodolfo Bonnin
Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you’ll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications.
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.
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.
Hyatt Saleh
As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.
Hyatt Saleh
As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.
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.
Machine learning i natural language processing w programowaniu. Podręcznik z ćwiczeniami w Pythonie
Piotr Wróblewski
Wejdź na nowy poziom programowania z ML i NLP Sztuczna inteligencja stale się rozwija. Właściwie codziennie słyszymy o jej rosnących możliwościach, nowych osiągnięciach i przyszłości, jaką nam przyniesie. Jednak w tej książce skupiamy się nie na przyszłości, a na teraźniejszości i praktycznym obliczu AI - na usługach, które świadczy już dziś. Większość najciekawszych zastosowań sztucznej inteligencji bazuje na ML (uczenie maszynowe, ang. machine learning), NLP (przetwarzanie języka naturalnego, ang. natural language processing) i architekturze RAG (ang. retrieval augmented generation) zwiększającej możliwości tzw. dużych modeli językowych (LLM, ang. large language model). Stanowią one podwaliny budowy systemów AI, bez których te systemy często wcale nie mogłyby powstać. Do niedawna ML i NLP pozostawały domeną badaczy i specjalistów - znajdowały się poza zasięgiem praktyków programowania. Aktualnie jest inaczej, szybkie komputery, pojemne pamięci RAM i zaawansowane procesory pozwalają stosować te technologie w codziennej pracy programisty. Szczególnie programisty języka Python, do którego są one niemal "naturalnie" przypisane. Mało tego, od kodujących w Pythonie coraz częściej wręcz wymaga się umiejętności znajomości obszaru AI. Tym bardziej warto sięgnąć po ten podręcznik z ćwiczeniami, dzięki któremu między innymi: Dowiesz się, jak używać Pythona do rozwiązywania problemów AI Poznasz tajniki analizy tekstów, analizy sentymentu Zrozumiesz, jak skutecznie używać algorytmów klasyfikacji, regresji i grupowania do rozwiązywania problemów biznesowych Pokonwersujesz z ChatGPT - i to bez wchodzenia na stronę internetową tego serwisu
Miroslaw Staron
Although creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products.The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you’ll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality.Towards the end, you’ll address the most challenging aspect of large-scale machine learning systems – ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began – large-scale machine learning software.
Faisal Masood, Ross Brigoli
MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization.You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow.By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built.
Machine learning, Python i data science. Wprowadzenie
Andreas Müller, Sarah Guido
Uczenie maszynowe kojarzy się z dużymi firmami i rozbudowanymi zespołami. Prawda jest taka, że obecnie można samodzielnie budować zaawansowane rozwiązania uczenia maszynowego i korzystać do woli z olbrzymich zasobów dostępnych danych. Trzeba tylko mieć pomysł i... trochę podstawowej wiedzy. Tymczasem większość opracowań na temat uczenia maszynowego i sztucznej inteligencji wymaga biegłości w zaawansowanej matematyce. Utrudnia to naukę tego zagadnienia, mimo że uczenie maszynowe jest coraz powszechniej stosowane w projektach badawczych i komercyjnych. Ta praktyczna książka ułatwi Ci rozpoczęcie wdrażania rozwiązań rzeczywistych problemów związanych z uczeniem maszynowym. Zawiera przystępne wprowadzenie do uczenia maszynowego i sztucznej inteligencji, a także sposoby wykorzystania Pythona i biblioteki scikit-learn, uwzględniające potrzeby badaczy i analityków danych oraz inżynierów pracujących nad aplikacjami komercyjnymi. Zagadnienia matematyczne ograniczono tu do niezbędnego minimum, zamiast tego skoncentrowano się na praktycznych aspektach algorytmów uczenia maszynowego. Dokładnie opisano, jak konkretnie można skorzystać z szerokiej gamy modeli zaimplementowanych w dostępnych bibliotekach. W książce między innymi: podstawowe informacje o uczeniu maszynowym najważniejsze algorytmy uczenia maszynowego przetwarzanie danych w uczeniu maszynowym ocena modelu i dostrajanie parametrów łańcuchy modeli i hermetyzacja przepływu pracy przetwarzanie danych tekstowych Python i uczenie maszynowe: programowanie do zadań specjalnych!
Machine learning, Python i data science. Wprowadzenie
Andreas Müller, Sarah Guido
Uczenie maszynowe kojarzy się z dużymi firmami i rozbudowanymi zespołami. Prawda jest taka, że obecnie można samodzielnie budować zaawansowane rozwiązania uczenia maszynowego i korzystać do woli z olbrzymich zasobów dostępnych danych. Trzeba tylko mieć pomysł i... trochę podstawowej wiedzy. Tymczasem większość opracowań na temat uczenia maszynowego i sztucznej inteligencji wymaga biegłości w zaawansowanej matematyce. Utrudnia to naukę tego zagadnienia, mimo że uczenie maszynowe jest coraz powszechniej stosowane w projektach badawczych i komercyjnych. Ta praktyczna książka ułatwi Ci rozpoczęcie wdrażania rozwiązań rzeczywistych problemów związanych z uczeniem maszynowym. Zawiera przystępne wprowadzenie do uczenia maszynowego i sztucznej inteligencji, a także sposoby wykorzystania Pythona i biblioteki scikit-learn, uwzględniające potrzeby badaczy i analityków danych oraz inżynierów pracujących nad aplikacjami komercyjnymi. Zagadnienia matematyczne ograniczono tu do niezbędnego minimum, zamiast tego skoncentrowano się na praktycznych aspektach algorytmów uczenia maszynowego. Dokładnie opisano, jak konkretnie można skorzystać z szerokiej gamy modeli zaimplementowanych w dostępnych bibliotekach. W książce między innymi: podstawowe informacje o uczeniu maszynowym najważniejsze algorytmy uczenia maszynowego przetwarzanie danych w uczeniu maszynowym ocena modelu i dostrajanie parametrów łańcuchy modeli i hermetyzacja przepływu pracy przetwarzanie danych tekstowych Python i uczenie maszynowe: programowanie do zadań specjalnych!
Georgia Kalyva, George Kavvalakis
With AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure.This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture.By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.
Alexia Audevart, Konrad Banachewicz, Luca Massaron
The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow.This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression.Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems.With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Joshua Arvin Lat
Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.
Joshua Arvin Lat
Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.
Alessandro Marrandino
BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML.The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement.By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.
Michael Bironneau, Toby Coleman
Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go.The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced.The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum.The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring.At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.
Brett Lantz
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data.You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data.