Applied Machine Learning and AI for Engineers by
While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment, or predict early failures in rotating machinery? This practical book teaches you the skills necessary to put AI and machine learning to work at your company. Applied Machine Learning and AI for Engineers provides examples and illustrations from the AI and ML course Prosise teaches at companies and research institutions worldwide. There's no fluff and no scary equations--just a fast start for engineers and software developers, complete with hands-on examples. This book helps you: Learn what machine learning and deep learning are and what they can accomplish Understand how popular learning algorithms work and when to apply them Build machine learning models in Python with Scikit-Learn, and neural networks with Keras and TensorFlow Train and score regression models and binary and multiclass classification models Build facial recognition models and object detection models Build language models that respond to natural-language queries and translate text to other languages Use Cognitive Services to infuse AI into the apps that you write
Behavioral Data Analysis with R and Python by
Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis. Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, this practical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately. Understand the specifics of behavioral data Explore the differences between measurement and prediction Learn how to clean and prepare behavioral data Design and analyze experiments to drive optimal business decisions Use behavioral data to understand and measure cause and effect Segment customers in a transparent and insightful way
Data Management at Scale by
As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization. Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabric Go deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and more Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata
Data Visualization with Python and JavaScript by
How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries
Essential Math for AI by
Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions
Essential Math for Data Science by
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
Fluent Python by
Don't waste time bending Python to fit patterns you've learned in other languages. Python's simplicity lets you become productive quickly, but often this means you aren't using everything the language has to offer. With the updated edition of this hands-on guide, you'll learn how to write effective, modern Python 3 code by leveraging its best ideas. Discover and apply idiomatic Python 3 features beyond your past experience. Author Luciano Ramalho guides you through Python's core language features and libraries and teaches you how to make your code shorter, faster, and more readable. Complete with major updates throughout, this new edition features five parts that work as five short books within the book: Data structures: Sequences, dicts, sets, Unicode, and data classes Functions as objects: First-class functions, related design patterns, and type hints in function declarations Object-oriented idioms: Composition, inheritance, mixins, interfaces, operator overloading, protocols, and more static types Control flow: Context managers, generators, coroutines, async/await, and thread/process pools Metaprogramming: Properties, attribute descriptors, class decorators, and new class metaprogramming hooks that replace or simplify metaclasses
Hands-On Healthcare Data by
Healthcare is the next frontier for data science. Using the latest in machine learning, deep learning, and natural language processing, you'll be able to solve healthcare's most pressing problems: reducing cost of care, ensuring patients get the best treatment, and increasing accessibility for the underserved. But first, you have to learn how to access and make sense of all that data. This book provides pragmatic and hands-on solutions for working with healthcare data, from data extraction to cleaning and harmonization to feature engineering. Author Andrew Nguyen covers specific ML and deep learning examples with a focus on producing high-quality data. You'll discover how graph technologies help you connect disparate data sources so you can solve healthcare's most challenging problems using advanced analytics. You'll learn: Different types of healthcare data: electronic health records, clinical registries and trials, digital health tools, and claims data The challenges of working with healthcare data, especially when trying to aggregate data from multiple sources Current options for extracting structured data from clinical text How to make trade-offs when using tools and frameworks for normalizing structured healthcare data How to harmonize healthcare data using terminologies, ontologies, and mappings and crosswalks
Introducing Python by
Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you've learned. You'll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages.
Learning Modern Linux by
If you use Linux in development or operations and need a structured approach to help you dive deeper, this book is for you. Author Michael Hausenblas also provides tips and tricks for improving your workflow with this open source operating system. Whether you're a developer, software architect, or site reliability engineer, this hands-on guide focuses on ways to use Linux for your everyday needs, from development to office-related tasks. Along the way, you'll gain hands-on experience with modern Linux terminals and shells, and learn how to manage your workloads. You'll understand how to run Linux applications by using containers, systemd, modern filesystems, and immutable distros such as Flatcar and Bottlerocket. Use Linux as a modern work environment, rather than just from an admin perspective Learn critical components such as the Linux kernel, terminal multiplexer, human-friendly shells, and portable shell scripting Become familiar with access control, from file permissions to capabilities, and understand the role of filesystems as a fundamental building block Learn about application dependency management and containers Gain hands-on experience with the Linux networking stack and tooling, including DNS Apply modern operating system observability to manage your workloads Become familiar with interprocess communication, virtual machines, and selected security topics
Mastering API Architecture by
Most organizations with a web presence build and operate APIs; the doorway for customers to interact with the company's services. Designing, building, and managing these critical programs affect everyone in the organization, from engineers and product owners to C-suite executives. But the real challenge for developers and solution architects is creating an API platform from the ground up. With this practical book, you'll learn strategies for building and testing REST APIs that use API gateways to combine offerings at the microservice level. Authors James Gough, Daniel Bryant, and Matthew Auburn demonstrate how simple additions to this infrastructure can help engineers and organizations migrate to the cloud; and open the opportunity to connect internal services using technologies like a service mesh. Learn API fundamentals and architectural patterns for building an API platform Use practical examples to understand how to design, build, and test API-based systems Deploy, operate, and configure key components of an API platform Use API gateways and service meshes appropriately, based on case studies Understand core security and common vulnerabilities in API architecture Secure data and APIs using threat modeling and technologies like OAuth2 and TLS Learn how to evolve existing systems toward API- and cloud-based architectures
Operating Continuously by
Software delivery doesn't stop with deployment. Modern teams rely on an emerging set of best practices post-deployment to continuously improve, release, and operate their software. With this practical guide, CTOs, software architects, and senior engineering leaders will learn how to apply these practices to their existing operations. Authors Edith Harbaugh, Cody De Arkland, and Brian Rinaldi provide actionable insights into setting up and maintaining a smooth operational process post-deployment. You'll learn new approaches to releasing software, controlling systems at runtime, and measuring the impact of change. This book helps you: Understand how modern development processes have moved beyond the DevOps infinity loop Understand the evolution of CI/CD, and the operational impacts of that change Use the tools and processes necessary to measure the impact of change on production systems Explore an emerging class of techniques to separate deployment from release Learn how to use canary launches and feature flags to release faster with less risk Use experimentation and impact analysis to drive data driven software improvements
Practical Data Privacy by
Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?
Python Data Science Handbook by
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
Python for Data Analysis by
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the Jupyter notebook and IPython shell for exploratory computing Learn basic and advanced features in NumPy Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
Python for Geospatial Data Analysis by
In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions. Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python. This book helps you: Understand the importance of applying spatial relationships in data science Select and apply data layering of both raster and vector graphics Apply location data to leverage spatial analytics Design informative and accurate maps Automate geographic data with Python scripts Explore Python packages for additional functionality Work with atypical data types such as polygons, shape files, and projections Understand the graphical syntax of spatial data science to stimulate curiosity
Raspberry Pi Cookbook by
If you've started to work with Raspberry Pi, you know that Raspberry Pi's capabilities are continually expanding. The fourth edition of this popular cookbook provides more than 200 hands-on recipes (complete with code) that show you how to run this tiny low-cost computer with Linux, program it with Python, hook it up to sensors and motors, and use it with the internet of things (IoT). This new edition includes new chapters on the Raspberry Pi Pico and machine learning with the Raspberry Pi. These easy-to-use recipes will show you, step-by-step, how to: Set up your Raspberry Pi and connect to a network Work with its Linux-based operating system Program your Raspberry Pi with Python Give your Pi "eyes" with computer vision Recognize objects from video and sounds using machine learning Control hardware through the GPIO connector Use your Raspberry Pi to run different types of motors Work with switches, keypads, and other digital inputs Use sensors to measure temperature, light, and distance Connect to IoT devices in various ways and automate your home Use the Raspberry Pi Pico microcontroller board with your Raspberry Pi
Security Chaos Engineering by
Cybersecurity is broken. Year after year, attackers remain unchallenged and undeterred, while engineering teams feel pressure to design, build, and operate "secure" systems. Failure can't be prevented, mental models of systems are incomplete, and our digital world constantly evolves. How can we verify that our systems behave the way we expect? What can we do to improve our systems' resilience? In this comprehensive guide, authors Kelly Shortridge and Aaron Rinehart help you navigate the challenges of sustaining resilience in complex software systems by using the principles and practices of security chaos engineering. By preparing for adverse events, you can ensure they don't disrupt your ability to innovate, move quickly, and achieve your engineering and business goals. Learn how to design a modern security program Make informed decisions at each phase of software delivery to nurture resilience and adaptive capacity Understand the complex systems dynamics upon which resilience outcomes depend Navigate technical and organizational trade-offsthat distort decision making in systems Explore chaos experimentation to verify critical assumptions about software quality and security Learn how major enterprises leverage security chaos engineering
