Bøger af Oliver Theobald

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?Well, hold on there...Before you embark on your journey, there are some high-level theory and statistical principles to weave through first.But rather than spend $30-$50 USD on a thick textbook, you may want to read this book first. As a clear and concise alternative, this book provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations. Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy to follow along at home.New Updated EditionThis new edition features extended chapters with quizzes, free supplementary online video tutorials for coding models in Python, and downloadable resources not included in the Second Edition. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and give a clear lay of the land.In This Step-By-Step Guide You Will Learn:¿ How to download free datasets ¿ What tools and machine learning libraries you need ¿ Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data ¿ Preparing data for analysis, including k-fold Validation ¿ Regression analysis to create trend lines ¿ k-Means Clustering to find new relationships ¿ The basics of Neural Networks ¿ Bias/Variance to improve your machine learning model ¿ Decision Trees to decode classification, and ¿ How to build your first Machine Learning Model to predict house values using PythonFrequently Asked QuestionsQ: Do I need programming experience to complete this e-book? A: This e-book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see some programming used in this book. Q: I have already purchased the Second Edition of Machine Learning for Absolute Beginners, should I purchase this Third Edition? A: As the same topics from the Second Edition are covered in the Third Edition, you may be better served reading a more advanced title on machine learning. If you have purchased a previous edition of this book and wish to get access to the free video tutorials, please email the author. Q: Does this book include everything I need to become a machine learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master machine learning.

Learn How to Make Your Own Recommender System in an Afternoon.Recommender systems are one of the most visible applications of machine learning and their uncanny ability to convert our unspoken actions into items we like is both addicting and concerning. Recommender systems, though, are here to stay and for anyone beginning their journey in data science, this is a lucrative space for future employment. This book will get you up and running with the basics as well as the steps to coding your own recommender system using Python. Exercises include predicting book recommendations, relevant house properties for online marketing purposes, and whether a user will click on an ad campaign. Who is the Book For?The contents of this book is designed for beginners with some background knowledge of data science, including classical statistics and computing programming. If this is your first exposure to data science, you may want to spend a few hours to read my first book Machine Learning for Absolute Beginners before you get started here. Topics covered in this book: - How to set up a free and easy sandbox environment using Jupyter Notebook- How to prepare your data for processing- How to code a collaborative filtering model- How to code a content-based filtering model- How recommender systems are evaluated- What you need to know about privacy and ethics- What the future of Recommender Systems might look like

Ready to add Machine Learning to your skill stack?As the second title in the Machine Learning From Scratch series, this book teaches you how to code machine learning models in Python. By working on different projects with repeatable steps, you will have the blueprints and the effective strategies to code and design prediction models using your own data.Who is this book for?The book is designed for beginners with basic background knowledge of machine learning, including common algorithms such as logistic regression and decision trees. For a gentle explanation of machine learning theory minus the code, we suggest reading the first book in this series Machine Learning for Absolute Beginners (Third Edition), which is written for a more general audience.In this step-by-step guide you will learn: - How to code a machine learning prediction model using a range of algorithms including logistic regression, gradient boosting, and decision trees.- How to install a development environment and use the programming language Python to code 10 different models.- How to write your model in the least amount of code possible with the help of Pandas, Scikit-learn, Matplotlib, and Seaborn.- How to visualize relationships in your dataset including Heatmaps and Pairplots with just a few lines of code.

Sind Sie bereit, eine GPU-Instanz zu entwickeln und Petabyte von Daten zu durchsuchen? Möchten Sie Ihrem LinkedIn-Profil "Maschinelles Lernen" hinzufügen?Nun, warten Sie mal....Bevor Sie sich auf den Weg in die Welt des maschinellen Lernens machen, gibt es eine wichtige Theorie und statistische Prinzipien, die Sie zunächst verstehen müssen. In dem Buch "Maschinelles Lernen für absolute Anfänger" lernen Sie die Grundlagen des maschinellen Lernens kennen und lernen, Ihr eigenes Vorhersagemodell mit Hilfe eines Immobiliendatensatzes zu kodieren, um Immobilienpreise vorherzusagen. Dieses Buch wurde für absolute Anfänger geschrieben und konzipiert. Das bedeutet, dass keine Programmierkenntnisse erforderlich sind. Wo Algorithmen eingeführt werden, werden eindeutige Erklärungen und visuelle Beispiele hinzugefügt, um es einfach und ansprechend zu gestalten, so dass Sie zu Hause damit weitermachen können.Denken Sie daran, dass dieses Buch erstmalig 2017 auf Englisch (Machine Learning for Absolute Beginners) erschienen ist und 2018 ins Deutsche übersetzt wurde.

Data is collected constantly: how far we travel, who we interact with online and where we spend our money. Every bit of data has a story to tell but isolated, these morsels of information lie dormant and useless, like unattached Lego blocks. Written by the author of Amazon Best Seller Machine Learning for Absolute Beginners, this book guides you through the fundamentals of inferential and descriptive statistics with a mix of practical demonstrations, visual examples, historical origins, and plain English explanations. As a resource for beginners, this book won't teach you how to beat the market or predict the next U.S. election but ensures a concise and simple-to-understand supplement to a standard textbook. This includes an introduction to important techniques used to infer predictions from data, such as hypothesis testing, linear regression analysis, confidence intervals, probability theory, and data distribution. Descriptive statistics techniques such as central tendency measures and standard deviation are also covered in this book. Full Overview of Book Themes- The Historical Development of Statistics- Data Sampling - Central Tendency Measures - Measures Of Spread - Measures Of Position- Designing Hypothesis Tests- Probability and Bayes Theory- Regression Analysis- Clustering Analysis As the launchpad to quantitative research, business optimization or a promising career in data science, it's never been a better time to brush up on statistics or learn these concepts for the very first time.

Feel like you're missing out on ChatGPT?If so, you're not alone and there is still time to master ChatGPT and 10x your productivity.In the rapidly evolving digital landscape, the ability to communicate effectively with AI-powered software is becoming increasingly important. The rise of natural language processing (NLP) technologies, such as ChatGPT, has revolutionized the way we interact with various software applications. This book aims to provide readers with a comprehensive understanding of how to harness the full potential of ChatGPT using proven prompt writing techniques including priming, training, and negative prompting. Whether you're a student, researcher, or simply curious about the potential of AI and NLP, this book offers a fascinating look into the inner workings of ChatGPT and its implications for the future of communication. Don't miss this opportunity to explore the cutting edge of conversational AI. Read the ChatGPT Prompts Book today and join the conversation!