Bøker av Oliver Theobald

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.

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!