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My Green Notebook: "Know Thyself" Before Changing Jobs is a guided reflection developed to draw out your lessons learned and help you thrive as a leader as you take on positions of increased responsibility, regardless of your line of work. We believe that reading, writing, and reflection are the keys to self-improvement and development. This notebook is your opportunity to spend a few minutes with yourself each day for a month and examine your experiences so that you can better understand your strengths and weaknesses and, more importantly, determine your opportunities for growth. "The book is original, innovative, invaluable--a portal into the mindset of a leader and a guide for the rest of us in enhancing our own mindfulness and self-awareness." Steven Pressfield, Bestselling author of Gates of Fire and The War of Art "When you change jobs, you are expressing your values. You'll choose much more wisely and get much further faster if you slow down at this critical juncture and get explicit with yourself about your values. This book will help you get to know yourself a little better, so you can make a better choice about your next step." Kim Scott, Bestselling author of Radical Candor "An innovative and useful tool for the difficult, and often-overlooked, task of critical self-evaluation" General Stanley McChrystal, U.S. Army (Retired)
ProgExpand your skillset by learning how to perform data science, machine learning, and generative AI experiments in .NET Interactive notebooks using a variety of languages, including C#, F#, SQL, and PowerShell Key Features Learn Conduct a full range of data science experiments with clear explanations from start to finish Learn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problems Access all of the code online as a notebook and interactive GitHub Codespace Purchase of the print or Kindle book includes a free PDF eBook Book Description As the fields of data science, machine learning, and artificial intelligence rapidly evolve, .NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in .NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your .NET skills into the world of analytics and AI. With Microsoft’s .NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as .NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for .NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you’ll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you’ll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the .NET ecosystem. What you will learn Load, analyze, and transform data using DataFrames, data visualization, and descriptive statistics Train machine learning models with ML.NET for classification and regression tasks Customize ML.NET model training pipelines with AutoML, transforms, and model trainers Apply best practices for deploying models and monitoring their performance Connect to generative AI models using Polyglot Notebooks Chain together complex AI tasks with AI orchestration, RAG, and Semantic Kernel Create interactive online documentation with Mermaid charts and GitHub Codespaces Who this book is for This book is for experienced C# or F# developers who want to transition into data science and machine learning while leveraging their .NET expertise. It’s ideal for those looking to learn ML.NET and Semantic kernel and extend their .NET skills to data science, machine learning, and Generative AI Workflows.rammer’s guide to data science using ML.NET, OpenAI, and Semantic Kernel
In addition to the approaches and methods covered in the first edition, this edition includes new chapters, such as whole language, multiple intelligences, neurolinguistic programming, competency-based language teaching, co-operative language learning, content-based instruction, task-based language teaching, and The Post-Methods Era.
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