[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-129202-en":3,"doc-seo-129202-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},129202,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",6,"Technology","Basics of Machine Learning and Data Science with Python - Level 3 - Hideaki Miyoshi","The guide introduces machine learning and data science through Python by treating data as a source of truthful signals that become meaningful storylines after the right questions, statistical thinking, and model building. It covers the full workflow, from Python-based data preprocessing and hypothesis testing to constructing and evaluating machine learning models. Learning is structured as an iterative loop that combines paper-and-pen mathematical reasoning with immediate verification in code, including a bridge to future applications, with support via downloadable Google Colab notebooks.","Basics of Machine Learning and Data Science with Python  \n\"Curiosity for math: The foundation of everything in the AI era.\"  \nGoogle Colaboratory, Google Colab, and the Google logo are trademarks or registered  \ntrademarks of Google LLC.  \nPython is a trademark or registered trademark of the Python Software Foundation.  \nPrologue: Let the Data Speak, Envision the Future  \nWhen faced with massive amounts of data, it is initially nothing more than a mere sequence of numbers. Yet, the moment we pose the right questions, examine it through the lens of statistics, and apply the engine of machine learning, data transforms into a powerful storyteller—one that predicts the future and guides decision-making.  \nThis book is not a simple manual that just lists how to use different tools. It is a comprehensive guide designed to help you acquire a practical mindset: how to solve complex, real-world problems using the power of data science. It systematically covers the entire pipeline, starting from data preprocessing in Python and statistical hypothesis testing, all the way to building and evaluating machine learning models.  \nOur world is full of uncertainty. That is exactly why I hope this book helps you experience the joy of clearly deciphering this complex world, step by step, grounded in the objective facts that data provides.  \nHideaki Miyoshi / Geraldine Angaga Lickas  \nHow to Use This Book—Paper, Pen, and Python  \nThe way to learn with this book is very simple. You will always move between these three things.  \nFigure 1.1 The learning flow of this book  \n① 􎇼􎇻􎇺 Think with paper and pen. First, use your own hands. Write equations, draw pictures, and ask yourself, “What does this mean?” This is mathematical thinking.  \n②  Translate it into Python. Next, “translate” your ideas into Python code. The computer can calculate and draw graphs in a moment. This is coding.  \n③ 􎈯 Learn both at the same time. The equation on paper and the result on the screen match—“They are exactly the same!” When you repeat this experience, you will learn math and programming at the same time, and deeply.  \nFigure 1.2 The four-step learning.  \nAt the start of each chapter, you first catch “the key math idea of this chapter” (the starting point) . Then you learn through the four steps below. You do not have to do all of them every time. At first, just moving between 􎇼􎇻􎇺 and  is enough.  \n The key math idea of this chapter (start) Catch “what you will learn, and why”in a few words.  \nStep 1 􎇼􎇻􎇺 Solve with paper and pen Calculate with your own hands and change the form of equations. When you move your hands, the ideas become part of you. Step 2 􊻱 Think about “Why?” Think about what a formula means and why adefinition is made that way. This is the most interesting part of high school math. Step 3  Experiment with Python Make a guess, then check it with code. “I tried it, and it really happened!” —this makes your understanding strong.  \nStep 4 􋝛􋝙􋝟􋝞 A bridge to next level. Take a quick look at how what you learn will be  \nuseful in the future.  \n􀟅 Different skill levels are OK  \nMaybe you are good at programming, or maybe it has been a long time. This book explains everything with both pictures and code. If the code feels difficult, just follow the pictures and the “paper and pen”explanations first. Furthermore, a Colab note file (downloadable) is provided that explains the Python code step by step.  \nBefore You Start—Let’s Use Google Colab  \nIn this textbook, we will learn Python with a tool called “Google Colab.”  \nGoogle Colab is a useful free service. Anyone can use Python with just a browser (Chrome, Safari, Edge, etc.) connected to the internet. You do not need to install anything on your computer.  \n\u003CNotice> Colab notebook files linked to the book can be downloaded from the following address:  \n[https://chic.institute/](https://chic.institute/)file-downloads/  \nPlease use this by selecting \"Upload Notebook\" from the Colab file  \nmenu.  \n■ Two types of Colab notebook files","cbCaiiXwsp4uaGap","https://ap.wps.com/l/cbCaiiXwsp4uaGap","pdf",8229572,3,1,249,"English","en",105,"# Prologue: Let the Data Speak, Envision the Future\n# How to Use This Book—Paper, Pen, and Python\n## The four-step learning flow\n# Before You Start—Let’s Use Google Colab\n## What is Google Colab\n## Step 1: Open Colab\n## Step 2: Write and Run Code\n## Useful shortcuts","[{\"question\":\"How does the book recommend learning math and programming together?\",\"answer\":\"It cycles through a four-step flow: solve with paper and pen, interpret formulas by asking “Why?”, experiment by checking guesses with Python, and then connect what you learn to the next level.\"},{\"question\":\"What role does Google Colab play in this learning process?\",\"answer\":\"The book uses Colab to run Python in a browser without installing anything, and provides downloadable Colab notebooks linked to the book to explain code step by step.\"},{\"question\":\"What topics are covered in the overall machine learning pipeline?\",\"answer\":\"It explains the end-to-end workflow starting from data preprocessing in Python, moving through statistical hypothesis testing, and then building and evaluating machine learning models.\"}]","Basics of Machine Learning and Data Science with Python - 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