dipopotamus/custom_instruct_coursera_course_module_list huggingface dataset:
Course: Generative AI Teach-Out
Modules:
What is a Teach-Out?, What is AI? What is ChatGPT?, What is Generative AI? What are Large Language Models (LLMs)?, GPT's Continual Learning, Generative AI in Art and Architecture, What is Embodied AI?, Emerging Research on LLMs, SEAGULL: Example of Embodied AI, Generative AI’s Perceived Accuracy, Implications of ChatGPT's Humanlike Output, Pros & Cons of ChatGPT's Humanlike Response, Humanlike Output & Logical Reasoning, Interpretability of AI, Writing Good Prompts for ChatGPT, Optional: ChatGPT Example - Simple Math Problem, Optional: ChatGPT Example - Complex Math Problem, Optional: ChatGPT Example - Programming (Part 1), Optional: ChatGPT Example - Programming (Part 2), Optional: ChatGPT Example - Character Backstory in Dungeons and Dragons, Setting the Scene: Ethics in AI, Setting the Scene: Limitations of Generative AI Chatbots, Setting the Scene: Plagiarism & Attribution, Bias in AI Training Data, Representation in AI Training Data, Bias & Discrimination in AI Output, Hallucinations, Deep Fakes, AI and Creative Works, Training AI, Building Generative AI Systems, Output Ownership & Monetization, Ownership of AI Generated Art, Implications of Using Different Forms of AI, Data Privacy, Regulation of AI, Society’s Response to New Technology, Short and Long-term Economic Impacts of Generative AI, Short and Long-term Societal Impacts of Generative AI, Economic Responses to Generative AI, Effects on Human Capital, AI, Business, & Entrepreneurship, Challenges for Businesses, Prompt Engineering, Implications on Education, Using ChatGPT for Skill Development, Evolving Skill Development in Art & Architecture, ChatGPT as a Teammate, AI in Medical Care, Practice, & Education, Implications of ChatGPT Passing Certification Exams, AI in Education: Skepticism vs. Cynicism, The Future of ChatGPT, AI for Advice - General Questions & Self-help, AI for Advice - Delicate Questions, Future of AI in Art & Architecture, Emerging Trends on Using AI in Business, Future Opportunities for AI Technologies, Live Q&A Panel - August 23, 2023
Course: Introduction to DevOps
Modules:
Course Introduction, Business Case for DevOps, DevOps Adoption, Definition of DevOps, Essential Characteristics of DevOps, Leading Up to DevOps, XP, Agile, and Beyond, Brief History of DevOps
Social Coding Principles, Git Repository Guidelines, Working in Small Batches, Minimum Viable Product, Test Driven Development, Behavior Driven Development, Cloud Native Microservices, Designing for Failure
Taylorism and Working in Silos, Software Engineering vs. Civil Engineering, Required DevOps Behaviors, Infrastructure as Code, Continuous Integration, Continuous Delivery
Organizational Impact of DevOps, There is No DevOps Team, Everyone is Responsible for Success
Rewarding for “A” while hoping for “B”, Vanity metrics vs. Actionable metrics, How to Measure Your Culture, Comparison of DevOps to Site Reliability Engineering
Congratulations & Next Steps, Team & Acknowledgements
Course: تقارير المبيعات باستخدام HubSpot
Modules:
مقدمة إلى البرنامج, فيديو مقدمة الدورة التدريبية, وظائف في إدارة المبيعات وإدارة علاقات العملاء, مقدمة أسبوعية: أساسيات الأعمال التجارية القائمة على البيانات, حالة لاتخاذ القرارات القائمة على البيانات, استخدام البيانات في تحديد أهداف نشاطك التجاري, تحديد الأهداف المستندة إلى البيانات الخاصة بنشاطك التجاري, تفريغ بيانات CRMs (إدارة علاقات العملاء) وبيانات الكائن, إعداد CRM (إدارة علاقات العملاء) الخاصة بك لإعداد التقارير, كيفية إنشاء خصائص جهات اتصال مخصصة في HubSpot, تدقيق وتنقيح بياناتك في HubSpot, مراجعة أسبوعية: أسس الأنشطة التجارية القائمة على البيانات
مقدمة أسبوعية: استخدام التقارير لتحقيق النجاح, استخدام البيانات في مرحلة الجذب: الفرضية واختبار A/B, استخدام البيانات في مرحلة الجذب: استخدام التجارب للتعرف على جمهورك, محور العملاء في HubSpot: التسويق لسلالة جديدة, مقاييس مرحلة الإشراك التي  ينبغي مراعاتها, التنبؤ والتحليلات في مركز المبيعات, اتخاذ القرارات القائمة على البيانات في خدمة العملاء, مراجعة أسبوعية: استخدام التقارير لتحقيق النجاح
مقدمة أسبوعية: لوحات المعلومات والتقارير المخصصة, اعتبارات البيانات قبل إنشاء تقارير مخصصة, خطوات إنشاء تقرير مخصص في HubSpot: أهمية سؤال البحث الجيد, خطوات إنشاء تقرير مخصص في HubSpot: استخدام مصادر البيانات, خطوات إنشاء تقرير مخصص في HubSpot: مثال على الإرشادات التفصيلية لواجهة المستخدم لإنشاء تقرير مخصص في HubSpot, خطوات إنشاء تقرير مخصص في HubSpot: إرشادات تفصيلية عن واجهة المستخدم: أهمية عوامل التصفية, خطوات إنشاء تقرير مخصص في HubSpot: إرشادات تفصيلية عن واجهة المستخدم: إنشاء تقرير مخصص يحتوي على بيانات الكائن والحدث, خطوات إنشاء تقرير مخصص في HubSpot: إرشادات تفصيلية عن واجهة المستخدم: استخدام بيانات المبيعات في التقارير المخصصة, أفضل ممارسات تصور البيانات, أساسيات تصميم لوحة المعلومات, تصميم لوحات معلومات جيدة المظهر في HubSpot, تغيير حجم التقارير على لوحة معلومات HubSpot الخاصة بك, مراجعة أسبوعية: لوحات المعلومات والتقارير المخصصة
مقدمة أسبوعية: إعداد التقارير باستخدام مشروع HubSpot, نظرة عامة على المشروع, تهانينا! إعداد التقارير باستخدام HubSpot
Course: Oracle SQL Practice Course
Modules:
SQL Practice Course Intro, Entity Relationship Diagram, Installing Oracle, Installing Oracle SQL Developer
Using The Create Table Statement, Extending The Create Table Statement, Using SQLPlus with an Editor, Using the Alter Table Statement, Practice DML Statements in SQLPlus
Exercise 1: Create and Populate Tables, Exercise 2: Modify, Drop, and Update Tables
Course: Introduction to VueJS Framework
Modules:
Navigating Codio Assignments, How to Reset an Assignment in Codio, Creating HTML Elements Notes (Optional Download), Decorating Elements with CSS Notes (Optional Download), Introduction to VueJS Notes (Optional Download)
VueJS Templates & Mustaches Notes (Optional Download), VueJS Computed Props Notes (Optional Download), VueJS Directives Notes (Optional Download)
Developing Todo Items Notes (Optional Download), Storing Todo List Notes (Optional Download), Multiple Components & Emits Notes (Optional Download)
Course: Mastering Cloud FinOps
Modules:
Introduction to the Course, Meet Your Instructor, What you will learn in this lesson, 5R Strategies Part 1, 5R Strategies Part 2, 5R Strategies Part 3, What you will learn in this lesson, Adoption of serverless and containers for more granular, usage-based cost management, Use of machine learning and analytics to gain insights from cloud spend data, Use of Gen AI to gain insights from cloud spend data
What you will learn in this lesson, Explore Cost forecasting, Explore Anomaly detection, Demo- how to set up, What you will learn in this lesson, Choosing the right size based on Utilisation, Eliminating Orphaned resources, Optimisation of extremely low usage
What you will learn in this lesson, Challenges in Adoption, Adoption Roadmap, Targeting persona, What you will learn in this lesson, Planning for FinOps in an organisation, Socialising FinOps for adoption in an organisation, Preparing the organisation for FinOps, Launching FinOps formally
What you will learn in this lesson, Understand the objective and consideration to design a dashboard, High level Approach to Design the dashboard, What you will learn in this lesson, Presenting Your FinOps Case Study, Critiquing and Improving FinOps Projects
Course: Introduction to Prompt Injection Vulnerabilities
Modules:
Welcome and Meet Your Instructor, Define Large Language Models (LLM), Example LLM Application, Demonstration: LLM Capabilities, Exploring the OWASP Top 10, Identifying LLM Attack Methods, LLM Attack, Ultimate Black Box Technology, Security Testing Challenges, Demonstration: Prompt Injection Risk, Passive and Active Methods, Concatenation of Prompts, Demonstration: Prompt Injection Attack Techniques, Principle of Least Services and Privileges, Human Loop, Segregation and Isolation, Demonstration: Segregation
Course: Attention Mechanism - 한국어
Modules:
주목 메커니즘: 개요
Course: Resilience & Leadership: Design, Development, & Integration
Modules:
Course and Module 1 Introduction, Learning from Crisis, Organizational Learning, Learning Organizations, Adaptive Change, Introduction to Adaptive Management, Practicing Adaptive Management
Introduction to Module 2 & Crisis Management Trends Part 1, Crisis Management Trends Part 2, Crisis Management Trends Part 3, Adaptive Change Part 1, Adaptive Change Part 2, Adaptive Management Part 1, Adaptive Management Part 2
COVID-19 & Infrastructure Resilience Part 1, COVID-19 & Infrastructure Resilience Part 2, Climate Change Meta Narratives, Climate Change & Resilience Transformation, Sustainable Development Part 1, Sustainable Development Part 2
Introduction to Module 4 & Human Resilience, Organizational Resilience, Systemic Risks & Human Actions, Vulnerability & Policy, Sustainable Change & Critical Change, Critical Trends, Sustainability, & Alternative Futures
Innovation in Sustainable & Resilient Infrastructure, Resilience & Human Development Part 1, Resilience & Human Development Part 2, Complexity Leadership, Resilience Thinking, Leadership in Crisis
Course: Berbagi Data Melalui Seni Visualisasi
Modules:
Pengantar untuk mengomunikasikan wawasan data Anda, Kevin: Kekuasaan dalam data viz, Mengapa visualisasi data penting, Menghubungkan gambar dengan data, Resep untuk visualisasi yang ampuh, Visualisasi dinamis, Elemen seni, Dampak visualisasi data, Design thinking dan visualisasi, Judul, sub judul (subtitle), dan label, Visualisasi yang mudah diakses, Andrew: Membuat data mudah diakses
Visualisasi data menggunakan Tableau, Tableau Public dan alat online lainnya, Berkenalan dengan Tableau, Membuat visualisasi data di Tableau, Baik, buruk, dan jelek, Menjadi kreatif, Menghubungkan data di Tableau
Bercerita dengan data, Mewujudkan ide, Berbicara kepada audiens Anda, Carolyn: Jurnalisme data, Dasar-dasar dasbor Tableau, Dari filter ke bagan, Kiat agar presentasi menarik, Berbagi narasi, Sundas: Bagaimana mengelola sindrom penipu (imposter syndrome)
Menyatukan Semuanya, Presentasi dengan menggunakan kerangka kerja, Merangkai data ke dalam presentasi Anda, Brittany: Keterampilan presentasi untuk analis data baru, Connor: Contoh presentasi data yang masih berantakan, Connor: Contoh presentasi data yang bagus, Kiat presentasi yang telah teruji, Melakukan presentasi seperti seorang profesional, Mengantisipasi pertanyaan, Menangani keberatan, Praktik terbaik untuk sesi Tanya Jawab, Connor: Menjadi penerjemah data yang ahli, Selamat!
Course: Introduction to Psychology
Modules:
How to take this course
The astonishing hypothesis, Dualism, Neurons, Parts of the brain, Our two brains, A bit of humility, Sigmund Freud, The psychodynamic approach, Stages of development, Defense mechanisms, Scientific assessment of Freud, Taking Freud seriously, B.F. Skinner, Habituation, Classical conditioning, Instrumental conditioning, Scientific assessment of Skinner
Big questions about development, Piaget, Piaget's Developmental Stage Theory, Scientific evaluation of Piaget, Methods for studying infants, How are children different from adults?, Explanations for development, What is language?, Basic facts about language, Phonology, Morphology, Syntax, Language acquisition, Language and thought
From the world to the mind, Problems of perception, Perception of brightness, Perception of objects, Perception of depth, Attention, Important memory distinctions, Memory storage, Learning, Remembering, Failures of memory, False memories
Evolution of emotion, Why evolution matters, Facial expressions, Fear, Kinship, Attachment, Prisoner's dilemma, Irrationality and culture, Social animals, Self, Attribution, Liking, The psychology of groups, Social categories, Social categories II
How are we different?, Personality, Intelligence, Behavioral genetics, Major discoveries about genes and environment, Parenting, Mental illness, Schizophrenia, Mood disorders, Anxiety disorders, Dissassociation disorders, Personality disorders, Therapy
Positive psychology, What is happiness?, Happiness set point, Happiness is relative, Judgments about past events are skewed, Humility and Optimism, Now what?
Course: 用 Python 做商管程式設計（三）(Programming for Business Computing in Python (3))
Modules:
C01-01 案例：生日記錄本, C01-02 類別基本概念(1), C01-03 類別基本概念(2), C01-04 類別基本概念(3), C01-05 成員函數(1), C01-06 成員函數(2), C01-07 成員函數(3), C01-08 進階議題(1), C01-09 進階議題(2)
C02_01_matplotlib 簡介, C02_02_直方圖 (1)：計算繳交時間, C02_03_直方圖 (2)：繪製基本直方圖, C02_04_直方圖 (3)：設定直方圖, C02_05_直方圖 (4)：上網查詢函式庫, C02_06_長條圖, C02_07_圓餅圖, C02_08_散佈圖與折線圖, C02_09_結語
C03_01_在經濟學的應用, C03_02_囚犯困境, C03_03_沙灘賣冰與均衡的概念, C03_04_成本相同之數量競爭 (1)：基本介紹, C03_05_成本相同之數量競爭 (2)：數值均衡分析, C03_06_成本相同之數量競爭 (3)：視覺化, C03_07_成本相異之數量競爭 (1)：基本介紹, C03_08_成本相異之數量競爭 (2)：數值均衡分析與視覺化, C03_09_成本相異之數量競爭 (3)：成本的影響
C04-01 基本概念, C04-02 視窗與元件, C04-03 根號計算器：建立視窗, C04-04 根號計算器：建立元件, C04-05 根號計算器：事件處理函數, C04-06 根號計算器：安排元件, C04-07 根號計算器：完成基本版, C04-08 根號計算器：使用文字方塊與插入圖片, C04-09 散佈圖繪製器：建立視窗與元件, C04-10 散佈圖繪製器：事件處理函數, C04-11 散佈圖繪製器：加上座標
C05-01 網路爬蟲的功用, C05-02 HTML 語言, C05-03 針對標籤做解析, C05-04 使用 requests 函式庫, C05-05 使用 beautifulsoup 函式庫, C05-06 利用標籤屬性做篩選, C05-07 自行解析出價格資訊, C05-08 完成應用與總結
C06-01 機器學習, C06-02 YouBike 預測, C06-03 單迴歸分析, C06-04 複迴歸分析, C06-05 用迴歸模型做預測, C06-06 Overfitting, C06-07 Overfitting 範例, C06-08 在 YouBike 預測避免 Overfitting, C06-09 模型複雜度與預測準確度, C06-10 交互驗證, C06-11 懲罰模型複雜度：原理, C06-12 懲罰模型複雜度：程式碼
Course: Think Again I: How to Understand Arguments
Modules:
Introduction to the Specialization
Why Arguments Matter, What Is an Argument?, What are Arguments Used For? Justification, Strong Arguments Don't Always Persuade Everyone, What Else are Arguments Used For? Explanation, What are Arguments Made Of?  Language, Meaning, Linguistic Acts, Speech Acts, Conversational Acts
Argument Markers, Standard Form, A Problem for Arguments, Assuring, Guarding, Discounting, Evaluation, Close Analysis (Part I), Close Analysis (Part II), More Close Analysis
Validity, Soundness, Get Down to Basics, Sharpen Edges, Organize Parts, A Student Example: A Debate About Smartphones in Class, Fill in Gaps, Conclude, An Example of Reconstruction  (Part I), An Example of Reconstruction  (Part II), An Example of Reconstruction  (Part III)
Final Quiz
Course: Designing and Building Institutional Anti-Racist Spaces
Modules:
Welcome to D-BIAS, Racial Justice Training: Past, Present and Future, Implicit Bias Clip One, Implicit Bias Clip Two, Implicit Bias Clip Three, Examples of Bias from a Personal Story, Performative vs. Intentional
The Four Eras of Criminal Legal (in)justice U.S. History: An Overview, What is Criminal Justice Reform// Equity Reforms vs. Criminalizing, Narrative-shifting: "Tough On Crime" (ToC), Law-in-History Era 1: Brooklyn As a Slave-Holding Capitol and Era 2 Segregation, Applying a Restorative Approach to the Issue
Poetry Discussion  - The Poetry of Structural Inequity, Anti-Racist Workshop Video, Post-Script Discussion about "Looting" vs. "Rioting" Hypothetical
Create Your Plan: Video Dialogue: Ben and Jonathan Final Video, Build Your Own Lab: Video 2 Week 4, Ben and Jonathan
Course: Getting Started with AWS DevOps
Modules:
AWS Certified DevOps Engineer - Professional (DOP-C02) exam overview, Overview of this Course, AWS Elastic Load Balancer - Overview, Auto Scaling In AWS - Overview, Amazon DynamoDB - Overview, Amazon Elasticache - Overview
AWS CloudFormation Overview - Part 1, AWS CloudFormation Overview - Part 2, AWS ElasticBeanstalk - Overview, AWS Lambda: Basic Concepts, AWS Lambda: Features and Quotas, AWS Lambda: Security, Monitoring & Logging, Amazon API Gateway - Overview, AWS Service Catalog - Overview
Amazon CloudWatch: Logs and Events, Amazon CloudWatch: Metrics and Alarms, AWS Kinesis - Overview, AWS RedShift & AWS Quicksight - Overview, AWS CloudTrail - Overview, AWS Config - Overview, AWS Trusted Advisor Overview, AWS Trusted Advisor: Features, Amazon Inspector - Overview
Course: Estratégicos empresariales: Proyecto final
Modules:
Planeación estratégica y Estructura organizacional
Mercado
Finanzas
Calidad
Course: U.S. Health Law Fundamentals
Modules:
Introduction, Overview and Distinctive Features of U.S. Law, The Administrative State and Modern Medical Care, The Basic Duty to Provide Care, EMTALA's Statutory Response, Institutional and Individual Obligations in the Emergency Context
Formation of the Treatment Relationship, The Law of Termination and Abandonment, The Rise of Conscientious Objection, Informed Consent: The Basic Materiality Standard
Quality Control through Gatekeeping: Licensure and Accreditation, Health Care Quality and the Basic Medical Malpractice Standard, Medical Malpractice Reforms and Variations, Hospital Liability, Insurance Company Liability for the Provision of Substandard Care
Introduction to Medicare and Medicaid, Medicaid, Constitutional Structure and the Federal Role in U.S. Health Law, Affordable Care Act: Initial Litigation and Ongoing Legal Controversy, Conclusion and Review
Course: Complete Guide to Unit Testing in .NET Core (NUnit & xUnit)
Modules:
Welcome, Manual Testing Overview, Advantages of Automated Testing, Tools Needed, Types of Tests, Testing Pyramid, Logical Phase of an Automated Test
.NET 6 Update - Nullable, Create Project, Create MSTest Project, First Unit Test, Failed Test Example
Create NUnit Project, First NUnit Test, Debug and Assert, Assignment 1 - Odd Number Checker, Assignment 1 Solution - Odd Number Checker, Assertion Model, Testing multiple values, Combine Unit Test with Expected Result, Asserting Doubles, Asserting String Values, String Assert Helper Methods, Asserting Null Values, Global Initialization for Class
Asserting Collections, Collections Helper Method, Range Assert, Multiple Assert, Working with Exceptions, Exceptions without Message, Set Up Inheritance, Assert Object Type, Assignment 2 - Grading Calculator Part 1, Assignment 2 - Grading Calculator Part 1 Solution, Assignment 2 - Grading Calculator Part 2, Assignment 2 - Grading Calculator Part 2 Solution, Assignment 3 - Fibo Series, Assignment 3 - Fibo Series Solution
Set Up Bank Account Class, Setting Up Logger Class, Integration Test Demo, Unit Test with Fake Logger, Mocking Overview, MOQ in Action, Missing MOQ, Setting Up LogBook Withdrawal, MOQ Setup with Conditional Return, MOQ Setup Default Return Value, MOQ Evaluate the Return Value, MOQ and Out Parameters, MOQ and Ref, MOQ Default Return Values, MOQ Properties, MOQ Callbacks, MOQ Verification
Create xUnit Project, xUnit Comparison, xUnit Calculator Part 1, xUnit Calculator Part 2, Assignment 4 - Grading Calculator, Assignment 4 - Grading Calculator xUnit Solution, Assignment 5 - Fibo Series xUnit, Assignment 5 - Fibo Series xUnit Solution, MOQ and xUnit, Customer xUnit, Assignment 6 - Bank Account xUnit
Set Up Main Project, Bongo Project Code Overview, Bongo Models Date Checker Part 1, Bongo Models Date Checker Part 2, Set Up Data Access Unit Test Project, In-Memory Database and Unit Testing, Data Access Unit Tests, Data Access Unit Tests Bugs and Pitfalls, Set Up Bongo Core Unit Test Project, Bongo Core MOQ Setup and GetAllBooking Test, Bongo Core Exception Unit test, Bongo Core Save Booking Setup, Bongo Core Save Booking Unit Test, Bongo Core Return Code TestCase, Bongo Core Separating Unit Tests, Bongo Web Test Project Setup, Bongo Web Index Action Method Unit Test, Bongo Web Checking View Name, Bongo Web Controller Unit Tests
Course: Value Creation with Dark Data
Modules:
Meet the Instructor, Value Creation Overview
Dark Data Categories With David Hand, The Four Principles of Working With Dark Data, Granularity and Its Impact On Dark Data, Thinking Through Granularity and Avoiding Scope Creep, Designing with a Growth Mindset, Avoiding “Deviation Creep”, Standardizing Dark Data for Data Products, Standardization: Controlling Dark Data, The Difference Between Dynamic Intelligence Layers (DIL) and Data Marts?, Best Management Practices for Dynamic Intelligence Layers (DIL), Helping Your Team Understand the DIL Approach
Difference Between Insights and Actionable Intelligence, Structuring Data for Rapid Intelligence Creation, Integrating Intelligence Into a UI, Data Management Overview, Reference and Master Data Management, Metadata Management, Data Quality Management
Background for the Case Study, Objectives and OKRs, Engineering Problems, Paths Solution
Course: Endpoints and Systems
Modules:
Introduction to Understanding Windows Operating System Basics, Windows Operating System History, Windows Operating System Architecture, Windows Processes, Threads, and Handles, Windows Virtual Memory Address Space, Windows Services, Windows File System Overview, Windows File System Structure, Windows Domains and Local User Accounts, Windows GUI, Run as Administrator, Windows CLI, Windows PowerShell, Windows net Command, Controlling Startup Services and Executing System Shutdown, Controlling Services and Processes, Monitoring System Resources, Windows Boot Process, Windows Networking, Windows netstat Command, Accessing Network Resources with Windows, Windows Registry, Windows Management Instrumentation, Common Windows Server Functions, Common Third-Party Tools, Wrap-Up
Course Introduction to Understanding Linux Operating System Basics, History and Benefits of Linux, Linux Architecture, Linux File System Overview, Basic File System Navigation and Management Commands, File Properties and Permissions, Editing File Properties, Root and Sudo, Disks and File Systems, System Initialization, Emergency/Alternate Startup Options, Shutting Down the System, System Processes, Interacting with Linux, Linux Command Shell Concepts, Piping Command Output, Other Useful Command-Line Tools, Overview of Secure Shell Protocol, Networking, Managing Services in SysV Environments, Viewing Running Network Services, Name Resolution: DNS, Testing Name Resolution, Viewing Network Traffic, Configuring Remote Syslog, Running Software on Linux, Executables vs. Interpreters, Using Package Managers to Install Software in Linux, System Applications, Lightweight Directory Access Protocol, Wrap-Up
Lesson Introduction, Host-Based Personal Firewall, Host-Based Antivirus, Host Intrusion Prevention System, Application Allowed Lists and Blocked Lists, Host-Based Malware Protection, Sandboxing, File Integrity Checking, Wrap-Up
Course: Healthcare Financial Management Fundamentals
Modules:
Course Introduction, Meet Your Faculty: Patrick Holland, Module Introduction, Balance Sheet Basics, Liabilities, Equity, Capital Structure, The Accounting Cycle
Module Introduction, Overview of the Financial Planning Process, Budget Overview and Methodologies Part 1, Budget Overview and Methodologies Part 2, Overview of Budget Variance Analysis, Capital Budgeting, Walkthrough of Operating Budget Development
Module Introduction, Overview of Long-Term Financing, Four Factors Affecting Capital, Types of Long-Term Debt, Debt Compliance Provisions, The Importance of Credit Ratings, Bond Basics, Mayo Clinic on Long-Term Debt Financing
Module Introduction, Overview of Financial Analysis, Ratio Analysis, Ratio Analysis Part 2, Analytical Techniques, Benchmarking and KPIs, Limitations of Financial Condition Analysis
Course: More on Change and Sustainability
Modules:
Leading and Managing- Two Roles a Change Agent Fills, Change Models- Three States; Driving and Resisting Change, Implementing Change- One Person's Experience, Implementing Change- A More Complex Example, Leveraging Roles and Role Expectations
Simple Changes: Should You Go It Alone?, Process Choices: When others must be 'sold', Gaining Support - Stakeholder Commitment Analysis, Gaining Support - Preference (Interest) Mapping, Change Politics: Examples to Analyze
Lesson 1: Green Design Principles, Lesson2: Green Design and LCA (Life Cycle Assessment), Lesson 3: Labels & Certification, Lesson 4: Advertising and Greenwashing, Lesson 5: Doing an LCA (Life Cycle Assessment), Michelle Reott - The Importance of Design, Honors: LCA Assignment
Lesson 1: Overview of Sustainability Reporting, Lesson 2: More on CSR Reporting, Roger Stace - An introduction to CSR reporting, GRI - Sustainability Reporting Starters Kit, GRI Reporting Standards
Tim Turner - Owner Zia Taqueria Restaurants, Dr. Pakhi Chaudhuri, MD, Hunter Mantell-Hecathorn - Home Builder, Claire Attkisson - Colorado Jaynes Construction, Beth Drum - Alpine Bank of Colorado
KSPIs Assignment Instructions
Course: Applied Machine Learning in Python
Modules:
Introduction, What's New?, Key Concepts in Machine Learning, Python Tools for Machine Learning, An Example Machine Learning Problem, Examining the Data, K-Nearest Neighbors Classification
Introduction to Supervised Machine Learning, Overfitting and Underfitting, Supervised Learning: Datasets, K-Nearest Neighbors: Classification and Regression, Linear Regression: Least-Squares, Linear Regression: Ridge, Lasso, and Polynomial Regression, Logistic Regression, Linear Classifiers: Support Vector Machines, Multi-Class Classification, Kernelized Support Vector Machines, Cross-Validation, Decision Trees, One-Hot Encoding (Optional)
Model Evaluation & Selection, Confusion Matrices & Basic Evaluation Metrics, Classifier Decision Functions, Precision-Recall and ROC Curves, Multi-Class Evaluation, Regression Evaluation, Model Selection: Optimizing Classifiers for Different Evaluation Metrics, Model Calibration (Optional)
Naive Bayes Classifiers, Random Forests, Gradient Boosted Decision Trees, Neural Networks, Deep Learning (Optional), Data Leakage, Introduction, Dimensionality Reduction and Manifold Learning, Clustering, Conclusion
Course: A Scientific Approach to Innovation Management
Modules:
Welcome to the course, Operation efficiency vs strategic efficiency, What data can and cannot do, Strategic efficiency, What does the scientific approach do: the Galilean manager, Inkdome case, What is innovation, The structure of the innovation decision, Risk and Uncertainty, Type I and type II errors in innovation decisions, Interactive tour of the Museum of Failure, Antecedents of the Scientific Approach, The Building Blocks: THEED, Formulate and apply theories to managerial problems, Tools: business model canvas and other tools
Basic tools: probabilities, Conditional probabilities and the Bayes Theorem, The Scientific Approach: Theory and Mechanisms, Using the organization to set the decision rule, The Scientific Approach: summary and its use in practice, How to derive hypotheses from a theory, Hypotheses and their context [p values don’t always matter], Cases, Design and logic of hypothesis testing (download the attached datasets), The use of experiments in innovation management, Randomized Control Trials, Split and multivariate tests, Quasi Experimental Design, Innovation metrics, Metrics validity and reliability, Metrics validity, Metrics reliability
Correlation vs causality, Regression analysis: Theory, Regression analysis: Application, Interview with Mimoto: paving the way for electric mobility using a scientific approach, Interview with Eni Gas and Power: leveraging big data to uncover customer preferences, Using data to answer important questions at Google, How firms and startups can gather and analyze data to test hypotheses, Reflection critical evaluation
Difference-in-difference approach: Theory (download the attached datasets), Difference-in-difference approach: Examples (download the attached datasets), Instrumental variables: Theory (download the attached datasets), Instrumental variables: Examples (download the attached datasets), Data science vs causal links, Machine learning for innovation management decisions, Summary, conclusions, limitations of the scientific approach
A Scientific Approach to Innovation Management - Final project
Course: GIS Applications Across Industries
Modules:
Course Introduction, GIS for Environment, GIS Applications in Land Use Planning, Land Use Change Analysis - Part 1, Land Use Change Analysis - Part 2, Land Use Change Analysis - Part 3, Buildout Analysis - Part 1, Buildout Analysis - Part 2, Buildout Analysis - Part 3, Species Distribution - Part 1, Species Distribution - Part 2, Watershed Delineation - Part 1, Watershed Delineation - Part 2, Watershed Delineation - Part 3, Landscape Conservation Planning, Conservation Planning Exercise
Introduction to GIS for Public Health, Disease Surveillance Example - Part 1, Disease Surveillance Example - Part 2, Social Determinates of Health, Social Determinates of Health Exercise, Access to Healthy Places - Part 1, Access to Healthy Places - Part 2
Introduction to GIS for Emergency Management, Evacuation Planning - Part 1, Evacuation Planning - Part 2, Wildfire Risk Assessment, Damage Assessment and Debris Removal
Introduction to GIS for Business, GIS Applications for Business, Market Trend Analysis, Market Analysis, Supply Chain Management, Location Allocation Analysis, 3D Visualization, Digital Twins and the Future of GIS, Course Summary
Course: 商务英语课程:市场与营销英语 Marketing & Sales
Modules:
1.2 开始脑力激荡 The Brainstorming Begins, 1.4 Effective Brainstorming Techniques, 1.7 Giving Opinions in Brainstorming, 1.9 Group Dynamics: What Role Will You Play?, 1.12 Agreeing/Disagreeing In Meetings, 1.16 Week 1 Review
2.2 市场调查的影响力 Survey Power, 2.4 Writing Surveys That Get You Actionable Insights, 2.7 Visually Communicate Data with Charts, 2.11 Chart and Graph Interpretation, 2.14 Graph and Chart Presentation, 2.17 Week 2 Review
3.2 对生物牙膏的印象 BioDent Impressions, 3.4 Perception in Visual Communication, 3.6 Visual Communication Strategies, 3.8 Concise Language for Social Media Marketing, 3.11 Clever and Catchy with Caution, 3.18 Week 3 Review
4.2 社群媒体营销，万岁! Hooray for Social Media Marketing, 4.4 Cialdini's Principles of Influence - Part 1, 4.7 Cialdini's Principles of Influence - Part 2, 4.11 Power of One Word, 4.15 Influential Language, 4.18 Week 4 Review
5.2 准备、就定位、开始推销 Ready..Set..Pitch, 5.4 Preparing your Presentation, 5.8 Consultative Sales Presentations, 5.15 Language for Sales Scripts, 5.18 Impromptu Speaking/Elevator Pitch, 5.25 Week 5 Review
6.2 Week 6 Review
Course: Dark Data Basics - Understanding the Unknown
Modules:
Dark Data, Meet the Instructor, Dark Data Foundations
Dark Data Definitions, High-level Examples of Dark Data, Value of Data Productization, Part 1, Value of Data Productization, Part 2, Value of Data Productization, Part 3, What is Impacted by Dark Data?, Non-obvious Impacts of Dark Data, An Example Use Case of Dark Data
Overview of Dark Data Classification: Inside and Outside, OLTP Systems, OLAP Systems, Externally Integrated Systems, Dark Data Tier 1, Captive Data Deep Dive, Dark Data Discovery, Dark Data Tier 1, Captive Data Example, Overview of Generative Data, Example of Generative Data
Overview of External Integrated Systems, Uncaptured Data Overview, Bridging the Gap: Strategies for Dark Data Awareness, Data Capture Example, Dark Data Tier 4: Unattainable Data, Strategies to Bridge the Gap of Unattainable Dark Data, Example of Working With Unattainable Dark Data, Dark Data Tier 5: Nebulous Data Overview, Nebulous Dark Data Examples
Overview of Computational Thinking, Elements of Computational Thinking, Examples of Computational Thinking, An Overview of Algorithms, Logical Reasoning for Dark Data, Space Travel Problems, Some General Information and Tips, Outcomes and OKR Targets for Space Travel
Course: Getting Started with GameMaker
Modules:
Course Introduction, Navigating a Coursera Course, Meet the Instructor (optional), Lesson Introduction, Installing GameMaker, Sprites and Sprite Strips, Paint.NET
Lesson Introduction, Objects and Instances, Visual Events and Actions, Code Events and Actions
Lesson Introduction, Blowing Stuff Up, Sound Effects, Audacity, Lesson Introduction, Built-In Variables, NPC Movement, Screen Wrapping
Lesson Introduction, Object Randomness, Rotation, Game-Level Randomness, Finishing Up
Course: 有机化学
Modules:
欢迎, 有机化合物的早期定义, 有机化学与有机化合物, 有机化学与其他学科的关系, 现代价键理论基本要点, 杂化轨道理论要点, 共价键的类型和参数, 分子的极性, 有机物的分类与结构, 有机反应类型, 确定有机物结构的步骤与方法
烷烃的普通命名, 烷烃的系统命名, 环烷烃的命名, 烷烃的构象, 环烷烃的稳定性, 环烷烃的立体结构, 烷烃的化学性质, 环烷烃的化学性质
烯烃的结构与构型异构, 烯烃的命名, 烯烃的亲电加成反应, 诱导效应, 马氏规则的解释, 过氧化物效应, 烯烃的氧化反应, 烯烃α-氢的反应, 炔烃、二烯烃的命名, 炔烃的结构, 二烯烃的结构, 共轭效应, 超共轭效应, 炔烃的化学性质, 二烯烃的化学性质
吸收光谱概述, 如何实现核磁共振？, 化学位移, 核磁氢谱的主要参数, 自旋耦合与自旋裂分, 核磁氢谱裂分规律, 氢谱解析示例, 碳谱简介, 影响化学位移及耦合常数的因素, 红外光谱概述, 红外谱图区段和特征峰, 红外谱图解析示例, 紫外吸收光谱概述, 紫外吸收谱带及应用, 质谱简介
芳香烃的分类和命名, 苯的结构, 苯的亲电取代反应机理, 苯的几类亲电取代反应, 苯环侧链的反应, 单取代苯亲电取代反应的定位效应, 二取代苯亲电取代反应的定位效应, 萘的结构和性质, 致癌稠环芳烃, 芳香性判断, 轮烯和足球烯
同分异构体的分类, 平面偏振光和比旋光度, 对映异构与手性分子, 对称因素与手性, 含有一个手性碳的化合物, 光学异构体构型标记法, 含两个手性中心的化合物, 无手性中心的手性化合物
卤代烃的分类和命名, 卤代烃的物理性质, 卤代烃单分子亲核取代反应(SN1), 卤代烃双分子亲核取代反应(SN2), 影响亲核取代反应的因素, 单分子消除反应(E1), 双分子消除反应(E2), 卤代烯烃和卤代芳烃, 格氏试剂
醇、酚、醚简介, 醇的结构和命名, 醇的物理性质, 醇的酸性, 醇的取代反应, 醇的脱水及酯化反应, 醇的氧化, 醇的鉴别、多元醇, 酚的结构、命名和物理性质, 酚的化学性质, 酚的鉴别, 醚的结构、命名和物理性质, 醚的质子化和醚键断裂, 过氧化物生成及环氧开环, 冠醚
醛和酮的结构和分类, 醛和酮命名, 醛酮的物理性质, 醛酮的亲核加成反应机理, 醛酮与氢氰酸、格氏试剂、亚硫酸氢钠的反应, 醛酮与含氧亲核试剂加成, 醛酮与胺衍生物的加成, α,β-不饱和醛酮的加成反应, 羰基加成的立体化学, 羰基的还原反应, α-H的卤代反应, 醇醛缩合反应, 醛的特殊反应, 醌
羧酸的分类, 羧酸的命名和结构, 羧酸的酸性, 酰氯和酸酐的生成, 酯和酰胺的生成, 羧酸的脱羧、还原、α-H卤代, 二元羧酸和甲酸的特殊反应, 羟基酸结构、命名和物理性质, 羟基酸的化学性质, 羰基酸, 酮与烯醇的互变, 羧酸衍生物的命名, 羧酸衍生物的水解、醇解、氨解反应, 羧酸衍生物亲核取代反应机理, Claisen酯缩合反应, 羧酸衍生物还原反应、酰胺的特性
硝基化合物, 胺的分类, 胺的命名, 胺的结构, 胺的碱性, 胺的酰化反应, 胺与亚硝酸的反应, 芳香胺的亲电取代, 芳香重氮盐的取代反应, 芳香重氮盐偶联反应, 五、六元杂环化合物命名, 稠杂环化合物命名, 杂环化合物的结构, 杂环化合物的酸碱性, 杂环化合物的亲电取代、亲核取代、氧化还原反应, 杂环、稠杂环化合物其它性质
Course: Prototyping
Modules:
Instructor Bio
Course Introduction
Types of Physical Prototyping Methods
3D Printing Technology – Polymer 1, 3D Printing Technology – Polymer 2, 3D Printing Technology – Polymer 3
Course: Introduction to Game Design
Modules:
Genres of games, Action and adventure, Shooters, Role Playing Games, Platformers, How to find and cultivate 'fun', Roles in a Development Studio, Career paths, Art Vs Design, Specialities, Indie Vs AAA Studio, Different Roles in Game Design, Responsibilities, Skillset
Where do ideas come from?, Brainstorming, Pillars of game design, Market research, Audience research, Technical research, Identifying scope, What is the aim of balancing a game?, How to be fair to the player?, How do we keep players engaged?, How do we entertain players?, What is a design methodology, Waterfall methodology, Agile methodology, Double diamond, What is a work pipeline?, Identifying target market, SWOT analysis, Pitch decks, Presenting a pitch deck
Iteration cycle, Working in a team, Source control, Project roadmaps, Prototyping
What is a game design document, How are game design documents used, Keeping organized, Reviewing an example
Course: 공급망금융 시장 및 핀테크 생태계
Modules:
전문 과정 소개, 강사 소개, 강좌 개요 및 목표, 주제 01: 잠재 및 현재 시장 추정치, 주제 02: 시장 침투, 성장 및 분포, 주제 03: 요약 잠재 및 현재 시장 규모, 주제04: 지역 안배, 주제 5: 지리적 시장 특성, 주제 06: 등급 및 산업별 잠재적 미지급금, 주제 07: 공급망 금융에 대한 설문 조사, Q&A: 공급망 금융에는 어떤 일자리가 있나요?
주제 01: 공급망 금융 생태계, 주제 02: 누가 시장을 이끌까요?, 주제 03: 서비스 및 기술 제공자, 주제 04: 자금 제공자, 주제 05: 자금 출처의 진화, 주제 06: 조력자, 주제 07: 시장 역학, 주제 01: 시장에 영향을 미치는 트렌드, 주제 02: 시장 리스크, Q&A: 더 많은 구매자 기업이 공급망 금융을 채택하지 않는 이유는 무엇입니까?
Course: Building Scalable Applications with .NET Core
Modules:
Course Introduction: Introduction to Scalability, Understanding Scalability in Software Applications, Scalability Patterns and Why They Matter, Horizontal Scaling Concepts and Benefits, Vertical Scaling and Its Limitations, Strategies for Combining Horizontal and Vertical Scaling, Common Scalability Bottlenecks in .NET Core, Introduction to Microservices Architecture Part 1, Introduction to Microservices Architecture Part 2, Implementing Microservices with .NET Core, Building Resilient and Fault-Tolerant Systems
Understanding Load Balancing in .NET Core, Implementing Load Balancers Demo Part 1, Implementing Load Balancers Demo Part 2, Implementing Load Balancers Demo Part 3, Implementing Load Balancers Demo Part 4, Introduction to High Availability (HA), Configuring Load Balancing for Web Applications, Strategies for Disaster Recovery and Backup
Introduction to Caching and Its Benefits, Implementing Caching in .NET Core Applications Part 1, Implementing Caching in .NET Core Applications Part 2, Asynchronous Programming for Improved Performance, Efficient Data Access and Database Optimization, Database Sharding and Partitioning, NoSQL Databases for Scalable Data Storage, Load Testing Strategies and Tools, Analyzing and Interpreting Load Test Results Part 1, Analyzing and Interpreting Load Test Results Part 2
Leveraging Cloud Infrastructure for Scalable Applications, Cloud Deployment for .NET Application Part 1, Cloud Deployment for .NET Application Part 2, Implementing Autoscaling Policies, Importance of Monitoring and Observability, Analyzing Application Metrics and Logs, Conclusion Course: Building Scalable Applications with .NET Core
Course: SAP Customer Engagement and Discovery
Modules:
Course & Instructor Introduction, Introduction to SAP ACTIVATE Methodology, SAP ACTIVATE Methodology Overview, Program and Customer Engagement, Program Planning and Stakeholder Analysis, Program Set-Up and Kickoff, Gathering Customer Insights and Information, Determining Analysis Approach and Preparation Activities, Identifying and Communicating Business Value
Introduction to Module 2, How are metrics and models used in analysis?, How are metrics and models used to validate?, How to Gather Business and Technical Inputs, Facilitating a Customer Discovery Session, Refining and Aligning Business Objectives
Introduction to Module 3, Understanding the Fit-to-Standard Analysis Approach, Other Analysis Frameworks and Tools, Set-Up and Preparation Activities, Review Systems and Application Architecture, Review Business Process and Functional Architecture, Review Data Landscape
Introduction to Module 4, Documenting Discovery and Validated Business Objectives, Customer Journey Roadmap Planning, Identifying and Communicating Business Value, Guiding and Providing Technical Expertise, Presenting the Customer Journey Roadmap, Incorporating Customer Feedback and Seeking Phase Signoff
Introduction to Module 5, Project Overview: Create a Customer Journey Map Plan to Prepare for Analysis and Design Phase
Course: Introduction to Data Engineering on Google Cloud
Modules:
Course Introduction
Module Introduction, The Role of a Data Engineer, Data Sources Versus Data Sinks, Data Formats, Storage Solution Options on Google Cloud, Metadata Management Options on Google Cloud, Sharing Datasets using Analytics Hub, Lab Intro: Loading Data into BigQuery, Coursera: Getting Started with Google Cloud and Qwiklabs
Module Introduction, Replication and Migration Architecture, The gcloud Command Line Tool, Moving Datasets, Datastream, Lab Intro: Datastream: PostgreSQL Replication to BigQuery
Module Introduction, Extract and Load Architecture, The bq Command Line Tool, BigQuery Data Transfer Service, BigLake, Lab Intro: BigLake: Qwik Start
Module Introduction, Extract, Load, and Transform (ELT) Architecture, SQL Scripting and Scheduling with BigQuery, Dataform, Lab Intro: Create and Execute a SQL Workflow in Dataform
Module Introduction, Extract, Transform, and Load (ETL) Architecture, Google Cloud GUI Tools for ETL Data Pipelines, Batch Data Processing Using Dataproc, Lab Intro: Use Dataproc Serverless for Spark to Load BigQuery, Streaming Data Processing Options, Bigtable and Data Pipelines, Lab Intro: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
Module Introduction, Automation Patterns and Options for Pipelines, Cloud Scheduler and Workflows, Cloud Composer, Cloud Run Functions, Eventarc, Lab Intro: Use Cloud Run Functions to Load BigQuery
Course Summary
Course: Liderazgo
Modules:
Conoce a la profesora Elizabeth Luckman, Introducción al curso, Conoce al profesor Michael Bednar, Conoce al profesor Geoffrey Love, Introducción a la toma de decisiones en las organizaciones, Toma de decisiones racional, de racionalidad acotada e intuitiva, Sesgos en la toma de decisiones humanas, La teoría de las perspectivas y cómo las pérdidas son mayores que las ganancias, Trampas en la toma de decisiones éticas, Racionalización, Pruebas de toma de decisiones éticas
¿Qué es un proyecto?, Enfoque en cascada de la gestión de proyectos, Inicio de un proyecto, Conoce a nuestro experto: Harshad Desai, Información de un experto sobre el enfoque en cascada, Gestión de las partes interesadas, Conoce a nuestro experto: Garrett Whitsitt, Información de un experto sobre la gestión de las partes interesadas, Alcance del proyecto, Ejemplo de alcance, Prioridades del proyecto, Información de un experto sobre las prioridades del proyecto, Estructura de desglose de trabajo, Información de un experto sobre la importancia de la planificación, Cómo utilizar notas adhesivas para crear una estructura de desglose de trabajo, Introducción a Project Libre (Mac), Introducción a Project Libre (PC), Creación de una estructura de desglose de trabajo con ProjectLibre, Estructura organizativa y del equipo, Conoce a nuestros expertos: Sean Bauer y Andrew Howard, La visión de un experto sobre la estructuración del equipo del proyecto
Definición de liderazgo para el mundo actual, Autoconciencia en el liderazgo, El líder emocionalmente inteligente, Toma de decisiones eficaces, Toma de decisiones éticas, Comunicación para el desarrollo de la confianza, Motivarse y motivar a los demás, Crecer como líder, Conclusión del curso
Course: Critical thinking for sustainable business solutions
Modules:
Welcome to the course, Course requirements, Engaging with your tasks, Introducing ideation for systemic challenges, What is ideation?, Divergent and convergent thinking modes, Ideation rounds illustrated, Week 1 summary
Introduction to Week 2, Rules and techniques for effective ideation, Using hybrid brainstorming, Ideation rounds, Your task: Ideation technique 1 – Pre-mortem, Your task: Ideation technique 2 – Worst idea technique, Your task: Ideation technique 3 – Round robin, Your task: Ideation technique 4 – Reverse brainstorming, Your task: Ideation technique 5 – Creative opposites, Your task: Ideation technique 6 – Sustainability ideation cards, Week 2 summary
Welcome to Week 3, Converging on final concepts, Creating a sustainability concept card, Your task: Understanding sustainability concept consequences, Week 3 summary
Welcome to Week 4, Concept refinement technique – the SCAMPER method, Your likely concept impacts, Conclusion and next steps
Course: 머신 러닝 기초: 사례 연구 접근 방식
Modules:
이 과정 및 전문화 과정에 오신 것을 환영합니다, 우리는 누구인가, 머신 러닝이 세상을 바꾸고 있다, 왜 사례 연구 접근 방식인가?, 전문화 개요, ML에 입문한 방법, 이 전문화는 누구를 위한 것인가?, 여러분이 할 수 있는 일, 캡스톤과 지능형 애플리케이션의 예, 지능형 애플리케이션의 미래, Jupyter Notebook 시작, Python에서 변수 만들기, Python의 조건문 및 루프, Python에서 함수 및 람다 만들기, Turi Create 시작 및 SFrame 로드, 데이터 시각화를 위한 캔버스, SFrame의 열과 상호 작용하기, 데이터 변환을 위한 .apply() 사용
주택 가격 예측: 회귀 사례 연구, 목표는 무엇이고 어떻게 단순하게 해결할 수 있는가?, 선형 회귀: 모델 기반 접근 방식, 고차 효과 추가, 학습/테스트 분할을 통한 과적합 평가, 학습/테스트 곡선, 다른 특성 추가, 기타 회귀 예제, 회귀 ML 블록 다이어그램, 주택 매매 데이터 로드 및 탐색, 데이터를 학습 세트와 테스트 세트로 분할하기, 집 크기로 주택 가격을 예측하는 단순 회귀 모델 학습하기, 단순한 모델의 오차(RMSE) 평가, Matplotlib으로 단순한 모델의 예측 시각화, 학습된 모델 계수 검사, 데이터의 다른 특성 탐색, 더 많은 특성으로 주택 가격을 예측하는 모델 학습하기, 학습된 모델을 적용하여 평균 주택 가격 예측하기, 학습된 모델을 적용하여 두 채의 멋진 주택 가격 예측하기
리뷰 감정 분석: 분류 사례 연구, 지능형 음식점 리뷰 시스템이란?, 분류 작업의 예, 선형 분류기, 결정 경계, 분류기 학습 및 평가, 좋은 정확도란 무엇인가?, 거짓 긍정, 거짓 부정 및 혼동 행렬, 학습 곡선, 클래스 확률, 분류 ML 블록 다이어그램, 제품 리뷰 데이터 로드 및 탐색, 단어 수 벡터 만들기, 가장 인기있는 제품 탐색, 긍정적이거나 부정적인 감정이 있는 리뷰 정의, 감정 분류기 학습, 분류기 및 ROC 곡선 평가, 모델을 적용하여 제품에 대한 가장 긍정적이고 부정적인 리뷰 찾기, 제품의 가장 긍정적인 측면과 부정적인 측면 탐색
문서 검색: 클러스터링 및 유사성 측정 사례 연구, 문서 검색 작업은 무엇인가?, 유사성 측정을 위한 단어 수 표현, tf-idf로 중요한 단어 우선 순위 지정, tf-idf 벡터 계산, 최근접 이웃 검색을 사용하여 유사한 문서 검색, 문서 클러스터링 작업 개요, 문서 클러스터링 비지도 학습 작업, k-평균: 클러스터링 알고리즘, 클러스터링의 다른 예, 클러스터링 및 유사성 ML 블록 다이어그램, Wikipedia 데이터 로드 및 탐색, 단어 수 살펴보기, TF-IDF 계산 및 탐색, Wikipedia 기사 간의 거리 계산, Wikipedia 기사에 대한 최근접 이웃 모델 구축 및 탐색, 실제 문서 검색의 예
추천 시스템 개요, 추천 시스템이 작동하는 분야, 분류를 통한 추천 시스템 구축, 협업 필터링: 다른 고객이 함께 구매한 상품..., 인기 상품의 효과, 동시 발생 행렬 정규화 및 구매 내역 활용, 행렬 완성 작업, 알려진 사용자/항목 특성의 추천, 행렬 형태의 예측, 행렬 분해를 통한 숨겨진 구조 발견, 모든 것을 통합: 특성별 행렬 분해, 추천 시스템에 대한 성능 측정항목, 최적의 추천 시스템, 정밀도-재현율 곡선, 추천 시스템 ML 블록 다이어그램, 노래 데이터 로드 및 탐색, 인기 기반 노래 추천 생성 및 평가, 개인화된 노래 추천 생성 및 평가, 추천 모델을 비교하기 위한 정밀도-재현율 사용
이미지 검색: 딥 러닝 사례 연구, 비주얼 제품 추천이란 무엇인가?, 신경망을 사용한 매우 비선형적인 특성 학습, 컴퓨터 비전에 딥 러닝 적용, 딥 러닝 성능, ImageNet 데이터에 대한 딥 러닝 모델 데모, 컴퓨터 비전에서 딥 러닝의 다른 예, 딥 러닝의 과제, 심층 특성, 딥 러닝 ML 블록 다이어그램, 이미지 데이터 로드, 원시 이미지 픽셀을 사용하여 분류기 학습 및 평가, 심층 특성을 사용하여 분류기 학습 및 평가, 이미지 데이터 로드, 이미지 검색을 위한 최근접 이웃 모델 만들기, 최근접 이웃 모델을 쿼리하여 이미지 검색, 자동차 이미지에 대해 가장 유사한 이미지 쿼리, Python 람다를 사용하여 다른 예제 이미지 검색 표시
다 왔습니다!, ML 서비스 배포, 배포 후에는 어떻게 되는가?, ML의 미결 과제, ML은 어디로 가고 있는가?, 앞으로의 전문화 과정, 감사합니다!
Course: Prospecting and Selling Insurance
Modules:
Prospecting and Selling Insurance Course Introduction, Overview of the Prospecting Model, Use Market Segmentation to Define Your Target Market, How To Fill Your Prospecting Pipeline, Developing Centers of Influence, Start Where You Go to Generate Leads, Cold Calling Techniques, Using Social Media for Prospecting, Define Your Follow-Up Process, Strategies to Stay Top of Mind, Monitor and Refine Your Prospecting Process, How to Analyze Your Successes and Failures, Conquering Prospecting Fears, Establishing Your Prospecting Process, Developing a Strategy to Manage Prospects
New Agent Sales Tips: How do you set yourself apart from other agents?, The Importance of the Sales Process, Building Rapport to Develop Relationships, New Agent Sales Tips: How do you confidently ask for the sale?, Closing Techniques in Action
Activity: Overcoming Objections, Introduction to Building Relationships, New Agent Sales Tips: Connect Outside the Office
Introduction to Client Retention, Introduction to Client Referrals, Referrals Through Client Satisfaction, Referrals Through Networking, Asking for Referrals in Action, Introduction to Upselling, Upselling Techniques for Personal Insurance, Upselling Techniques for Commercial Insurance, Introduction to Cross-Selling, Uncovering Cross-Sell Opportunities, Leveraging Technology for Prospecting and Selling Insurance
Course: Accounting for Business Decision Making: Measurement and Operational Decisions
Modules:
Syllabus, About the Discussion Forums, Updating Your Profile
1-1.1. Overview of Business Decisions, 1-1.2. Application and Extension, 1-1.3. What We’ve Learned, 1-2.1. Overview of Managerial Accounting, 1-2.2. Decision-Making Roles of Managerial Accounting, 1-2.3. Application and Extension, 1-2.4. What We’ve Learned
2-1.1. Defining Costs, 2-1.2. Application and Extension, 2-1.3. What We’ve Learned, 2-2.1. Importance of Organizing Cost Information, 2-2.2. Organizing Costs by Product, 2-2.3. Further Refinements, 2-2.4. Organizing Costs by Behavior, 2-2.5. Application and Extension, 2-2.6. What We’ve Learned
3-1.1. Measuring Profitability According to Cost Behavior, 3-1.2. Defining and Using Cost-Volume-Profit Analysis, 3-1.3. Application and Extension, 3-1.4. What We’ve Learned, 3-2.1. Interpreting CVP Analysis, 3-2.2. Limitations of CVP Analysis, 3-2.3. Application and Extension, 3-2.4. What We’ve Learned
4-1.1. Identify Common Operations-Based Decisions, 4-1.2. Creating and Using Relevant Information, 4-1.3. Application – Make or Buy?, 4-1.4. Application – Manage Product Lines, 4-1.5. What We’ve Learned, 4-2.1. Identify Common Sales Decisions, 4-2.2. Application – Special Orders, 4-2.3. Application – Sell Now, or Process Further?, 4-2.4. What We’ve Learned, Learn on Your Terms
Course: Responsible Conduct of Research
Modules:
1.1.1 Welcome to the course!, 1.2.1 Empowerment
2.1.1 To publish or not to publish, 2.1.3 Sharing experiences, 2.2.1 Academic Quality
3.1.1 Standing on the shoulders of giants?, 3.1.3 Sharing experiences, 3.2.1 Authorship
4.1.1 Never waste a good talent, 4.1.3 Sharing experiences, 4.2.1 Social safety
5.1.1 Sour and sweet tastes, 5.1.3 Sharing experiences, 5.2.1 Property and sharing data
6.1.1 Course summary, 6.2.2 Closing message
Course: Exploratory Data Analysis
Modules:
Overview of the short course, Why data type is important, Data types in real examples, Why data visualisation is important, Using RStudio for graphs
Why missing data and outliers matter in data analysis, Tutor feedback for 'Use RStudio to produce graphs'
Tutor feedback for 'Make graphical summaries within RStudio', Course complete - do you want to take your studies further?