跳到主要內容
AI 武林
影片入門EN7.7 萬 次觀看

Python for Engineers & Robotics – Master NumPy, Pandas, and ChatGPT Automation

來源　freeCodeCamp.org

看影片（在新分頁開啟原站）連到 freeCodeCamp.org

摘要

這門課程從零開始教導工程師如何用 Python 進行機械工程與機器人學，涵蓋變數、控制流程與函式等基礎。學員將學習使用 NumPy、Pandas 和 Matplotlib 進行數值計算、資料處理與繪圖，並透過 ChatGPT 自動化工作流。完成後能掌握 Python 程式設計，提升工程分析效率。

A comprehensive course teaching Python programming from scratch for mechanical engineers, covering libraries like NumPy and Pandas with ChatGPT automation.

摘要、重點與章節標題由語言模型整理，細節（誰說的、數字、先後）可能有誤；要引用請以原始內容為準。

重點

  • 從零開始學習 Python 基礎語法與工程應用。
  • 掌握 NumPy、Pandas 與 Matplotlib 進行資料分析。
  • 利用 ChatGPT 自動化程式開發與資料處理流程。

章節

依話題轉折切分，標題由 AI 產生

  1. 00:00Course Overview & Python Basics
  2. 01:14Introduction to Python for Mechanical Engineers
  3. 02:43Important Features & Execution of Python
  4. 05:12Significance of Python in Mechanical Engineering
  5. 06:30Top Applications: Data Analysis, CFD, & Robotics
  6. 10:44Setting Up Python & VS Code on Windows
  7. 13:38Running Your First Python Program
  8. 18:53Interactive Shell (REPL) vs. Python Scripts
  9. 25:44Single-Line & Multi-Line Comments in Python
  10. 31:48Understanding Variables & Naming Rules
  11. 35:33Variable Assignment Methods & Data Types
  12. 43:00Python Literals Explained
  13. 46:50Implicit & Explicit Type Conversion
  14. 55:33Basic Input & Output (Print Formatting)
  15. 1:05:46User Input & Split Method
  16. 1:12:46Arithmetic & Logical Operators
  17. 1:20:11Comparison, Assignment, & Identity Operators
  18. 1:30:30Operator Precedence Rules & Examples
  19. 1:35:28Using ChatGPT to Learn Python
  20. 1:41:11Control Flow: Conditional Statements (if/elif/else)
  21. 1:51:14Engineering Practical Examples for Conditional Logic
  22. 2:04:42Loops: For Loops & The `range()` Function
  23. 2:16:53Mechanical Engineering Applications Using For Loops
  24. 2:22:48While Loops & Simulating Dynamic Processes
  25. 2:32:52Loop Control Statements: `break` & `continue`
  26. 2:37:58Nested Loops
  27. 2:42:48Mechanical Engineering Case Studies with Loops
  28. 2:52:58ChatGPT Prompts for Loops & Conditionals
  29. 2:57:48Functions & Code Reusability
  30. 3:04:54Function Arguments & Return Values
  31. 3:11:59Arbitrary Positional (`*args`) & Keyword (`**kwargs`) Arguments
  32. 3:18:56Understanding Variable Scope & LEGB Rule
  33. 3:24:10Working with Global Variables
  34. 3:28:19Introduction to Python Modules
  35. 3:33:35Useful Built-in Modules for Engineering
  36. 3:38:19Creating & Importing User-Defined Modules
  37. 3:41:51Designing Functions with ChatGPT
  38. 3:47:00Introduction to NumPy & Installation
  39. 3:53:30Methods for Creating NumPy Arrays
  40. 3:59:41Creating Multi-Dimensional (`ND`) Arrays
  41. 4:07:48NumPy Data Types & Type Conversion
  42. 4:13:32Essential NumPy Array Attributes
  43. 4:18:14NumPy Array Indexing (1D, 2D, 3D)
  44. 4:27:38Slicing & Reversing NumPy Arrays
  45. 4:36:46Element-Wise Arithmetic Operations
  46. 4:41:12Mathematical & Statistical Array Functions
  47. 4:47:40String Operations in NumPy
  48. 4:53:30Trigonometric Functions & Angle Conversions
  49. 4:58:45Matrix Operations: Multiplication, Transpose, Inverse, & Reshape
  50. 5:03:44Solving Mechanical Engineering Problems with NumPy
  51. 5:12:40Troubleshooting NumPy Code with ChatGPT
  52. 5:17:06Introduction to Pandas & Installation
  53. 5:20:16Working with Pandas Series
  54. 5:27:56Creating & Managing Pandas DataFrames
  55. 5:35:50Default, Custom, & Range Indexing
  56. 5:40:38Exploring Data: `head()`, `tail()`, & `info()`
  57. 5:45:07Modifying DataFrames: Adding, Dropping, & Renaming
  58. 5:52:11Advanced Selection & Slicing: `.loc` vs. `.iloc`
  59. 6:05:50Multi-Indexing & Removing Duplicates
  60. 6:15:52Reading & Writing Excel and CSV Files
  61. 6:25:58Pivoting & Creating Pivot Tables
  62. 6:34:50Real-World Case Study: Aircraft Material Data Analysis
  63. 6:44:15Using ChatGPT for Pandas Data Cleaning & Analysis

提到的工具與公司

  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • ChatGPT
  • VS Code

適合誰看

機械工程、機器人學領域的學生或專業人員。

摘要依據

依據
自動字幕

為什麼排在這裡

人氣
0.85
新鮮
0.82

在主題頁與搜尋結果裡，名次由相關、人氣、新鮮三個分數決定；這一頁沒有搜尋的關鍵字，所以沒有相關分數。排序怎麼算

相關內容

摘要由 AI 根據原文產生，可能有誤；完整內容請看原站。看影片（在新分頁開啟原站）