Wang
Github Repo

AI Sprint Week 1 / Day 1: Environment Setup & Data Processing

Week 1 / Day 1 Learning Summary

Learning Date

August 11, 2025

Today's Goals

Complete AI Sprint project environment configuration, master basic data processing and visualization skills

Completed Tasks

1. Project Environment Setup

  • GitHub Repository Creation: Successfully created private repository ai-sprint
  • Project Structure: Completed 4-week learning plan directory structure
  • Environment Configuration: Python virtual environment + dependency package installation
  • Documentation Internationalization: All README and code comments converted to English for professional presentation

2. Technical Skills Mastered

  • Jupyter Notebook Usage: Successfully started and ran code
  • Python Library Import: numpy, pandas, matplotlib, seaborn, sklearn
  • Data Processing Basics: Complete workflow of data loading, transformation, and saving
  • Data Visualization Introduction: Bar chart creation and chart beautification

3. Dataset Exploration

  • Wine Dataset: Red wine quality classification task (3-class classification)
  • Data Scale: 178 samples, 13 features
  • Data Storage: CSV format local storage, ensuring project reproducibility

Core Technical Points

  • Data Processing Pipeline
  • Data Visualization

Learning Outcomes

| Skill Area | Status | Details | |------------|--------|---------| | Environment Configuration | Complete | Fully functional | | Data Loading | Complete | Successfully implemented | | Data Exploration | Basic Complete | Fundamental understanding | | Data Storage | Complete | Local storage implemented | | Basic Visualization | Complete | Bar chart display |

Skills Acquired

  • Project Setup Capability: Building complete AI learning projects from scratch
  • Environment Management: Virtual environment configuration and dependency management
  • Data Processing Capability: Complete workflow of data loading, transformation, and storage
  • Visualization Capability: Basic chart creation and beautification
  • Version Control Capability: Basic usage of Git and GitHub

Project Value

  • Showcaseable: Fully internationalized, suitable for international presentation
  • Reproducible: Complete environment configuration and data storage
  • Professional: Standard data science workflow
  • Learning Path: Clear 4-week progressive plan

Tomorrow's Plan

  • Deep Data Analysis: Feature correlation heatmap
  • Data Preprocessing: Standardization and normalization
  • Algorithm Implementation: Begin Logistic Regression from scratch

Learning Insights

Today successfully completed the leap from zero to one, establishing a complete AI learning environment. Through hands-on practice, I gained deep understanding of the fundamental data science workflow, laying a solid foundation for subsequent machine learning algorithm studies. Each step has clear learning objectives and technical gains, and this progressive learning approach is very suitable for AI beginners.

Related Resources

  • Project Repository: https://github.com/uoftWANGTIANMING/ai-sprint
  • Learning Plan: 4-week AI On-Ramp complete roadmap
  • Tech Stack: Python + Jupyter + mainstream data science libraries
  • Today's Learning Time: Approximately 3 hours
  • Learning Status: Complete
  • Tomorrow's Preparation: Continue data exploration and algorithm implementation

Progress Tracker

  • Week 1 Progress: 1/5 days completed (20%)
  • Overall Sprint Progress: 1/20 days completed (5%)
  • Next Milestone: Complete Week 1 data processing and basic ML implementation

This learning log is part of the 4-Week AI Sprint series. Each day builds upon the previous, creating a comprehensive AI learning journey.