A.I. Plan in Detail

owens.rustyon

Month 1-2: Python Foundations (8 weeks, ~5 hrs/wk)

Focus here to bridge from C#; expect 1-2 hrs/day, 3-4 days/week.

  • Weeks 1-2: Setup + Syntax (DeepLearning.AI “AI Python for Beginners”)
    • Install Python/Anaconda/VS Code (with Python extension—feels like Visual Studio).
    • Learn variables, functions, loops, conditionals via hands-on: build a simple recipe generator or to-do list using AI chat for feedback.
    • Why: Compares directly to C#; you’ll see lists like arrays, dicts like dictionaries.
  • Weeks 3-4: Data Structures + Files
    • Lists/tuples/dicts/sets; file I/O; basic error handling.
    • Project: Simple notepad app—read/write text files, like a mini database CLI.
    • Resources: “Python for C# Developers” videos (free YouTube series) for direct comparisons.​​
  • Weeks 5-6: Functions + Modules
    • Functions, lambdas, modules (import math/random); intro to pandas (DataFrames feel like SQL tables).
    • Project: Weather CLI app—fetch API data, parse JSON, display like a query result.​
  • Weeks 7-8: OOP + Libraries
    • Classes/inheritance (Python’s more flexible than C#); NumPy for arrays/math.
    • Project: Basic class-based data analyzer (load CSV, compute stats).
    • Milestone: Rewrite a simple C# console app (e.g., inventory manager) in Python.

Month 3-4: Classical ML Basics (8 weeks)

Now apply Python to data/ML; use scikit-learn (simple API).

  • EDA + Linear Regression (Titanic dataset): Clean data, visualize (matplotlib like charts), predict survival.
  • Classification (Iris/churn): Logistic regression, decision trees; cross-validation.
  • Project: Housing price predictor—leverage your DB skills for feature engineering.

Month 5-7: Deep Learning Intro (12 weeks)

Shift to PyTorch (easier for beginners than TensorFlow).

  • MNIST digits classifier (from scratch then framework).
  • Simple CNN on CIFAR-10 images.
  • Project: Custom image classifier (e.g., car parts—ties to your auto interest).

Month 8-9: NLP + Generative (8 weeks)

  • Sentiment analysis; basic chatbot with Hugging Face transformers.
  • Project: Bible Q&A tool (fine-tune small LLM on texts—fits your biblical studies).

Month 10-12: Advanced + Deploy (12 weeks, capstone)

  • RL basics (CartPole agent).
  • Simple RAG QA system.
  • Capstone: Deploy a full pipeline (e.g., automotive troubleshooting app) as a web API via Streamlit/Flask—containerize like Docker basics.
  • MLOps intro: GitHub repo, basic CI.

Weekly Rhythm + Tips

  • 4-6 hrs: 2-3 hrs tutorials/videos + 2-3 hrs coding/project.
  • Track in a Jupyter notebook (like interactive SQL queries).
  • Use your DB strength: Pandas/SQLite early for comfort.
  • Free resources: DeepLearning.AI (AI-assisted), freeCodeCamp Python, fast.ai for ML.
  • Stuck? ChatGPT/Copilot with “explain like C#”