Final Review – Python for AI/ML
Congratulations! You’ve completed the Python foundation for AI/ML.
Recap of key concepts:
- Variables, data types (int, float, str, bool)
- Operators (arithmetic, comparison, logical)
- Strings & methods (.upper, .split, etc.)
- Lists & methods (.append, .sort, etc.)
- Loops (for, while)
- Conditionals (if/elif/else)
- Functions (def, return)
- Dictionaries & sets (key-value data)
- File I/O (read/write CSV)
- Modules & imports (math, random, numpy)
- NumPy basics (arrays, vector ops)
- Pandas intro (DataFrames, filtering, grouping)
- First ML project: Iris classification
You’re now ready for real data science and machine learning!
Final Advice & Next Steps
You now have the Python skills to:
- Manipulate data (Pandas, NumPy)
- Write clean, reusable code (functions, loops)
- Build simple ML models
Practice daily — try Kaggle datasets or small projects.
Next modules:
- Data Visualization & Statistics
- Supervised Learning (Regression & Classification)
- Unsupervised Learning & Projects
Thank you for completing Module 1! You’re ready for real machine learning.
Keep coding — you’ve got this!