I learned Python and ML a while ago, and I'm now looking to refresh my knowledge. Can I get any suggestions for an effective way or resource for a quick and structured revision?

[goku-kakarot1997]

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I learned Python and ML a while ago, and I'm now looking to refresh my knowledge. Can I get some suggestions for an effective way or resource for a quick and structured revision?

[MaSTERmIKK]

Hey @goku-kakarot1997 i repost for u a roadmap i create for my class! Great that you’re jumping back in. Here’s a quick, structured way to refresh your Python + ML knowledge:

🧩 1. Quick Python Refresher
✅ realpython.com – concise and well-explained topics

✅ Python Morsels – great for intermediate practice

✅ Try building a mini CLI or web scraper to reactivate your syntax & logic

🤖 2. Machine Learning Refresher
✅ Google’s Machine Learning Crash Course

✅ fast.ai's Practical Deep Learning – perfect if you're rusty but want to go hands-on fast

✅ Use Kaggle notebooks to revisit Scikit-learn, XGBoost, etc. – free datasets + community

🗂️ 3. Suggested Roadmap
Python basics: list comps, dicts, classes, decorators

NumPy & Pandas: slicing, grouping, joins

Scikit-learn: pipelines, model selection, metrics

TensorFlow or PyTorch: basic models and training loop

Mini-project: Try replicating a simple Kaggle kernel or a blog post

⚙️ Bonus Tools
JupyterLab or Google Colab to get coding right away

Excalidraw + Markdown cheatsheets for reference

I hope this help u.

[goku-kakarot1997]

Thanks @MaSTERmIKK

[BalajiKudumu]

Hi @goku-kakarot1997 , sure! Since you’ve already learned Python and machine learning before, here’s a structured and efficient way to quickly refresh your knowledge:

Start with Practice Questions:
Go through 150+ Python for Data Science questions I’ve compiled — it’s a great hands-on way to revisit core concepts.
https://github.com/BalajiKudumu/Python-for-Datascience

Revise Key Libraries:
Check my GitHub repos covering:
NumPy & Pandas – for data manipulation
https://github.com/BalajiKudumu/Numpy-Pandas

Matplotlib, Seaborn, Plotly, Cufflinks – for data visualization
https://github.com/BalajiKudumu/Data-Visualization-using-python-seaborn-plotly-cufflinks

Scikit-learn – for machine learning fundamentals
https://github.com/BalajiKudumu/Machine-Learning-Algorithms-Supervised-and-unsupervised
https://github.com/BalajiKudumu/Hand-written-digits-recognizing

Explore End-to-End Projects:
Apply concepts in small end-to-end ML projects available in my repos — it helps tie everything together.
https://github.com/BalajiKudumu/Mileage_app-django
https://github.com/BalajiKudumu/GRU-Neural-Networks
https://github.com/BalajiKudumu/Heart-disease-app-Flask
https://github.com/BalajiKudumu/Recurent-Neural-Network-Sentimental_Analysis

Refer my GitHub repos on LLM's and Deep Learning.
Use Jupyter Notebooks or Google Colab for Active Learning:

Let me know if you’d like links to specific topics or projects — happy to share!*

[goku-kakarot1997]

Thanks @BalajiKudumu