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🧠 How I’d Learn ML from Scratch in 2025 (Without Getting Overwhelmed)

Machine Learning sounds scary in 2025. Too many courses. Too much math. Too many “build this billion-dollar AI” videos.Here’s how I’d start from scratch—and actually stick with it.👇

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MLguy
June 21, 2025 • Estimated Reading Time: 2 minutes

⚙️ Step 1: Learn Just Enough Python

Don’t spend months becoming a Python master.

Just learn:

  • Variables, functions, lists, loops

  • NumPy and pandas

Use: Python for Data Science – freeCodeCamp (YouTube)

⏱️ 2 weeks max

📚 Step 2: Take Andrew Ng’s ML Course (2025 Edition)

This is the only full course I’d do.

Why?

  • Explained simply

  • No need to code much early on

  • Focuses on core ML algorithms (like linear regression, decision trees, etc.)

Take it slow: 3–4 hours/week for 5–6 weeks.

🧠 Step 3: Reinforce Through Projects

Courses don’t teach real-world problem-solving. Projects do.

Start with:

  1. Titanic Dataset (classification)

  2. Housing Prices (regression)

  3. MNIST or Fashion-MNIST (image classification)

Use Kaggle + Google Colab. Use scikit-learn.

📈 Step 4: Learn Math as You Build

No need to memorize formulas.

Just learn:

  • Mean, variance, and standard deviation

  • Linear regression intuition

  • Gradient descent concept

Use visual YouTube videos like StatQuest or 3Blue1Brown.

🔥 Final Advice:

✅ Pick ONE course (Andrew Ng’s)
✅ Build 3–5 projects
✅ Don’t touch TensorFlow until you’ve mastered scikit-learn
✅ Stay consistent, not perfect

The fastest way to learn ML in 2025… is to stop overthinking it.

Build. Learn. Repeat.
Let’s go, future ML pro. 🚀

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