Supervised Machine Learning: Concepts I Finally Understand

AnshikaAnshika
2 min read

πŸ‘‹ Hi, I'm Anshika β€” a B.Tech student on a mission to learn and build using AI and machine learning.

I recently completed the Supervised Machine Learning course by Andrew Ng on Coursera β€” the first step in the Machine Learning Specialization by DeepLearning.AI.

Here’s what I learned, how I approached it, and what I plan to do next πŸš€


πŸ“˜ What is Supervised Machine Learning?

Supervised ML is about teaching a machine to learn from labeled data. That means we give the model input features (X) and the correct output (y), so it can learn to make predictions on new data.

There are two main types of problems:

  • Regression – Predicting continuous values (e.g., traffic speed, house prices)

  • Classification – Predicting categories (e.g., spam or not spam)


🧠 Key Concepts I Learned

  • Linear Regression (single and multiple variables)

  • Gradient Descent – optimization technique to minimize error

  • Cost Function (MSE) – measures how wrong the model is

  • Logistic Regression – used for binary classification

  • Overfitting vs. Underfitting

  • Regularization – helps prevent overfitting


πŸ› οΈ Tools I Used

  • Python

  • NumPy

  • Jupyter Notebook (to test code and visualize learning)

I mostly worked through the course labs, re-implemented some functions from scratch, and played with small data examples to get comfortable.


πŸ’‘ What Helped Me Understand Better

  • Visualizing the cost function and gradient descent updates

  • Repeating tough concepts like regularization multiple times

  • Taking notes in my own words

  • Reading peer discussions and Stack Overflow when stuck


πŸ”œ What's Next?

I'm planning to:

  • Start the next course in the specialization: Unsupervised Learning

  • Apply what I’ve learned to a real dataset (maybe traffic or energy consumption!)

  • Write more tutorials on ML concepts β€” in beginner-friendly ways


πŸ™Œ Final Thoughts

Starting out in ML felt overwhelming at first β€” but this course gave me a strong foundation. If you're also learning machine learning or about to start this course, feel free to reach out or drop a question!

Thanks for reading 🌱


πŸ”— Let’s Connect:


Stay tuned for more beginner-friendly posts as I learn and build in public! ✨

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Anshika
Anshika