Hi, I'm Anshika — a B.Tech student diving into the world of AI and Machine Learning.
I just completed Andrew Ng’s Supervised Machine Learning course on Coursera (the first in the ML Specialization by DeepLearning.AI), and I wanted to document my learnings, struggles, and next steps as I begin my ML journey.
What is Supervised Machine Learning?
Supervised ML is about teaching machines using labeled data.
You provide inputs (features) along with the correct outputs (labels), and the model learns to predict outputs for new, unseen inputs.
There are two key types:
- Regression → Predict continuous values (e.g., house price, traffic speed)
- Classification → Predict categories (e.g., spam vs. not spam)
Key Concepts I Learned
- Linear Regression (with one and multiple variables)
- Gradient Descent – how the model "learns"
- Cost Function (Mean Squared Error) – measuring how wrong the model is
- Logistic Regression – used for binary classification problems
- Overfitting vs. Underfitting – finding the balance between simplicity and accuracy
- Regularization (L2) – prevents the model from overfitting the training data
Tools I Used
- Python
- NumPy
- Jupyter Notebook (for practice exercises)
What Helped Me Understand Better
- Visualizing gradient descent and cost function graphs
- Coding linear regression from scratch before using libraries
- Reading discussion forums whenever I got stuck
- Taking handwritten notes to simplify complex terms
What’s Next?
Now that I’ve finished the Supervised Learning course, I plan to:
- Continue the specialization: Next up → Unsupervised Learning
- Apply regression to a real-world dataset (maybe traffic or energy!)
- Start writing beginner-friendly tutorials alongside learning
If you’re on a similar journey or just starting out — feel free to reach out! Let’s learn and build together.
Thanks for reading!
🔗 Connect with me:
keep up the good work👏🏻
Happy Coding