My Journey into Machine Learning: Completed Data Processing

I'm excited to share that I've completed the Data Processing section of the Udemy course "Machine Learning A-Z: AI, Python." This marks my first step into the world of Machine Learning, and I'm eager to keep going!

🔍 What I Learned:

✔ Handling missing data using Mean, Median, Mode
✔ Encoding categorical variables (Label Encoding & One-Hot Encoding)
✔ Feature Scaling with Standardization & Normalization
✔ Splitting data into Training & Test sets
✔ Importance of data preprocessing in ML models

Challenges I Faced & How I Overcame Them:

🔹 Understanding Feature Scaling: Initially, I was confused about when to use Standardization (Z-score) vs. Normalization (Min-Max Scaling). After experimenting with different datasets, I realized that Standardization works best for normally distributed data, while Normalization is useful for bounded feature values.

🔹 Dealing with Categorical Variables: At first, I struggled with when to use Label Encoding vs. One-Hot Encoding. Through hands-on practice, I understood that Label Encoding is better for ordinal data, whereas One-Hot Encoding prevents misinterpretation of categorical values.

What's Next?

I'm now moving on to the next part of the course—Regression Models! Can't wait to implement ML algorithms and build predictive models.

Let's connect! If you're also learning ML, drop a comment! Let’s grow together.

#MachineLearning #AI #Python #DataScience #LearningJourney #Udemy