Welcome to the finale of Unit 2 in Mindforge ML. We are bridging the gap between raw data and a trainable model.

Computers don't understand text, and models cheat if you let them see the answers. In this episode, we cover the final critical steps: translating categories into numbers and rigorously testing your setup to prevent overfitting.

Key topics:

• Encoding: One-Hot vs. Label Encoding—translating the world into math.

• The Split: Why 80/20 isn't just a random number, and how Stratified Splitting saves classification models.

• Cross-Validation: The most robust way to trust your model's score.

• Data Leakage: How to avoid the most embarrassing mistake in data science.

Your data is now ready. The modeling begins.

Series: Mindforge ML | Unit 2Produced by: Chatake Innoworks Pvt. Ltd.Initiative: MindforgeAI

Welcome to Mindforge ML. In this episode, we explore Feature Scaling—the mathematics of fairness in machine learning.

When one feature ranges from 0-1 and another from 0-10,000, your model gets confused. We discuss how to bring all your data to a level playing field without losing the relationships between them.

Key topics:

• Normalization vs. Standardization: The battle between Min-Max and Z-Score.

• Algorithm Sensitivity: Why KNN and SVMs fail without scaling, while Random Forests don't care.

• Robust Scaling: How to scale data that is full of outliers.

• Data Leakage: The golden rule of fit_transform() vs. transform().

Make sure your model listens to every feature equally.

Series: Mindforge ML | Unit 2Produced by: Chatake Innoworks Pvt. Ltd.Initiative: MindforgeAI

Welcome to Mindforge ML. In this episode, we investigate the rebels of your dataset: outliers.

An outlier can be a critical insight (fraud detection) or a disastrous error (sensor glitch). The difference lies in context. We move beyond simple deletion to explore detection and sophisticated treatment strategies.

Key topics:

• Detection: Using Z-scores, IQR, and Isolation Forests to hunt down anomalies.

• The Choice: Deciding when to remove, cap, or keep extreme values.

• Visualization: Spotting problems with box plots and scatter plots.

• Context: Why domain knowledge is your best tool for outlier management.

Stop blindly deleting data. Learn to read the extremes.

Series: Mindforge ML | Unit 2Produced by: Chatake Innoworks Pvt. Ltd.Initiative: MindforgeAI