Proclus Academy

Machine Learning / Deep Learning

Let's learn how to calculate Confusion Matrix and Accuracy using Python libraries.

I'll also show you two different ways to visualize the Confusion Matrix.

Summary Image: Confusion Matrix and Accuracy Using Scikit-Learn & Seaborn
Let's look at the basic metrics to estimate a classification model’s predictive performance.

You'll also gain practical skills to generate and visualize these metrics using Scikit-Learn and Seaborn.

Summary Image - Using Confusion Matrix and Accuracy to Test Classification Models
How can you perform K-Fold Cross-Validation to evaluate machine learning models?

I'll show you two ways using Python and Scikit-Learn's helper functions - cross_val_score() and cross_validate().

Summary Image: K-Fold Cross-Validation Using Python and Scikit-Learn
It's time to learn Cross-Validation, the tool serious data scientists use to estimate model performance.

Cross-Validation builds upon Train Test Split and provides a better estimate of a machine learning model's performance.

Summary Image: K-Fold Cross-Validation
Sometimes you want to draw boxplots where each column gets its own y-axis.

Here's how you can do it using pandas, Matplotlib, and Seaborn.

Boxplot with separate Y-Axis For Each Column: Summary image - Taipei skyline with skyscrapers of different heights
Outliers can overshadow other data points of a feature. That can negatively influence standard scaling.

Here's why you should use robust scaling to handle outliers.

Robust Scaling Summary Image: A tiny boat and a tall lighthouse.
Sometimes the default statistics provided by pandas describe() method are not enough.

Here's how you can generate custom statistics using the agg() method.

Image: Track and field with numbered tracks
Features with vastly different scales can lead to subpar machine learning models.

We must scale such features. Here’s how sklearn's Standard and MinMax scalers can help.

Image: Half Dome at Yosemite
Overfitted machine learning models can lead to serious performance issues in the real world.

Let's explore how you can stop overfitting dead in it's tracks and train your models with confidence.

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How can you apply different transformations to different columns of your dataset?

We'll explore a few ways and discuss why using ColumnTransformer is the best approach.

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