Discretization transforms continuous data into categories for better analysis. Explore key techniques for both unsupervised and supervised m

Discover how scaling techniques in machine learning can boost model performance, ensure faster training, and enhance prediction accuracy.

Explore the basics of statistical data analysis, covering key metrics like mean, variance, skewness, kurtosis, and correlation.

Discover why more dimensions can spell trouble for data and learning, turning simple problems into complex, high-dimensional challenges!

Explore the depths of non-linear dimensionality reduction methods! Unravel complex data structures and enhance your analytics.

In this article, we explored PCA and SVD, the two most used linear dimensionality reduction techniques.

In this article we will go through a quick overview of what Bayesian modeling is and how can we use probabilistic programming to leverage it

In this article we will explore variational autoencoders in terms of the intuition behind them and the math needed to train them.

What are Generative Models? What are Discriminative Models? In this article we'll go through the fundamentals of each type of model.