Random Forest: A Beginner's Guide with Visual Illustrations & Examples

In this post, let us explore:

• Random Forest
• When to use
• Advantages
• Disadvantages
• Hyperparameters
• Examples

Basics of Ensemble Models

In this post, let us explore:

• Ensemble Models
• Bagging
• Boosting
• Stacking

Train-Test split and Cross-validation: Visual Illustrations & Examples

Building an optimum model which neither underfits nor overfits the dataset takes effort. To know the performance of our model on unseen data, we can split the dataset into train and test sets and also perform cross-validation.

Heatmap: Visual Examples

Heatmap depicts the two-dimensional data (matrix form) in the form of graph.

Occam's Razor, Bias-Variance Tradeoff, No Free Lunch Theorem and The Curse of Dimensionality

In this post, let us discuss some of the basic concepts/theorems used in Machine Learning:

• Occam's Razor (Law of Parsimony)
• What is Bias-variance Tradeoff
• No Free Lunch Theorem
• The curse of dimensionality

Mastering Decision Trees with Visual Examples

Decision Tree models are simple and easy to interpret.

In this post, let us explore

• What are decision trees
• When to use decision trees
• Advantages
• Disadvantages
• Examples with code (Python)

Mastering Exploratory Data Analysis: A Beginner's Guide with Visual Illustrations

"A picture is worth a thousand words"

A complex idea can be understood effectively with the help of visual representations. Exploratory Data Analysis (EDA) helps us to understand the nature of the data with the help of summary statistics and visualizations capturing the details which numbers can't.

In this post, let us explore

• Visualizing the data 
• Summarizing the data
• Correlation matrix