Understanding Normal Distribution
đź“Ś What is Normal Distribution (Gaussian Distribution)? The normal distribution (or Gaussian distribution) is a type of continuous probability distribution for a real-valued random variable. It d...
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🧱 What You’ll Learn Here
đź“Š Foundations
Clear explanations of the statistics, linear algebra, and calculus you actually need — no more, no less.
🤖 ML Concepts
Build up from intuitive models to CNNs and neural networks — explained visually and step-by-step.
🧠Problem Solving
Practice with real problems, detailed solutions, and structured problem-solving roadmaps.
Mean, Variance, and Standard Deviation of Random Variables
How do we summarize a random variable with a single number? What happens to the mean and variance if we shift or scale the variable? This post explains the mean, variance, and standard deviation fo...
What Are Random Variables and How Do We Visualize Their Distributions?
Learn how to understand and visualize random variables using PMF, PDF, and CDF. Covers discrete vs continuous distributions with real examples and intuitive plots.
Understanding Independence and Bayes’ Rule
Learn how Bayes’ Theorem works, when to apply it, and how it connects to independence and conditional probability. This post breaks down key concepts with clear examples and practical relevance in ...
Making Sense of Probabilities: Union, Tables, and Conditional Thinking
Learn how to calculate the probability of combined events using union rules, contingency tables, and conditional logic. This post walks you through marginal, joint, and conditional probabilities wi...
How Random Is Random? Understanding Probability and Events
Ever flipped a coin and wondered why you got heads three times in a row? Welcome to the world of randomness and probability — where short-term surprises often give way to long-term patterns. In th...
Regression: Predicting Relationships Between Variables with the Best Fit Line
Learn linear regression step by step — from drawing the best-fit line and calculating residuals to interpreting slope and R², all in a beginner-friendly, ML-oriented guide.
Pearson’s r: Measuring the Strength and Direction of Linear Relationships
Learn how Pearson’s r measures the strength and direction of linear relationships between variables. Includes formula breakdown, Python code, and machine learning relevance.
Conditional vs. Marginal Proportions: What’s the Difference?
Understand the difference between marginal and conditional proportions using examples and contingency tables. A must-know concept for analyzing categorical data and preparing ML datasets.
Correlation Between Variables: Contingency Tables and Scatter Plots
Learn how to analyze relationships between variables using correlation techniques like contingency tables and scatter plots, based on whether your data is categorical or quantitative.
Bringing It All Together: A Real-World Stats Example
Walk through a complete real-world statistics example using test scores: calculate mean, median, range, IQR, standard deviation, z-scores, and visualize it with dot and box plots.
Z-Score: Comparing Values Using Standardization
Understand z-scores and standardization — how far a value is from the mean, and why it's a powerful tool in comparing values across distributions and machine learning tasks.
Measuring Variability: Variance and Standard Deviation
Learn how variance and standard deviation measure the spread of your data — with formulas, worked examples, and relevance to data science and machine learning.
Understanding Dispersion: Range, IQR, and the Box Plot
Understand how data spreads using range, interquartile range (IQR), and box plots — with clear examples and why they matter in machine learning.
Measuring the Center: Mean, Median, and Mode Explained
Learn how to calculate and choose between mean, median, and mode — key measures of central tendency in statistics and machine learning, with Python code and visual examples.