Day 8 – Probability Distributions in Machine Learning Applications

Hey everyone 👋 Dhairya here,

On Day 6, I explored the basics of probability distributions (Bernoulli, Binomial, Normal, Uniform). Today, I took it a step further and studied how these distributions are applied in actual ML models and algorithms.

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🔢 What I Learned Today

• Bernoulli Distribution in Logistic Regression

  • Binary classification → probability of success/failure.

  • Output layer of logistic regression models uses Bernoulli.

• Binomial Distribution in Experiments

  • Example: predicting number of correct answers out of 10 attempts.

  • Applied in hypothesis testing and success rates.

• Normal Distribution in Regression & Neural Nets

  • Many ML algorithms assume data/errors are normally distributed.

  • Weight initialization in deep learning uses variants of normal distribution.

• Uniform Distribution in Random Sampling

  • Used in random initialization of parameters.

  • Important in Monte Carlo methods.

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🌱 Reflections

It was eye-opening to see that distributions are not just “math stuff” — they’re everywhere in ML, from logistic regression to neural nets. Understanding them deeply will definitely make me a better ML engineer.

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💻 Notebook

My Day 8 notebook is available here 👉 GitHub Link – Day 8 Notebook

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📚 Resources

🎥 YouTube

• StatQuest – Logistic Regression

• 3Blue1Brown – Neural Network Initialization

• Khan Academy – Probability Distributions Applications

🌐 Websites

• GeeksforGeeks – Probability Distributions in ML

• Machine Learning Mastery – Probability for ML

• Towards Data Science – Why Probability is Key for ML

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🎯 What’s Next?

For Day 9, I’ll dive into Bayes’ Theorem and Naive Bayes Classifier.

See you tomorrow 👋
— Dhairya