Statistics and Probability for Machine Learning

Welcome back to the Gudsky AI & ML Educational Series 🚀

In this video, we dive into the statistics and probability concepts that every ML engineer and data scientist must know.

You’ll learn:
✅ Descriptive statistics – mean, median, variance, standard deviation
✅ Probability basics – random variables, conditional probability, Bayes’ theorem
✅ Probability distributions – Normal, Bernoulli, Binomial, Poisson
✅ Inferential statistics – sampling, hypothesis testing, confidence intervals
✅ How statistics and probability power ML algorithms like Logistic Regression, Naive Bayes, and Decision Trees

📘 Why this video matters?
Machine Learning is built on statistics and probability. Without this foundation, it’s impossible to understand:
How models learn patterns from data
How to evaluate performance with statistical metrics
Why probabilistic reasoning is crucial for predictions

👉 Stay tuned, because in the next video, we’ll get practical with "Implementing Linear Algebra Operations in Python".
🔔 Don’t forget to Like, Share & Subscribe to Gudsky Research Foundation for more AI/ML tutorials.

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