The pre-requisite topics that you should understand before you get started Data science and Machine Learning.

Mathematics:-

- covering differential calculus,
- integral calculus,
- matrices and linear algebra,
- set theory,
- differential equations,
- complex numbers,
- sequences and series,
- logic and proofs,
- multivariate calculus,
- convergence of sequences and functions,
- and partial differential equations.

Statistics:-

- covering exploratory data analysis,
- elementary probability,
- univariate random variables,
- bi-variate random variables,
- generating functions,
- statistical estimation theory,
- hypothesis testing and confidence intervals,
- and simple linear regression.

#DataScience #machinelearning #statistics #mathematics @epythonlab

Why "Z-Score" is a Must-Know for Your Next ML Interview 📊

​In a Machine Learning interview, you aren't just asked about complex models. You're asked how you handle messy data.
​One of the most common questions: "How do you detect outliers in a dataset?"

​If you’re monitoring thousands of payments and a single transaction is 100x larger than the rest, you need a statistical way to flag it. Enter the Z-Score.

​How it works:

The Z-Score tells you how many standard deviations a data point is from the mean [01:43].
🔹 The Formula: z = (x - \mu) / \sigma
🔹 The Logic: If the absolute value of Z is > 2 or 3, it’s a red flag.
​In my latest video, I walk through a Python implementation for fraud detection:
✅ Using the statistics module for mean and stdev [02:46].
✅ Writing a reusable function to flag suspicious values [03:04].
✅ Why we use abs(z) to catch both high and low extremes [05:18].
​Don't let a few "noisy" numbers ruin your model's accuracy. Master the basics of data pre-processing first.

​Watch the full breakdown here: https://www.youtube.com/watch?v=cCIg80H0Qp8
​#DataScience #MachineLearning #Python #InterviewPrep #FraudDetection #AI #Statistics