Core Skills Guide for AI Interviews (Math, Code, SQL) 2025

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Success in AI and Machine Learning roles requires more than just models. This guide covers the essential bedrock: the math that powers the algorithms, the coding skills to implement them, and the data analytics abilities to derive insights.
💡 Why focus on these three pillars?

Top-tier interviews test your depth across three fundamental areas:

  1. Math Foundations: To prove you understand the why behind the algorithms.
  2. Coding & Algorithms: To show you can build efficient and correct solutions.
  3. SQL & Analytics: To demonstrate you can work with the data that fuels everything.

Mastering these areas will set you apart as a well-rounded and capable candidate.

📐 Math Foundations

What’s covered here?
This section covers the mathematical principles that underpin nearly every AI/ML model. A deep understanding of these topics allows you to derive algorithms from scratch, understand research papers, and innovate beyond existing tools.

Learn the theory of uncertainty with Probability and the practice of data analysis with Statistics.

🎲 Probability & Distributions

The language of uncertainty, essential for modeling and understanding data.

Conditional Probability Bayes' Theorem PDF vs CDF Gaussian Distribution

📊 Statistics & Inference

The tools to describe data and make robust conclusions from samples.

Law of Large Numbers Central Limit Theorem Maximum Likelihood (MLE) p-value Type I vs Type II Error A/B Testing

Master how data is structured with Linear Algebra and how models learn with Calculus.

▦ Linear Algebra

The language of deep learning, used to represent and manipulate data efficiently.

Vectors & Operations Matrix Operations Eigenvalues & Eigenvectors SVD

📈 Calculus & Optimization

The engine of how models “learn” by minimizing error.

Derivatives Chain Rule Gradient Descent Convex Functions

🧮 Coding Interview Prep

What’s covered here?
Even for specialized AI roles, strong fundamentals in data structures and algorithms are a prerequisite. This section covers classic interview problems grouped by pattern, helping you build the problem-solving muscle needed for technical screens.
🧑‍💻
Practice is key! Use the Simulation Guide and Reflection Notes to prepare for real-world interview pressure.

🗃️ SQL + Analytics

What’s covered here?
Machine learning runs on data. The ability to efficiently query, join, and aggregate data using SQL is a mandatory skill for nearly every data scientist, research scientist, and machine learning engineer.