Track
📊 ML & Data Science Interviews
Bias/variance, the core algorithms, evaluation metrics, feature engineering, statistics and A/B testing, deep-learning fundamentals (gradient descent, attention, RAG vs fine-tuning), and the ML system-design round. Classic ML is the floor in 2026 — this track covers the floor and the breadth questions above it.
Readiness0%
Lessons0/21
Cards mastered0/27
Focused study154 min
Roadmap
8 modules1
Fundamentals
2
Algorithms
3
Deep Learning
4
Metrics
5
Features
6
Stats
7
System Design
8
SQL/Pandas
Modules
clear 60% to unlock the next📐MODULE 01
ML Fundamentals
Bias/variance, overfitting, regularization, and honest validation.
0%
🧮MODULE 02 · 🔒
Core Algorithms
The models everyone asks about — intuition, and when/why each.
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🧠MODULE 03 · 🔒
Deep Learning Fundamentals
Gradient descent, neural nets, and the LLM-era breadth questions.
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🎯MODULE 04 · 🔒
Evaluation Metrics
Pick the metric that matches the cost of being wrong.
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🧱MODULE 05 · 🔒
Feature Engineering & Data
Where models are really won — and quietly ruined by leakage.
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🎲MODULE 06 · 🔒
Statistics & Probability
Distributions, hypothesis tests, Bayes, and honest A/B tests.
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⚙MODULE 07 · 🔒
ML System Design
The ML design round: from problem framing to drift.
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🗃MODULE 08 · 🔒
SQL & pandas for DS
The coding round most DS candidates actually get.
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