ML/DS Master Study Guide

ML/DS Study Guide

57 Topics · Interview & Concept Reference

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🔢 Mathematical Foundations▾

01Linear Algebra

02Probability Theory

03Statistics & Inference

04Calculus & Optimization

🧭 ML Thinking & Evaluation▾

05ML Paradigms

06Bias-Variance Tradeoff

07Cross-Validation

08Evaluation Metrics

09Confusion Matrix & ROC

10Calibration & Explainability

🌳 Classical ML Algorithms▾

11Linear & Logistic Regression

12k-Nearest Neighbors

13Naive Bayes

14Support Vector Machines

15Decision Trees

16Random Forest

17Gradient Boosting

18XGBoost Deep Dive

19Clustering

20Dimensionality Reduction

⚙️ Data & Model Engineering▾

21Feature Engineering

22Imbalanced Datasets

23Regularization

24Hyperparameter Tuning

25Ensemble Methods

🧠 Neural Network Foundations▾

26Perceptron & MLP

27Activation Functions

28Loss Functions

29Backpropagation & Training Loop

30Optimizers

31Weight Initialization

🤖 Deep Learning Architectures▾

32CNNs

33RNNs & LSTMs

34Attention & Architecture

35Transformers

36Transfer & Distillation

💬 NLP & Language Models▾

37Tokenization & Embeddings

38BERT & Encoder Models

39LLMs, SLMs & RLHF

✨ LLM Era▾

48RAG & RetrievalNEW

49Vector DatabasesNEW

50Agentic AINEW

54Multimodal ModelsNEW

📡 Specialized Domains▾

40Time Series Modeling

41Credit Risk & Scorecards

46Graph Neural NetworksNEW

51Responsible AI & FairnessNEW

52Causal Inference & A/B TestingNEW

53Data Engineering for MLNEW

🚀 Production & System Design▾

42MLOps & Deployment

43ML System Design

44Python, SQL & DSA

45Model Cheatsheet

47TF, Keras & PyTorch