AI & ML Interview Roadmap: A Step-by-Step Study Guide (2025)

Welcome to the AI & Generative AI Learning Roadmap — your one-stop study companion for building a full-stack understanding of AI, ML, and GenAI.

Whether you’re a beginner starting from scratch or an experienced engineer revising for Top Companies interviews, this guide helps you master theory, code, and system design — step by step.

🏗️ How to Use This Roadmap

1. Follow modules in order. Each builds upon the previous one.
2. Track your hours. The timeline estimates are based on focused study (no distractions).
3. Dive deeper. Every topic is linked to a full explanation and practice guide.
4. Interview Focus. Each module includes hints for top tech company interview expectations.

📘 Module 1 — Math Foundations (~35 hrs)

Mathematics is the language of ML. These topics ensure you can reason about optimization, geometry, and uncertainty in models.

🧮 Linear Algebra (~10 hrs)

• Vectors & Operations
• Matrix Operations
• Determinant, Inverse, Rank
• Eigenvalues, Eigenvectors & SVD

💬 Interview Focus: PCA, gradient derivations, data transformations.

📈 Calculus & Optimization (~8 hrs)

• Limits, Continuity, Differentiability
• Derivatives & Gradients
• Chain Rule & Backpropagation
• Optimization & Convexity

💬 Interview Focus: loss surfaces, gradient descent intuition, activation derivatives.

🎲 Probability & Statistics (~12 hrs)

• Random Variables & Distributions
• Bayes Theorem & Independence
• Expectation, Variance & Covariance
• Sampling, MLE & Hypothesis Testing
• Confidence Intervals & A/B Testing

💬 Interview Focus: statistical inference, bias-variance, p-values, uncertainty quantification.

🔢 Information Theory (~3 hrs)

• Entropy, Cross-Entropy & KL Divergence
• Mutual Information

💬 Interview Focus: cross-entropy loss, information gain, VAEs, and regularization theory.

🤖 Module 2 — Machine Learning (~45 hrs)

The backbone of every AI system. Learn algorithms, evaluation, feature handling, and interpretability.

⚙️ Core ML Concepts (~8 hrs)

• Bias-Variance Tradeoff
• Overfitting vs Underfitting
• Regularization (L1, L2)
• Cross Validation & Metrics

💬 Interview Focus: model generalization, metrics trade-offs, confusion matrices.

📊 Linear & Logistic Regression (~6 hrs)

• Linear Regression
• Logistic Regression
• Loss Functions (MSE, Cross-Entropy)
• Gradient Descent Optimization

💬 Interview Focus: analytical gradient derivations, regularization intuition.

🌳 Decision Trees & Ensembles (~6 hrs)

• Decision Trees
• Random Forest
• Gradient Boosting & XGBoost

💬 Interview Focus: Gini vs Entropy, bias-variance in ensembles, feature importance.

🔍 SVMs & Kernel Methods (~5 hrs)

• SVM Intuition & Geometry
• Hard vs Soft Margin
• Kernel Trick

💬 Interview Focus: margin intuition, kernel transformations.

🧩 Feature Engineering & Preprocessing (~7 hrs)

• Normalization & Standardization
• Encoding (One-Hot, Label, Target)
• Outlier Detection & Handling

💬 Interview Focus: data cleaning, scaling for gradient stability, categorical encodings.

📈 Unsupervised Learning (~5 hrs)

• K-Means Clustering
• PCA (Dimensionality Reduction)
• HDBSCAN, UMAP

💬 Interview Focus: clustering metrics, PCA math, distance metrics.

⏱️ Time Series (~4 hrs)

• ARIMA, SARIMA
• Feature Engineering for Temporal Data
• Forecast Evaluation Metrics

💬 Interview Focus: seasonality, stationarity, and autocorrelation intuition.

🧠 Module 3 — Deep Learning (~40 hrs)

Neural architectures and training dynamics — the building blocks of modern AI.

🧩 Core Neural Networks (~8 hrs)

• Perceptron & MLP
• Backpropagation
• Activation Functions
• Optimization Algorithms (SGD, Adam)

💬 Interview Focus: weight updates, vanishing gradients, optimizer behavior.

🧱 CNNs (~6 hrs)

• Convolution & Pooling
• Modern Architectures (ResNet, Inception)
• Transfer Learning

💬 Interview Focus: feature maps, filters, and residual connections.

🔄 RNNs & Sequence Models (~6 hrs)

• RNN Fundamentals
• LSTM, GRU
• Seq2Seq Models

💬 Interview Focus: gating mechanisms, temporal gradients.

⚡ Transformers (~8 hrs)

• Self-Attention & Multi-Head Attention
• Positional Encoding
• Encoder-Decoder Architecture

💬 Interview Focus: attention math, QKV projections, scaling behavior.

🌀 Autoencoders & Diffusion (~6 hrs)

• Autoencoders, Variational Autoencoders
• Denoising Autoencoders
• Diffusion Models & Stable Diffusion

💬 Interview Focus: latent space representations, noise scheduling.

🧪 Module 4 — ML System Design (~25 hrs)

Everything needed to take models to production — pipelines, scaling, feedback loops.

• Lifecycle Stages
• Data Infrastructure
• Model Registry & Versioning
• CI/CD for ML Pipelines
• Feature Stores
• Monitoring (Data Drift, Concept Drift)
• Batch vs Real-Time Systems
• End-to-End ML System Design

💬 Interview Focus: scalability, data freshness, retraining, latency trade-offs.

🗃️ Module 5 — SQL + Analytics (~20 hrs)

Master analytical SQL — the foundation of data reasoning and ML feature work.

• SQL Query Lifecycle
• Joins, Aggregations, Filtering
• Window Functions
• Cohort & Retention Analysis
• Execution Plans & Indexing
• Schema Design & Normalization

💬 Interview Focus: query efficiency, analytical reasoning, and metric derivations.

🧬 Module 6 — Generative AI & LLMs (~40 hrs)

Advanced module on modern foundation models and intelligent systems.

• Transformers from Scratch
• LLM Architecture & Tokenization
• Fine-Tuning (SFT, LoRA, PEFT)
• Prompt Engineering & CoT
• RAG (Retrieval-Augmented Generation)
• LangChain & LlamaIndex Frameworks
• Agentic Systems: ReAct, AutoGPT
• Model Serving & Deployment (Triton, VLLM)

💬 Interview Focus: LLM architecture, fine-tuning trade-offs, prompt reasoning, retrieval design.

⏰ Approximate Total Study Time

💡 Tip: Pair this roadmap with the Interactive Mindmap to visualize dependencies between topics.

📚 Next Step:
Once you’ve covered this roadmap, practice coding and case studies using:

• Kaggle + StrataScratch (Data Practice)
• LeetCode SQL
• OpenAI API / HuggingFace Transformers Notebooks
• Mock ML System Design Interviews

🎯 Outcome:
By completing this roadmap, you’ll have both theoretical mastery and interview-ready intuition — exactly what Top Companies and top-tier startups expect.

🚀 Happy Learning!
Made with ❤️ by Raj Shaikh