Roadmap for AI/ML Roles [2024/2025]

Introduction

Preparing for AI/ML roles can be overwhelming due to the vast amount of topics to cover. To stay organized, I created a structured roadmap that covers fundamental concepts from classical ML to Generative AI. Here’s my checklist, which helped me stay on track.

Roadmap for AI/ML Interviews

1. Mathematics for ML
2. Classical Machine Learning
3. Multi-Layer Perceptrons (MLP)
4. Deep Neural Networks (DNN)
5. Natural Language Processing (NLP)
6. Computer Vision (CV)
7. Generative AI (GenAI)
8. Model Evaluation & Training Techniques
9. Final Interview Preparation

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Mathematics for ML

• Linear Algebra
  • Vector spaces
  • Eigenvalues/Eigenvectors, Orthogonal Eigenvectors
  • Matrix decompositions (SVD, PCA)
• Probability and Statistics
  • Bayes’ theorem
  • Naïve Bayes
  • Gaussian Distributions
  • KL Divergence
  • Entropy
• Optimization
  • Gradient Descent (SGD, Adam, RMSprop)
  • Convex Optimization
  • Lagrange Multipliers
• Calculus
  • Differentiation
  • Chain Rule
  • Hessian Matrices
  • Jacobians

Classical Machine Learning

• Overfitting, Underfitting, and Regularization
  • L1 Regularization
  • L2 Regularization
  • Dropout (relation with L1 and L2)
• Bias-Variance Tradeoff
• Feature Engineering
• Supervised vs. Unsupervised Learning
• Common ML Algorithms
  • k-NN
  • SVM
  • XGBoost
  • Random Forests
  • Naïve Bayes
  • Decision Trees
  • Gini Index

Multi-Layer Perceptrons (MLP)

• Perceptron Model, Learning Rule, XOR Problem
• Activation Functions: ReLU, Sigmoid, Tanh, Softmax
• Backpropagation, Chain Rule, Weight Updates
• Batch Normalization, Dropout
• Vanishing and Exploding Gradient Problem

Deep Neural Networks (DNN)

• CNNs (Filters, Pooling, Architectures: AlexNet, VGG, ResNet, EfficientNet)
• Transfer Learning, Fine-Tuning, Normalizations
• Residual Connections, 1x1 Convolutions
• RNN, LSTM, GRU: Vanishing Gradients, Gated Mechanisms
• Attention Mechanisms, Transformers (Self-Attention, Multi-Head Attention)

Natural Language Processing (NLP)

• Basic NLP Preprocessing
  • Tokenization, Lemmatization, Stemming, Stop-word Removal
• Word Embeddings
  • Word2Vec (CBOW, Skip-Gram), GloVe, FastText
  • Transformer-based embeddings
• Sequence Models
  • RNNs, LSTMs, GRUs (gates) and need of BERT
• NLP Tasks
  • Named Entity Recognition (NER), Part-of-Speech (POS) Tagging
  • Sentiment Analysis, Machine Translation

Computer Vision (CV)

• CNN Architectures: ResNet, MobileNet, EfficientNet, Residual Connections
• Object Detection: YOLO, Faster R-CNN, SSD
• Non-Maximum Suppression (NMS) in Object Detection
• Image Segmentation: U-Net, R-CNNs family, Semantic vs. Instance vs. Panoptic Segmentation, RoIPooling
• Generative Models for Images: GANs, Autoencoders

Generative AI (GenAI)

• GANs: Vanilla GAN, DCGAN, StyleGAN, CycleGAN
• Variational Autoencoders (VAEs) and Latent Representations
• Transformers & LLMs: Basic architecture, GPT-series, LLaMA, Mistral
• Diffusion Models: DALL-E, Stable Diffusion
• Transformer Models in GenAI: ViT, Transformer-XL
• Types of Attention Mechanisms: Self-Attention, Multi-Head Attention, Cross-Attention, Group Query Attention
• Optimization Techniques for Attention: FlashAttention, Sliding Window Attention, Linformer
• Other quite asked topics : Positional encodings, RPE, layernormalization, RMS Norm, SwiGLU

Model Evaluation & Training Techniques

• Evaluation Metrics
  • Accuracy, Precision, Recall, F1-score, MAE, MSE, cosine similarity
  • ROC-AUC Curve, PR Curve, perplexity score, BLUE score
• Validation Techniques
  • Train-Test Split, Cross-Validation (k-Fold, Leave-One-Out)
  • Bootstrapping
• Famous Training Techniques
  • Early Stopping, Learning Rate Scheduling
  • Data Augmentation, Transfer Learning
  • Hyperparameter Tuning (Grid Search, Random Search, Bayesian Optimization)

Final Preparation

• Common pitfalls in ML model deployment
  • Overfitting
  • Data Leakage
  • Model Interpretability
• System Design for AI Applications
• Coding Challenges for ML/AI Roles
  • Implementing ML Algorithms from Scratch
  • Data Structures & Algorithms (DSA)

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Conclusion

This checklist helped me cover essential topics in a structured manner while preparing for AI/ML roles. If you’re on the same journey, feel free to use this as a reference and tailor it to your needs!