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
- Mathematics for ML
- Classical Machine Learning
- Multi-Layer Perceptrons (MLP)
- Deep Neural Networks (DNN)
- Natural Language Processing (NLP)
- Computer Vision (CV)
- Generative AI (GenAI)
- Model Evaluation & Training Techniques
- Final Interview Preparation
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)
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!
![Roadmap for AI/ML Roles [2024/2025]](/images/logbook.png)