Machine Learning & Deep Learning Engineering
Master the mathematics, architectures, and algorithms behind modern AI. Build and deploy predictive supervised models, unsupervised clusters, deep CNNs, RNNs, transformer models, and scalable inference pipelines with PyTorch and TensorFlow.
Core Engineering Pillars
Designed to build mathematical intuition, algorithmic mastery, and enterprise deployment skills for real-world ML systems.
Regression, Decision Trees, XGBoost, Random Forests, SVMs, and Bayesian optimization pipelines.
Custom PyTorch architectures, backpropagation calculus, regularizers, and vanishing gradient solutions.
Object detection with YOLOv8, segmentation with Mask R-CNN, BERT embeddings, and Attention mechanisms.
Model tracking with MLflow, containerization with Docker, Triton Inference Server, and AWS SageMaker endpoints.
Curriculum & Technical Roadmap
Step-by-step modular breakdown covering classical statistical modeling through deep foundation networks.
- Linear Algebra, Multivariate Calculus, Probability Distributions, and Hypothesis Testing
- Feature scaling, Imputation, One-Hot/Target Encoding, PCA dimensionality reduction
- Supervised Algorithms: Logistic Regression, SVM, KNN, Random Forest, LightGBM, CatBoost
- Cross-Validation, ROC-AUC, Precision-Recall tradeoffs, and Hyperparameter Tuning (Optuna)
- PyTorch Tensors, Autograd engine, custom Datasets and DataLoaders
- Multi-Layer Perceptrons (MLP), Activation Functions (ReLU, GELU, Swish), Loss Formulations
- Optimization Algorithms: SGD with Momentum, RMSprop, Adam, AdamW
- Overfitting Mitigation: Dropout, Batch Normalization, LayerNorm, Weight Decay
- Convolutions, Pooling, Strides, Receptive Field calculations
- ResNet, EfficientNet, MobileNet, and Transfer Learning strategies
- Object Detection & Localization: Real-time YOLOv8 architecture
- Image Segmentation with U-Net and Vision Transformers (ViT)
- Word2Vec, GloVe, Recurrent Neural Networks (RNN), LSTM, and GRU gates
- Seq2Seq models and the Self-Attention mechanism (Scaled Dot-Product Attention)
- Transformer Encoders & Decoders: Deep dive into BERT, RoBERTa, and GPT-2
- HuggingFace Transformers library, Tokenizers, and fine-tuning pipelines
- Experiment tracking, artifact storage, and model registry via MLflow / Weights & Biases
- Serving models with FastAPI, TorchScript, ONNX Runtime, and Docker
- Data drift monitoring, model degradation metrics, and automated retraining pipelines
- Deploying production inference endpoints to AWS SageMaker
Production-Grade Capstones
Deploy real-world models to showcase rigorous engineering depth on your resume.
Real-Time Defect Detection (YOLOv8)
Train and deploy an edge computer vision pipeline detecting micro-fractures in industrial parts with 45+ FPS inference speeds.
Healthcare Multi-Modal Diagnostic System
Build a deep CNN + clinical tabular model predicting patient risk scores by fusing chest X-rays with structured EHR lab data.
Financial Fraud Detection Engine
Deploy an ensemble XGBoost & autoencoder network processing imbalanced streaming transaction logs with 99.4% precision.
End-to-End MLOps Pipeline on AWS
Implement an automated CI/CD retraining loop with MLflow, GitHub Actions, Docker, and AWS SageMaker autoscaling endpoints.
Frameworks & Tool Ecosystem
Industry-standard libraries used across production data science and AI teams.