🧠 Core AI & Deep Tech Track

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.

4 Months Comprehensive Track

4+ Production Labs Industry Capstones

100% Placement Support

Global Cert ML Engineer Credential

Core Engineering Pillars

Designed to build mathematical intuition, algorithmic mastery, and enterprise deployment skills for real-world ML systems.

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Statistical ML & Optimization

Regression, Decision Trees, XGBoost, Random Forests, SVMs, and Bayesian optimization pipelines.

🧠

Deep Neural Networks (DNN)

Custom PyTorch architectures, backpropagation calculus, regularizers, and vanishing gradient solutions.

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Computer Vision & NLP

Object detection with YOLOv8, segmentation with Mask R-CNN, BERT embeddings, and Attention mechanisms.

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MLOps & Model Deployment

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.

Module 1: Mathematics, Feature Engineering & Statistical ML ▾

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)

Module 2: Deep Learning Architectures with PyTorch ▾

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

Module 3: Computer Vision & Convolutional Networks ▾

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)

Module 4: Natural Language Processing & Transformers ▾

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

Module 5: Production MLOps, CI/CD & Model Serving ▾

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.

Project 01

Real-Time Defect Detection (YOLOv8)

Train and deploy an edge computer vision pipeline detecting micro-fractures in industrial parts with 45+ FPS inference speeds.

Project 02

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.

Project 03

Financial Fraud Detection Engine

Deploy an ensemble XGBoost & autoencoder network processing imbalanced streaming transaction logs with 99.4% precision.

Project 04

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.

◆ PyTorch ◆ TensorFlow / Keras ◆ Scikit-Learn ◆ HuggingFace ◆ OpenCV ◆ XGBoost / LightGBM ◆ MLflow ◆ ONNX Runtime ◆ Docker ◆ AWS SageMaker

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