Generative AI
Advanced
5-6 Hours
Agentic Multi-Document RAG System with LangChain, LlamaIndex & ChromaDB
Develop an enterprise-grade Retrieval-Augmented Generation (RAG) agent capable of parsing heterogeneous documents (PDFs, spreadsheets, technical manuals), generating dense contextual embeddings, and storing them in ChromaDB. Implement hybrid semantic search combining BM25 keyword matching with dense cosine similarity, followed by a cross-encoder reranking stage and self-reflective query routing via LangGraph to eliminate LLM hallucinations.
Key Learning Outcomes:
- Chunking strategies with parent-document retrievers and hierarchical vector indexing.
- Cross-encoder semantic reranking to boost context precision and recall.
- Building multi-agent state machines with LangGraph for validation loops.
Stack: LangChain, LlamaIndex, ChromaDB, HuggingFace, FastAPI
Computer Vision
Intermediate
3-4 Hours
Real-Time Multi-Object Tracking and Anomaly Detection with YOLOv11 & DeepSORT
Implement high-framerate object detection and multi-camera trajectory tracking on surveillance video feeds using the latest Ultralytics YOLOv11 model coupled with DeepSORT (Kalman filtering and appearance embedding extraction). Add a spatial velocity analytics module to flag anomalous movement patterns such as wrong-way driving, perimeter breaches, and sudden overcrowding.
Key Learning Outcomes:
- Fine-tuning YOLOv11 on custom annotated datasets (COCO format) with Albumentations.
- Object association across frame occlusions using cosine distance embeddings.
- Exporting PyTorch weights to TensorRT and ONNX for 60+ FPS edge execution.
Stack: YOLOv11, OpenCV, PyTorch, DeepSORT, ONNX Runtime
Medical AI & PyTorch
Advanced
6-8 Hours
Medical Image Segmentation for Brain Tumor MRI with 3D U-Net in PyTorch
Construct an automated volumetric segmentation pipeline for multi-modal brain MRI scans (T1, T1Gd, T2, FLAIR) from the BraTS benchmark dataset. Build a custom 3D U-Net with residual skip connections, deep supervision, and generalized Dice-Focal hybrid loss to isolate necrotic cores, peritumoral edema, and active enhancing tumor boundaries.
Key Learning Outcomes:
- Preprocessing 3D NIfTI volumes: skull stripping, z-score intensity normalization, and patch extraction.
- Mitigating class imbalance using compound Dice and Cross-Entropy loss formulations.
- Quantitative evaluation using Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance.
Stack: PyTorch, MONAI, SimpleITK, Nibabel, Matplotlib
FinTech & RL
Advanced
5-6 Hours
Algorithmic Trading & Portfolio Optimization with Deep Reinforcement Learning (PPO)
Create a custom OpenAI Gymnasium trading environment that streams historical tick-by-tick equity and crypto data with realistic transaction slippage and commission fees. Train a Proximal Policy Optimization (PPO) agent with custom Sharpe-ratio reward formulations, comparing risk-adjusted returns against standard Buy & Hold and Markowitz Mean-Variance baselines.
Key Learning Outcomes:
- Building custom Gym environments with continuous and discrete action spaces.
- Designing multi-feature state representations (RSI, MACD, Bollinger Bands, Order Book Imbalance).
- Evaluating backtests with Maximum Drawdown, Calmar Ratio, and Monte Carlo stress tests.
Stack: Gymnasium, Stable-Baselines3, Polars, TA-Lib, Backtrader
Cybersecurity & GNN
Advanced
5 Hours
Real-Time Network Intrusion Detection with Graph Neural Networks (PyG)
Model network packet traffic as directed graph topologies where IP addresses serve as nodes and communication flows serve as attributed edges on the CSE-CIC-IDS2018 dataset. Train Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) in PyTorch Geometric to detect distributed Denial of Service (DDoS) and lateral movement attacks with high precision.
Key Learning Outcomes:
- Converting tabular network flow captures into relational PyG Graph Data objects.
- Implementing multi-head attention mechanisms to weight suspicious IP edge interactions.
- Comparing GNN performance against Random Forests, XGBoost, and MLP baselines.
Stack: PyTorch Geometric, NetworkX, Scikit-Learn, Scapy, Pandas
NLP & Multimodal AI
Intermediate
4 Hours
Multimodal Emotion and Sentiment Recognition from Video & Audio with Transformers
Build an end-to-end multimodal classification system that ingests interview videos and extracts synchronized text transcripts (via Whisper), acoustic pitch and spectrogram features (via Wav2Vec 2.0), and facial expression dynamics (via ViT). Fuse cross-attention embeddings using a transformer fusion layer to classify subtle emotional states with 91%+ accuracy.
Key Learning Outcomes:
- Feature extraction pipelines for text tokens, MFCC acoustic frames, and visual bounding boxes.
- Cross-modal attention fusion mechanisms to align temporal speech and facial cues.
- Evaluating models with weighted F1-scores and confusion matrices on the CMU-MOSEI dataset.
Stack: PyTorch, Hugging Face Transformers, Torchaudio, Librosa, OpenCV
Edge AI & Vision
Beginner
2-3 Hours
Automated Plant Leaf Disease Diagnosis with Edge AI Deployment using FastAPI & ONNX
Train an EfficientNet-V2 convolutional architecture on 54,000+ leaf images from the PlantVillage dataset to classify 38 distinct crop disease categories. Quantize the trained PyTorch model into ONNX format with 8-bit integer quantization (INT8) and deploy an asynchronous FastAPI prediction endpoint with interactive Streamlit UI.
Key Learning Outcomes:
- Transfer learning, learning rate warmups, and cosine annealing schedulers in PyTorch.
- Model size reduction by 75% using ONNX Runtime quantization without loss of accuracy.
- Building production REST endpoints with Pydantic schema validation and Swagger docs.
Stack: PyTorch, EfficientNet, ONNX Runtime, FastAPI, Streamlit
Robotics & Control
Advanced
6 Hours
Autonomous Vehicle Trajectory Planning & Obstacle Avoidance using MPC & Hybrid A*
Implement a complete kinematic and dynamic motion planning stack for autonomous ground vehicles. Use a kinodynamic Hybrid A* pathfinder to compute continuous collision-free dubins curves through cluttered obstacle fields, followed by a non-linear Model Predictive Controller (MPC) formulated in CasADi to track the generated path while obeying tire steering and acceleration constraints.
Key Learning Outcomes:
- Bicycle kinematic model formulation with non-holonomic vehicle constraints.
- Formulating constrained quadratic optimization problems (QP) using CasADi and OSQP.
- Visualizing real-time tracking trajectories and tracking error metrics with Pygame and Matplotlib.
Stack: Python, CasADi, OSQP, NumPy, SciPy, Matplotlib
Time Series & Energy
Intermediate
4 Hours
Renewable Grid Load & Solar Forecasting with Temporal Fusion Transformers (TFT)
Build an interpretable multi-horizon time series forecasting model using Temporal Fusion Transformers on real-world electricity consumption and weather irradiance datasets. Handle static metadata (location, station capacity), known future inputs (calendar days, forecasted solar angles), and past observed loads to output calibrated quantile forecasts (P10, P50, P90).
Key Learning Outcomes:
- Multi-horizon probabilistic forecasting and quantile loss metric calculations.
- Self-attention weight extraction to interpret feature importance across forecasting horizons.
- Comparing TFT against ARIMA, Prophet, and LSTM baselines via Mean Absolute Scaled Error (MASE).
Stack: PyTorch Forecasting, PyTorch Lightning, Polars, Plotly
Speech AI & Audio
Advanced
5 Hours
Zero-Shot Voice Cloning & Low-Latency Speech Synthesis with Whisper & FastSpeech2
Develop an end-to-end zero-shot neural voice cloning pipeline. Extract speaker acoustic identity vectors from a 5-second reference audio sample using a pre-trained speaker encoder, synthesize phoneme duration-controlled Mel-spectrograms via FastSpeech2, and reconstruct natural audio waveforms using HiFi-GAN vocoders.
Key Learning Outcomes:
- Audio signal representation: Short-Time Fourier Transforms (STFT), Mel-scale filters, and vocoders.
- Non-autoregressive speech synthesis for deterministic inference speeds without recurrent latency.
- Measuring subjective Mean Opinion Score (MOS) and objective Mel-Cepstral Distortion (MCD).
Stack: PyTorch, Torchaudio, FastSpeech2, HiFi-GAN, Librosa
Bioinformatics & GNN
Advanced
6 Hours
Molecular Property Prediction & Drug-Target Interaction with Graph Convolutional Networks
Build a molecular graph neural network using RDKit and PyTorch Geometric to predict biochemical properties (solubility, toxicity, and blood-brain barrier permeability) from SMILES chemical strings on the MoleculeNet benchmark. Implement Graph Isomorphism Networks (GIN) with edge features representing chemical bond orders and atomic hybridization states.
Key Learning Outcomes:
- Parsing SMILES strings into molecular graphs with atom and bond feature vectors.
- Weisfeiler-Lehman graph isomorphism tests and message-passing pooling layers.
- Evaluating ROC-AUC scores for multi-task quantitative structure-activity relationship (QSAR) models.
Stack: RDKit, PyTorch Geometric, DeepChem, Scikit-Learn, Pandas
Recommendation Systems
Intermediate
4 Hours
High-Throughput E-Commerce Recommendation Engine using Two-Tower PyTorch Models & Redis
Design a two-tower deep retrieval and ranking recommendation architecture on the MovieLens-25M dataset. Train user and item embedding encoders in PyTorch using in-batch negative sampling and cross-entropy loss. Index item embeddings inside Redis Vector Search for sub-10ms Approximate Nearest Neighbor (ANN) candidate retrieval.
Key Learning Outcomes:
- Two-tower neural architecture: separate query (user context) and candidate (item metadata) towers.
- Sub-millisecond vector similarity search using HNSW indexes in Redis.
- Evaluating Recall@K, NDCG@K, and Mean Reciprocal Rank (MRR) metrics.
Stack: PyTorch, Redis, Polars, Scikit-Learn, FastAPI
FinTech & Anomaly Detection
Intermediate
3-4 Hours
Real-Time Credit Card Fraud Detection with Isolation Forests & Autoencoders
Tackle severe class imbalance (0.17% fraud rate) on European credit card transaction data. Implement unsupervised deep autoencoders to learn latent representations of legitimate spending patterns, flagging transactions with high reconstruction loss errors. Combine autoencoder embeddings with an ensemble of Isolation Forests and LightGBM for robust classification.
Key Learning Outcomes:
- Data preprocessing under extreme class imbalance with SMOTE and focal loss.
- Unsupervised anomaly detection via autoencoder reconstruction error thresholds.
- Optimizing Precision-Recall AUC (PR-AUC) and minimizing false positive customer declines.
Stack: PyTorch, Scikit-Learn, LightGBM, Imbalanced-Learn, Matplotlib
Data Science & XAI
Beginner
2 Hours
Customer Churn Prediction and Explainable AI (XAI) using XGBoost & SHAP
Predict subscription customer churn on telecom datasets using an optimized gradient-boosted tree model (XGBoost). Leverage SHAP (Shapley Additive Explanations) and LIME to generate individual local customer explanations and global feature impact plots, converting black-box predictions into actionable business retention strategies.
Key Learning Outcomes:
- Automated hyperparameter tuning with Optuna cross-validation loops.
- Game-theoretic feature attribution using TreeSHAP summary and waterfall plots.
- Building an interactive Streamlit customer risk dashboard with decision triggers.
Stack: XGBoost, SHAP, Optuna, Scikit-Learn, Streamlit
Robotics & RL
Advanced
6 Hours
Sim-to-Real Robotic Manipulator Pick-and-Place Control with Gymnasium & Stable-Baselines3
Train a 7-DOF Franka Emika robotic manipulator arm in a PyBullet / MuJoCo physics simulator using Soft Actor-Critic (SAC) reinforcement learning. Implement domain randomization (varying friction coefficients, link masses, and camera noise) to enable robust zero-shot sim-to-real transfer for grasping multi-shaped objects under uncertain lighting.
Key Learning Outcomes:
- Physics simulation modeling in MuJoCo and Gymnasium Robotics environments.
- Continuous action-space policy optimization with Soft Actor-Critic (SAC).
- Domain randomization strategies for resilient real-world hardware deployment.
Stack: Gymnasium, MuJoCo, Stable-Baselines3, PyBullet, PyTorch