Program Overview
Step beyond basic Jupyter notebooks. Learn practical data science, feature engineering, classical ML models, deep learning with PyTorch, and deploying models as production REST APIs.
Who Can Apply
What You Will Master
Curriculum & Roadmap
Progressive milestones designed to advance you from core foundational concepts to production-grade deployment.
Mathematics, Data Wrangling & Feature Engineering
Perform clean data preparation, statistical validation, and feature scaling.
- NumPy array operations and linear algebra basics
- Pandas data manipulation and missing data imputation
- Feature encoding, normalization, and scaling
- Data visualization with Matplotlib & Seaborn
Classical Machine Learning with Scikit-Learn
Build, tune, and evaluate regression and classification models.
- Linear/Logistic regression and regularization (L1/L2)
- Decision Trees, Random Forests, and Gradient Boosting (XGBoost)
- Model evaluation metrics: ROC-AUC, F1-score, Confusion Matrix
- Hyperparameter tuning with GridSearchCV
Deep Learning Foundations with PyTorch
Build multi-layer perceptrons and training loops from scratch.
- Tensors, autograd, and GPU acceleration
- Building Neural Networks with torch.nn
- Loss functions, optimizers (Adam, SGD), and backprop
- Overfitting prevention: Dropout, Early Stopping
Applied NLP & Transformer Models
Leverage modern pre-trained models for text classification and summarization.
- Text preprocessing and tokenization
- Word embeddings and semantic search
- Fine-tuning Hugging Face Transformers
- Evaluating language model outputs
Model Serving & Inference API Development
Turn trained weights into a low-latency production API endpoint.
- Model serialization with ONNX and joblib
- FastAPI inference endpoint with batching
- Containerizing ML models with Docker
- Tracking experiments with MLflow
Model Evaluation, Defense & Certificate Issuance
Present model accuracy, latency trade-offs, and complete senior evaluation.
- Model drift monitoring concepts
- Production documentation and architecture defense
- Evaluation review by AI practitioners
- Certificate validation
What You Will Build
Customer Churn Prediction & Risk Scoring Pipeline
An end-to-end classification pipeline with feature importance analysis, threshold tuning, and real-time inference API.
Intelligent Support Ticket Classifier (NLP)
A multi-class text classification system using fine-tuned DistilBERT to categorize and route incoming support requests.
Computer Vision Defect Detection System
A convolutional neural network model trained to classify manufacturing surface anomalies with high precision.
Performance Evaluation & Standards
Submissions are systematically checked by engineering reviewers. You receive written feedback and numerical rubric scores covering modularity, test quality, Git conventions, and deployment integrity.
Configurable fee covering curriculum, project reviews, and credential verification.
Refund Policy: See our transparent terms on our Refund & Cancellation page.
Eligibility: Students & freshers with relevant domain interest.
Reach out to our admissions team at birlasolutions.in@gmail.com.