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Detailed Syllabus & Weekly Breakdown

Term 1: Foundations of Python, Data Science & ML (Weeks 1–12)

Focus: Building a strong foundation in programming and machine learning fundamentals.

• Advanced Python for Data Science (NumPy, Pandas).
• Data Visualization and Exploratory Data Analysis (Matplotlib, Seaborn).
• Supervised Learning: Regression & Classification Models.
• Unsupervised Learning: Clustering & Dimensionality Reduction.

Term 2: Advanced Machine Learning & Deep Learning (Weeks 13–24)

Focus: Mastering neural networks and advanced ML techniques.

• Ensemble Methods (Bagging, Boosting, Stacking).
• Introduction to Deep Learning with TensorFlow & Keras.
• Artificial Neural Networks (ANN) & Convolutional Neural Networks (CNN).
• Recurrent Neural Networks (RNN) & Long Short-Term Memory (LSTM).

Term 3: Specializations (NLP & Computer Vision) (Weeks 25–36)

Focus: Applying deep learning to real-world text and image data.

• Natural Language Processing (NLP) with NLTK & SpaCy.
• Advanced NLP: Transformers, BERT, and GPT models.
• Computer Vision with OpenCV.
• Advanced Computer Vision: Object Detection, Image Segmentation.

Term 4: MLOps, Deployment & Capstone Project (Weeks 37–48)

Focus: Deploying models into production and solving a large-scale industry problem.

• Machine Learning Operations (MLOps) Principles.
• Model Deployment with Docker, Flask/FastAPI.
• Cloud Deployment on AWS/GCP.
• Industry-grade Capstone Project.

Comprehensive Learning Outcomes

• Design, build, and deploy advanced deep learning models for NLP and Computer Vision.
• Master the end-to-end MLOps lifecycle from development to production.
• Develop a portfolio of complex, industry-relevant AI projects.

Assessment Weightage

Assessment Type	Weightage	Focus Area
Term-End Projects	40%	Hands-on projects at the end of each term.
Quizzes & Assignments	20%	Continuous evaluation of concepts.
Final Capstone Project	40%	Demonstration of end-to-end AI project execution.

The "WhiteCollar" Career Advantage

This Master Program goes beyond theoretical knowledge, focusing on building production-grade AI systems. Graduates will be equipped with the skills to lead AI initiatives, architect complex models, and drive innovation in top tech companies and research labs.