PGCP-AI Course Vision & Eligibility
The Post Graduate Certificate Programme in Artificial Intelligence (PGCP-AI) (Course ID: 30, formerly PG-DAI) is designed to cultivate skilled machine learning and AI practitioners.
Spanning a 24-week full-time curriculum, the course balances mathematical fundamentals with production deployment of deep neural networks and computer vision models.
Module Breakdown & Focus Areas
| Module Code | Topic Title | Curriculum Scope |
|---|---|---|
| MOD-01 | Mathematics & Statistics for AI | Linear Algebra, Multivariate Calculus, Probability Distributions |
| MOD-02 | Advanced Programming for AI | Python Programming, NumPy, Pandas, Data Manipulation |
| MOD-03 | Data Analytics | Exploratory Data Analysis, Visualization, SQL Analytics |
| MOD-04 | Practical Machine Learning | Supervised & Unsupervised Learning, Ensemble Methods |
| MOD-05 | Deep Neural Networks | Perceptrons, Backpropagation, CNNs, RNNs, PyTorch |
| MOD-06 | Natural Language Processing & Computer Vision | Text Tokenization, Transformers, Object Detection, YOLO |
| MOD-07 | AI Compute Platforms & MLOps | Model Serving, Containerization, Cloud AI, Pipeline Automation |
| MOD-08 | Aptitude & Communication | Quantitative Reasoning, Technical Presentations |
| MOD-09 | Capstone AI Project | End-to-End AI Engineering & Viva Voce |
Mathematics for AI & Advanced Python
1. Mathematical Foundations
- Linear Algebra: Vectors, matrices, dot products, eigenvalues, eigenvectors, matrix decompositions
- Calculus & Optimization: Partial derivatives, gradients, chain rule, gradient descent optimization algorithms
- Probability & Statistics: Bayes' theorem, probability distributions, hypothesis testing, variance and covariance
2. Advanced Python for AI
- Object-oriented Python, decorators, generators, data pipelines
- NumPy array vectorization, broadcasting, and tensor operations
- Pandas data frame transformations, time-series analysis, data cleaning
- Data visualization using Matplotlib, Seaborn, and modern plotting libraries
Machine Learning & Deep Neural Networks
- Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, Gradient Boosted Trees (XGBoost, LightGBM)
- Unsupervised Learning: Clustering algorithms (K-Means, DBSCAN), Dimensionality reduction (PCA)
- Deep Learning Frameworks: Multi-Layer Perceptrons, loss functions, optimizers (Adam, SGD), regularizations
- Convolutional Neural Networks (CNNs): Feature extraction, pooling, transfer learning with modern architectures
NLP, Computer Vision & Generative AI
- Natural Language Processing: Text pre-processing, word embeddings, sequence modeling, attention mechanisms
- Transformer Architectures: Self-attention, BERT, sequence-to-sequence modeling, modern LLM workflows
- Computer Vision: Image classification, object localization, bounding box detection, semantic segmentation
- Deployment & MLOps: Packaging models with REST APIs, containerized deployment, performance monitoring
❓ Frequently Asked Questions
Is there a minimum degree percentage required for PGCP-AI?
Yes, candidates typically require a qualifying degree with at least 60% aggregate marks to be eligible for PGCP-AI (Artificial Intelligence).
What is the duration of the PGCP-AI course?
PGCP-AI is conducted as a 24-week full-time intensive programme.
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Topics:#PGCP-AI#Artificial Intelligence#Machine Learning#Deep Learning#NLP#Python