Description
DURATION 11 MONTHS
| Paper 01 |
Communicative English |
| Paper 02 |
Foundations of Artificial Intelligence |
| Paper 03 |
Mathematics for AI & ML |
| Paper 04 |
Core Machine Learning |
| Paper 05 |
Deep Learning and Neural Networks |
| Paper 06 |
Natural Language Processing (NLP) |
| Paper 07 |
AI in Real-World Applications |
| Paper 08 |
Ethics, Governance, and Future of AI |
| Paper 09 |
Machine Learning Lab (Practical) |
| Paper 10 |
Deep Learning & AI Lab (Practical) |
Paper 01: Communicative English
- Focuses on developing Practical language skills
- Development of listening ,speaking,& writing skill in real-life situations
Paper 02: Foundations of Artificial Intelligence
- What is AI? Definitions and scope
- History and evolution of AI
- Types of AI (Narrow, General, Super AI)
- Intelligent agents and environments
- AI problem solving: Search algorithms, State-space
- Applications of AI in different sectors
Paper 03: Mathematics for AI & ML
- Linear Algebra: Vectors, Matrices, Eigenvalues
- Probability and Statistics: Distributions, Bayes Theorem
- Calculus: Derivatives, Gradients (used in optimization)
- Optimization techniques in ML
- Random variables and Markov decision processes
Paper 04: Core Machine Learning
- Supervised learning: Regression, Classification
- Unsupervised learning: Clustering, Dimensionality Reduction
- Model selection and evaluation
- Feature engineering and preprocessing
- Algorithms: KNN, SVM, Naive Bayes, Decision Trees, Random Forest
- Ensemble learning: Bagging, Boosting
Paper 05: Deep Learning and Neural Networks
- Neural network architecture
- Forward and backward propagation
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs), LSTM
- Transfer Learning and Fine-tuning
- Use cases: Vision, Speech, NLP
Paper 06: Natural Language Processing (NLP)
- Text processing and feature extraction (TF-IDF, word embeddings)
- Sentiment analysis, text classification
- Language models and tokenization
- Transformers and BERT basics
- Chatbots and question answering systems
Paper 07: AI in Real-World Applications
- AI in healthcare, finance, manufacturing, marketing
- Robotics and autonomous systems
- AI for cybersecurity
- AI in education and smart cities
- Industry use cases and success stories
Paper 08: Ethics, Governance, and Future of AI
- Ethics in AI and responsible AI design
- Bias and discrimination in AI systems
- Data privacy and legal considerations
- Explainable AI (XAI)
- AI governance and regulation
- The future of AI: Risks, promises, and job impact
PRACTICAL PAPERS
Paper 09: Machine Learning Lab
- Python and Jupyter Notebook usage
- Data loading, cleaning, and visualization (Pandas, Matplotlib)
- Building and tuning ML models using Scikit-learn
- Model deployment using Flask or Streamlit
- Mini projects: Fraud detection, recommendation system
Paper 10: Deep Learning & AI Lab
- Deep learning frameworks: TensorFlow/Keras
- Building CNNs for image classification
- Creating RNNs for time series or text data
- NLP hands-on with HuggingFace/Transformers
- Final capstone project (student’s choice or provided use case)
