Course Syllabus
SEMESTER-i
1. Computer Fundamentals
- Computer basics
- Operating System
- Files, folders, data storage
- Internet & email basics
2. Programming with Python
- Python installation
- Variables, loops, functions
- Error handling
- Modules & packages
3. Mathematics for AI
- Basic algebra
- Statistics (Mean, Median, Mode)
- Probability
- Linear algebra concepts
- Graph interpretation
4. Data Science with Python
- NumPy
- Pandas
- Data cleaning
- Data visualization
- Data preprocessing
5. Machine Learning – I (Fundamentals)
- Introduction to ML
- Supervised vs Unsupervised Learning
- Linear Regression
- Logistic Regression
- KNN Algorithm
SEMESTER-ii
6. Machine Learning – II (Advanced)
- Decision Tree
- Random Forest
- Naïve Bayes
- Clustering (K-Means)
- Model Evaluation, Accuracy Score
7. Deep Learning
- Introduction to Neural Networks
- Perceptron Model
- TensorFlow / Keras basics
- CNN (Image Processing)
- ANN Architecture
8. Generative AI & Modern Tools
- AI Image Generation
- AI Video Generation
- Prompt Engineering
- Automation Tools
9. Projects & Research Work
- Students will complete minimum 3 projects:
- House Price Prediction
- Face Recognition / Image Classifier
- Customer Behaviour Prediction
- Small AI Chatbot
- Sentiment Analysis
10. Career & Industry Skills
- Resume building
- Portfolio creation
- Freelancing basics
- GitHub project uploading
- Interview preparation
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