Python Programming with AI Syllabus

Phase 1: Python Programming Basics (Beginner Level)

1. Introduction to Python

• Why Python? Real-world examples
• Installing Python and setting up your environment (IDLE, VS Code, Jupyter Notebook)
• Your first “Hello World” program

2. Basic Syntax & Variables

• Variables and data types (int, float, string, boolean)
• Comments and code readability
• Taking user input

3. Operators in Python

• Arithmetic, comparison, logical, assignment
• Real examples like a mini calculator

4. Conditional Statements

• If, else, elif
• Nested conditions
• Real-life use: Decision making in an AI chatbot

5. Loops

• For loop and while loop
• Using range()
• Break, continue and pass

6. Functions

• Defining and calling functions
• Parameters and return values
• Lambda (anonymous) functions

7. Data Structures

• Lists, tuples, sets, dictionaries
• Real examples: Grocery list, contact book

8. String Manipulation

• String slicing, formatting, methods
• Use in NLP (Natural Language Processing) preview

Phase 2: Intermediate Python Programming

9. File Handling

• Reading and writing files (.txt, .csv)
• Example: Writing user data to a file

10. Exception Handling

• Try, except, finally blocks
• Raise errors manually

11. Object-Oriented Programming (OOP)

• Classes and objects
• Constructors (init)
• Inheritance, encapsulation, and polymorphism
• Real-world example: Building a robot class

12. Modules and Libraries

• Importing modules (math, random, datetime)
• Creating your own module

13. Virtual Environments & Pip

• Creating isolated environments
• Installing packages using pip

Phase 3: Python for Data Handling

14. NumPy

• Arrays vs Lists
• Array operations
• Statistics and math functions

15. Pandas

• DataFrames and Series
• Reading CSV, Excel
• Filtering, sorting, grouping data

16. Data Visualization

• Matplotlib: Line plots, bar charts, histograms
• Seaborn: Heatmaps, pair plots

17. Real Project

• Analyzing a real dataset (e.g., Titanic, COVID-19)
• Drawing conclusions

Phase 4: Python for AI (Machine Learning)

18. Intro to AI & ML

• What is AI, ML, Deep Learning – simple explanation
• Types of ML: Supervised, Unsupervised, Reinforcement

19. Scikit-learn Basics

• Train-test split
• Accuracy score, confusion matrix

20. Supervised Learning Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees & Random Forest

21. Unsupervised Learning

• Clustering (K-Means)
• Dimensionality reduction (PCA)

22. Model Evaluation

• Overfitting and underfitting
• Cross-validation
• Grid search

23. Mini ML Projects

• Predict house prices
• Detect spam messages
• Classify handwritten digits (MNIST)

Phase 5: Python for Deep Learning (Using AI Libraries)

24. TensorFlow & Keras Basics

• Installing TensorFlow
• Creating neural networks using Keras
• Activation functions (ReLU, sigmoid)

25. Image Classification

• Using CNN (Convolutional Neural Networks)
• Dataset: CIFAR-10 or Fashion MNIST

26. Natural Language Processing (NLP)

• Tokenization, stemming, lemmatization
• Sentiment analysis
• Building a simple chatbot

27. Advanced Topics (Optional)

• Transfer learning
• Generative AI overview (GANs)
• Object detection basics

Phase 6: Final Projects + Deployment

28. Real AI Projects (Choose 2-3)

• AI Personal Assistant (text-based)
• Movie recommendation system
• Image classifier app

29. Web + AI (Bonus)

• Flask basics for web apps
• Integrate ML model with Flask

30. Deployment

• Exporting models using pickle or joblib
• Hosting on platforms like Streamlit, Heroku