Python & AI Courses

PART A: Python Programming (30 hours)

Module 1: Python Basics (6 hours)

·       Introduction to Python and setup (Anaconda, Jupyter, VS Code)

·       Syntax, variables, and data types

·       Operators and expressions

·       Input/output functions

·       Comments and indentation

Module 2: Control Structures (4 hours)

·       Conditional statements (if, elif, else)

·       Loops: for, while

·       Loop control statements: break, continue, pass

Module 3: Data Structures (6 hours)

·       Strings, Lists, Tuples, Sets

·       Dictionaries and their operations

·       Comprehensions (list, dict, set)

Module 4: Functions & Modules (4 hours)

·       Defining and calling functions

·       Arguments, return values

·       Lambda and map/filter/reduce

·       Importing built-in and custom modules

Module 5: File Handling & Exceptions (4 hours)

·       Reading/writing text and CSV files

·       Working with file paths

·       Try/except/finally

·       Raising exceptions

Module 6: Object-Oriented Programming (6 hours)

·       Classes and objects

·       Constructors, attributes, methods

·       Inheritance and polymorphism

·       init, str, super()

PART B: AI & Machine Learning Basics (30 hours)

Module 7: Introduction to AI & ML (3 hours)

·       What is AI, ML, DL?

·       Real-world use cases

·       Types of ML: Supervised, Unsupervised, Reinforcement Learning

·       AI vs ML vs DL

Module 8: NumPy & Pandas (6 hours)

·       Introduction to NumPy arrays and operations

·       Pandas DataFrames and Series

·       Data cleaning and manipulation

·       Filtering, grouping, merging datasets

Module 9: Data Visualization (3 hours)

·       Using matplotlib and seaborn

·       Plot types: line, bar, pie, histogram, scatter

·       Customizing plots

Module 10: Supervised Learning (6 hours)

·       Train-test split and model evaluation (accuracy, precision, recall)

·       Linear regression

·       Logistic regression

·       Classification: k-NN, Decision Trees

·       Hands-on using scikit-learn

Module 11: Unsupervised Learning (4 hours)

·       Clustering: k-Means, Hierarchical

·       Dimensionality reduction: PCA

·       Hands-on examples

Module 12: Basic NLP & Chatbots (4 hours)

·       Tokenization, Stopwords, Stemming, Lemmatization

·       Word frequency (Bag of Words)

·       Introduction to simple chatbot logic using rule-based or ML techniques

Module 13: Capstone Project (4 hours)

Choose one mini project:

·       AI-based movie recommender

·       Email spam classifier

·       Student performance predictor

·       Rule-based chatbot

·       Weather app with visualization