Setting up Python environment (Anaconda, Jupyter Notebook)

Python basics: variables, data types, loops, conditionals

Functions, modules, and error handling
Practical : Write Python scripts for basic calculations and data manipulation

Introduction to NumPy and Pandas

Data cleaning, handling missing values

Data visualization using Matplotlib & Seaborn
Practical : Analyze and visualize a dataset (like sales or student scores)

Vectors, matrices, and basic operations

Probability basics, distributions, Bayes theorem

Importance in AI algorithms
Practical : Implement simple vector operations and probability calculations in Python

What is Machine Learning vs AI

Types: Supervised, Unsupervised, Reinforcement Learning

Applications of ML in real life
Practical : Build a simple linear regression model to predict numerical data

Regression (Linear, Polynomial)

Classification (Logistic Regression, K-Nearest Neighbors)

Model evaluation (Accuracy, Precision, Recall)
Practical : Build a classifier (e.g., iris dataset or loan prediction)

Clustering (K-Means, Hierarchical)

Dimensionality reduction (PCA)

Use cases of clustering in real-world datasets
Practical : Group data points (e.g., customer segmentation)

Introduction to neural networks

Perceptron, activation functions, forward/backpropagation

Deep learning basics with TensorFlow/Keras
Practical : Build a simple neural network to classify handwritten digits (MNIST)

Text preprocessing: tokenization, stemming, lemmatization

Bag-of-Words, TF-IDF

Simple NLP models for sentiment analysis
Practical : Build a sentiment analysis model on a movie review dataset

Project planning and best practices

Building a mini AI application (e.g., digit recognizer, chatbot, or predictive model)

Optional: Deploy AI model using Streamlit or Flask
Final Project : Students build and showcase a working AI application