📍 Module 1: Introduction to Artificial Intelligence

• What is AI?

• AI vs ML vs Deep Learning

• Applications of AI in real life

• AI Domains: NLP, Computer Vision, Expert Systems, Robotics

• Tools and Libraries Overview

📍 Module 2: Python Essentials for AI

• Python Basics: Variables, Data Types, Operators

• Conditional Statements & Loops

• Functions & Lambda

• Object-Oriented Programming

• File Handling

• Working with Libraries: NumPy, Pandas, Matplotlib, Seaborn

📍 Module 3: Data Handling & Preprocessing

• Data Collection & Cleaning

• Handling Missing Values

• Encoding Categorical Data

• Feature Scaling & Normalization

• Data Visualization Techniques

• Exploratory Data Analysis (EDA)

📍 Module 4: Introduction to Machine Learning

• Supervised vs Unsupervised Learning

• Algorithms Overview: Regression, Classification, Clustering

• Model Building Steps

• Splitting Data: Train/Test

• Evaluation Metrics: Accuracy, Confusion Matrix, Precision, Recall, F1 Score

📍 Module 5: Machine Learning with Scikit-Learn

• Linear & Logistic Regression

• K-Nearest Neighbors (KNN)

• Decision Trees & Random Forests

• Naive Bayes Classifier

• K-Means Clustering

• Model Tuning: Cross Validation, Hyperparameter Tuning

📍 Module 6: Introduction to Neural Networks

• Biological vs Artificial Neural Networks

• Perceptron & Multi-layer Perceptron

• Forward & Backward Propagation

• Activation Functions: ReLU, Sigmoid, Tanh

📍 Module 7: Deep Learning with TensorFlow & Keras

• Setting up TensorFlow and Keras

• Building and Training Neural Networks

• Optimizers, Loss Functions & Epochs

• Convolutional Neural Networks (CNN) – Basics

• Recurrent Neural Networks (RNN) – Overview

📍 Module 8: Natural Language Processing (NLP)

• What is NLP?

• Text Preprocessing: Tokenization, Stemming, Lemmatization

• Bag of Words & TF-IDF

• Sentiment Analysis

• Chatbot Development (Mini Project)

📍 Module 9: Computer Vision (Overview)

• Basics of Image Processing

• OpenCV Introduction

• Face Detection & Object Recognition Concepts

📍 Module 10: AI Project Development

• End-to-End AI Project using Real Dataset

• Data Cleaning + Model Building + Evaluation

• Deployment of AI Model

• Project Documentation