Artificial Intelligence and Machine Learning - Priyavardhini S R

📌 Module 1: Introduction to AI & Machine Learning

• What is Artificial Intelligence?

• Types of AI (Weak AI, Strong AI, Super AI)

• What is Machine Learning?

• Differences between AI, ML & DL

• Real-time use cases across industries

📌 Module 2: Python for AI & ML

• Python basics & syntax

• Variables, loops, functions

• Libraries for AI/ML

  • NumPy

  • Pandas

  • Matplotlib

  • Scikit-learn

• Data visualization essentials

📌 Module 3: Data Handling & Preprocessing

• Importing datasets

• Cleaning and transforming data

• Handling missing values

• Encoding categorical variables

• Feature scaling & selection

• Train-test split

📌 Module 4: Core Machine Learning Algorithms

Supervised Learning

• Linear & Logistic Regression

• Decision Trees & Random Forest

• KNN

• Naive Bayes

• Support Vector Machine

Unsupervised Learning

• K-means clustering

• Hierarchical clustering

• PCA (Dimensionality reduction)

📌 Module 5: Model Evaluation

• Confusion matrix

• Accuracy, precision, recall

• F1 score

• ROC & AUC

• Overfitting vs underfitting

📌 Module 6: Artificial Intelligence Foundations

• Search algorithms (BFS, DFS)

• Game playing & heuristics

• Intelligent agents

• Knowledge representation

• Rule-based systems

📌 Module 7: AI with Python

• Integrating Python with AI workflows

• Building intelligent systems

• Automating ML tasks with Python

• Introduction to TensorFlow basics

• Neural networks overview

📌 Module 8: Mini Projects

Hands-on project experience:

• Spam email classifier

• Customer segmentation

• Movie recommendation system

• House price prediction

📌 Module 9: Real-World Industry Applications

• AI in healthcare

• AI in robotics

• AI in finance

• AI in e-commerce

• AI in automation & IoT

📌 Module 10: Capstone Project

End-to-end AI/ML project including:

• Dataset selection

• Data preprocessing

• Model development

• Evaluation metrics

• Working demo explanation