Course Curriculum

Module 1: Introduction to AI & Machine Learning

  • What is Artificial Intelligence?
  • History and Evolution of AI
  • Types of Machine Learning
  • AI Applications in Real World

Module 2: Python Programming for AI

  • Python Fundamentals
  • NumPy and Pandas Libraries
  • Data Manipulation Techniques
  • Visualization with Matplotlib

Module 3: Supervised Learning Algorithms

  • Linear Regression Models
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines

Module 4: Unsupervised Learning

  • Clustering Algorithms (K-Means, DBSCAN)
  • Dimensionality Reduction (PCA)
  • Association Rule Learning
  • Anomaly Detection

Module 5: Neural Networks & Deep Learning

  • Introduction to Neural Networks
  • Activation Functions
  • Backpropagation Algorithm
  • Building Deep Learning Models

Module 6: Model Evaluation & Optimization

  • Training, Validation, and Testing
  • Cross-Validation Techniques
  • Hyperparameter Tuning
  • Overfitting and Underfitting

Module 7: Real-World AI Projects

  • Image Classification Project
  • Sentiment Analysis Application
  • Predictive Analytics Case Study
  • Final Capstone Project