AI & ML Mastery Topics

1. Introduction to AI & ML

   • What is AI?
   • What is Machine Learning?
   • History and Evolution of AI & ML

2. Mathematics for AI & ML

   • Linear Algebra
   • Calculus
   • Probability and Statistics

3. Python for AI & ML

   • Python Basics
   • Libraries: NumPy, Pandas, Matplotlib, Seaborn

4. Data Preprocessing

   • Data Cleaning
   • Data Transformation
   • Feature Engineering

5. Supervised Learning

   • Regression Algorithms
   • Classification Algorithms
   • Evaluation Metrics

6. Unsupervised Learning

   • Clustering Algorithms
   • Dimensionality Reduction
   • Anomaly Detection

7. Neural Networks and Deep Learning

   • Introduction to Neural Networks
   • Deep Learning Frameworks: TensorFlow, Keras, PyTorch
   • Convolutional Neural Networks (CNNs)
   • Recurrent Neural Networks (RNNs)

8. Natural Language Processing (NLP)

   • Text Preprocessing
   • Sentiment Analysis
   • Language Models

9. Reinforcement Learning

   • Markov Decision Processes
   • Q-Learning
   • Deep Q-Networks

10. Model Deployment and Production

    • Model Serving
    • APIs for ML Models
    • Monitoring and Maintenance

11. Ethics and Bias in AI

    • Understanding Bias
    • Ethical AI Practices
    • Fairness in AI

12. Capstone Projects

    • Real-world Applications
    • End-to-End ML Projects
    • Research and Innovation

Happy Learning!