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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!

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