Machine Learning

Machine Learning

Machine Learning

Lessons

    • What is Artificial Intelligence
    • History of Artificial Intelligence
    • Foundations of Artificial Intelligence
      • Philosophy
      • Mathematics
      • Phycology
      • Neuroscience
      • Computer Engineering
    • Overview of Machine Learning
    • Types of Machine Learning
    • Applications of Machine Learning
    • Steps in developing a Machine Learning Application
    • AI Frameworks - Tensorflow
    • Linear Regression
    • Logistic Regression
    • Vectors
    • K-Mean
    • Probability
    • NumPy
    • SciPy
    • Maximum Margin Linear Separators
    • Quadratic Programming solution to finding maximum margin separators
    • Kernels for learning non-linear functions.
    • Using Decision Trees
    • Constructing Decision Trees,
    • Classification and Regression Trees (CART)
    • K-means clustering
    • Hierarchical clustering
      • Agglomerative clustering
      • Divisive clustering
    • Expectation Maximization Algorithm
    • Supervised learning after clustering
    • Radial Basis functions
    • Dimensionality Reduction Techniques
    • Principal Component Analysis
    • Independent Component Analysis
    • Introduction
    • Elements of Reinforcement Learning
    • Model based learning
    • Temporal Difference Learning
    • Generalization
    • Partially Observable States
    • Q-Learning

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