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    Quick Facts

    Medium Of InstructionsMode Of LearningMode Of Delivery
    EnglishSelf StudyVideo and Text Based

    Courses and Certificate Fees

    Certificate Availability
    no

    The Syllabus

    • What is Machine Learning (ML) ?
    • ML and Finance; not ML for Finance
    • Classical Machine Learning: Introduction
    • Supervised Learning
    • Our first predictor
    • Notational conventions

    • Linear Regression
    • The Recipe for Machine Learning
    • The Regression Loss Function
    • Bias and Variance

    • Data Transformations: Introduction and mechanics
    • Logistic Regression
    • Non-numeric variables: text, images
    • Multinomial Classification
    • The Classification Loss Function

    • Baseline model
    • The Dummy Variable Trap
    • Transformations
    • Loss functions: mathematics

    • Entropy, Cross Entropy, KL Divergence
    • Decision Trees
    • Naive Bayes
    • Ensembles
    • Feature Importance

    • Support Vector Classifiers
    • Gradient Descent
    • Interpretation: Linear Models

    • Unsupervised Learning
    • Dimensionality Reduction
    • Clustering
    • Principal Components
    • Pseudo Matrix Factorization: preview of Deep Learning

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