Module 3 • Machine Learning with Python — ICARUS AI

Dr. Nijat Aliyev

Module 3 • Machine Learning with Python

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Course Details

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Dr. Nijat Aliyev

Profile Courses

Lessons

W1L1 Machine Learning background P1 Supervised, Unsupervised, Semisupervised, Reinforcement learning

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W1L1 Machine Learning background P2 Regression vs Classification

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W1L1 Machine Learning background P3 Train-Validation-Test splits

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W1L1 Machine Learning background P4 Loss functions

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W1L1 Machine Learning background P5 Bias-Variance Tradeoff (Overfitting, Underfitting)

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W1L1 Machine Learning background P6 Cross-validation

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W1L2 KNN (first non-parametric algorithm) P1 KNN classification

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W1L2 KNN (first non-parametric algorithm) P2 KNN regression

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W1L2 KNN (first non-parametric algorithm) P3 KNN package

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W1L2 KNN (first non-parametric algorithm) P4 Choosing k via cross-validation

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W1L2 KNN (first non-parametric algorithm) P5 Comparison with logistic regression or MLR

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W1L2 KNN (first non-parametric algorithm) P6 Confusion matrices, Accuracy

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W2L1 Theory P1 Bias-variance tradeoff

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W2L1 Theory P2 Regularization controlling variance

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W2L1 Theory P3 Loss function modification

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W2L1 Theory P4 Types of regularization (l1, l2, mixed)

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W2L1 Theory P5 Ridge regression

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W2L2 LASSO, Elastic-Net regressions P1 Ridge vs LASSO

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W2L2 LASSO, Elastic-Net regressions P2 Combination of Ridge and LASSO

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W2L2 LASSO, Elastic-Net regressions P3 Choosing the regularization parameter

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W3L1 Unsupervised Learning: K-means (clustering) P1 Clustering, hyper-parameter selection

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W3L1 Unsupervised Learning: K-means (clustering) P2 Local vs Global minimum

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W3L1 Unsupervised Learning: K-means (clustering) P3 Other clustering algorithms

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