Module 2 • Data Science and Introduction to Machine Learning with Python — ICARUS AI

Dr. Nijat Aliyev

Module 2 • Data Science and Introduction to Machine Learning with Python

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Course Details

Contact course teacher for more information

Dr. Nijat Aliyev

Profile Courses

Lessons

W1L1 Feature Engineering P1 Feature Selection

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W1L1 Feature Engineering P2 Categorical Features

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W1L1 Feature Engineering P3 Correlation, Trend, Heatmap

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W1L1 Feature Engineering P4 Feature Pipelines

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W1L2 Data wrangling P1 Imputation of missing values

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W1L2 Data wrangling P2 Formatting each variable

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W1L2 Data wrangling P3 Data transformation/manipulation

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W2L1 Simple Linear Regression (SLR) P1 Theory, assumptions

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W2L1 Simple Linear Regression (SLR) P2 Error analysis/Diagnostics

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W2L1 Simple Linear Regression (SLR) P3 Prediction/Inference

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W2L1 Simple Linear Regression (SLR) P4 SLR with variable transformation

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W2L1 Simple Linear Regression (SLR) P5 Performance measures: R squared, Adjusted R square, SSE, MSE, MAE

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W2L2 Naïve Bayes Classification P1 Bayesian Classification

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W2L2 Naïve Bayes Classification P2 Gaussian Naïve Bayes

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W2L2 Naïve Bayes Classification P3 Multinomial Naïve Bayes

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W2L2 Naïve Bayes Classification P4 When to Use Naïve Bayes

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W3L1 Decision Trees and Random Forests P1 Decision Trees

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W3L1 Decision Trees and Random Forests P2 Motivating Random Forests

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W3L1 Decision Trees and Random Forests P3 Random Forest Regression

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W3L1 Decision Trees and Random Forests P4 Example: Random Forest for Classifying Digits

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W3L2 Logistic Regression P1 Simple Logistic Regression

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W3L2 Logistic Regression P2 Theory, Activation function(s), probabilities

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W3L2 Logistic Regression P3 Predictions and Inference

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W3L2 Logistic Regression P4 Multiple Logistic Regression

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W3L2 Logistic Regression P5 Polynomial Logistic Regression

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W3L2 Logistic Regression P6 Performance measures: AIC, BIC, Deviance, Cross-Entropy, Accuracy

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