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

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Course Content

Week 1 • Data Science with Python

W1L1 Feature Engineering P1 Feature Selection

W1L1 Feature Engineering P2 Categorical Features

W1L1 Feature Engineering P3 Correlation, Trend, Heatmap

W1L1 Feature Engineering P4 Feature Pipelines

W1L2 Data wrangling P1 Imputation of missing values

W1L2 Data wrangling P2 Formatting each variable

W1L2 Data wrangling P3 Data transformation/manipulation

Week 2 • Machine Learning Algorithms: Regression

W2L1 Simple Linear Regression (SLR) P1 Theory, assumptions

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

W2L1 Simple Linear Regression (SLR) P3 Prediction/Inference

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

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

W2L2 Naïve Bayes Classification P1 Bayesian Classification

W2L2 Naïve Bayes Classification P2 Gaussian Naïve Bayes

W2L2 Naïve Bayes Classification P3 Multinomial Naïve Bayes

W2L2 Naïve Bayes Classification P4 When to Use Naïve Bayes

Week 3 • Intro to ML with Python continues

W3L1 Decision Trees and Random Forests P1 Decision Trees

W3L1 Decision Trees and Random Forests P2 Motivating Random Forests

W3L1 Decision Trees and Random Forests P3 Random Forest Regression

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

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About Instructor

Nijat Aliyev

4 Courses