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

icarusai_banner

Course Details

Contact course teacher for more information

Dr. Nijat Aliyev

Profile Courses

Lessons

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

..

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

..

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

..