Machine Learning - 기초 과정

 

 

✏️ Course created by Tatev Karen Aslanyan.

More from Tatev here: https://lunartech.ai/

 

Colab Code: https://colab.research.google.com/drive/16HdFVhvRq-DEmNU61Qp8YXMTA3CxUmg-?usp=sharing

 

Contents

⌨️ (0:00:00) Introduction

⌨️ (0:03:13) Machine Learning Roadmap for 2024

⌨️ (0:10:39) Must Have Skill Set for Career in Machine Learning

⌨️ (0:38:54) Machine Learning Common Career Paths

⌨️ (0:45:48) Machine Learning Basics

⌨️ (1:00:59) Bias-Variance Trade-Off

⌨️ (1:08:04) Overfitting and Regularization

⌨️ (1:23:38) Linear Regression Basics - Statistical Version

⌨️ (1:36:56) Linear Regression Model Theory

⌨️ (2:00:20) Logistic Regression Model Theory

⌨️ (2:15:37) Case Study with Linear Regression

⌨️ (2:33:44) Loading and Exploring Data

⌨️ (2:39:54) Defining Independent and Dependent Variables

⌨️ (2:45:59) Data Cleaning and Preprocessing 

⌨️ (2:54:39) Descriptive Statistics and Data Visualization

⌨️ (3:03:39) InterQuantileRange for Outlier Detection

⌨️ (3:14:00) Correlation Analysis 

⌨️ (3:32:14) Splitting Data into Train/Test with sklearn

⌨️ (3:34:31) Running Linear Regression - Causal Analysis

⌨️ (4:01:24) Checking OLS Assumptions of Linear Regression Model

⌨️ (4:10:10) Running Linear Regression for Predictive Analytics

⌨️ (4:15:54) Closing: Next Steps and Resources