Introduction

Machine Learning Roadmap for 2024

Must Have Skill Set for Career in Machine Learning

Machine Learning Common Career Paths

Machine Learning Basics

Bias-Variance Trade-Off

Overfitting and Regularization

Linear Regression Basics - Statistical Version

Linear Regression Model Theory

Logistic Regression Model Theory

Case Study with Linear Regression

Loading and Exploring Data

Defining Independent and Dependent Variables

Data Cleaning and Preprocessing

Descriptive Statistics and Data Visualization

InterQuantileRange for Outlier Detection

Correlation Analysis

Splitting Data into Train/Test with sklearn

Running Linear Regression - Causal Analysis

Checking OLS Assumptions of Linear Regression Model

Running Linear Regression for Predictive Analytics

Closing: Next Steps and Resources

freeCodeCamp.org

This machine learning course is created for beginners who are learning in 2024. The course begins with a Machine Learning Roadmap for 2024, emphasizing career paths and beginner-friendly theory. Then it the course moves on to hands-on practical applications and a comprehensive end-to-end project using Python.

✏️ 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


🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan 
👾 Oscar Rahnama

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Machine Learning in 2024 – Beginner's Course

Machine Learning in 2024 – Beginner's Course

Machine Learning in 2024 – Beginner's Course

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