Machine Learning in 2024 – Beginner's Course

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 ❤️ Try interactive AI courses we love, right in your browser: https://scrimba.com/freeCodeCamp-AI (Made possible by a grant from our friends at Scrimba) 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 -- Learn to code for free and get a d